system

Abstract

The system according to this embodiment aims to automate and efficiently carry out data center rack placement and wiring planning. [Solution] The system according to the embodiment comprises an input unit, a calculation unit, and a procedure manual creation unit. The input unit inputs facility drawings and equipment information. The calculation unit automatically calculates the optimal rack layout and wiring, taking into account the power supply system, thermal management, and load balancing, based on the information input by the input unit. The procedure manual creation unit creates an operation manual based on the data generated by the calculation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that the rack arrangement and wiring plan of the data center are complicated, require time and labor, and are prone to human errors.

[0005] The system according to the embodiment aims to automate and efficiently perform the rack arrangement and wiring plan of the data center.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a calculation unit, and a procedure manual creation unit. The input unit inputs facility drawings and equipment information. The calculation unit automatically calculates the optimal rack layout and wiring based on the information input by the input unit, taking into account the power supply system, thermal management, and load balancing. The procedure manual creation unit creates an operation manual based on the data generated by the calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and efficiently perform data center rack placement and cabling planning. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The data center support AI agent according to an embodiment of the present invention is a system that automates the physical rack placement and equipment wiring planning in the construction of a data center. The data center support AI agent takes in facility drawings and equipment information, and the AI ​​automatically calculates the optimal rack placement and wiring considering the power system, thermal management, and load balancing. Based on the generated data, it creates an operation manual, allowing workers to proceed with placement based on specific instructions. This system is particularly scalable and is designed to easily accommodate data centers of different sizes. For example, the data center support AI agent inputs facility drawings and equipment information into the AI. Next, the AI ​​automatically calculates the optimal rack placement and wiring considering the power system, thermal management, and load balancing. For example, the AI ​​analyzes the power consumption and heat generation of each piece of equipment to determine the optimal placement. Furthermore, the AI ​​optimizes the length and route of wiring to prevent wiring errors. Finally, it creates an operation manual based on the generated data, allowing workers to proceed with placement based on specific instructions. This system provides solutions to industry-specific challenges for data center operators, IT infrastructure managers, and system engineering companies. The AI ​​can optimize the power system, improving construction accuracy and operational efficiency. By utilizing generative AI, equipment information and environmental data can be analyzed to optimize placement and automatically formulate wiring plans. This enables a precise process and smooth operation. The data center market is currently worth hundreds of billions of yen annually and is expanding rapidly. With the spread of cloud computing and the increasing complexity of data center operations, efficiency and immediate response are increasingly demanded. Companies are seeking faster and more efficient ways to handle large amounts of data and achieve their business objectives. This invention aims to provide efficient data center operations and high-quality services. As a result, the data center support AI agent can streamline data center construction and create operational manuals, allowing workers to proceed with placement based on specific instructions.

[0029] The data center support AI agent according to this embodiment comprises an input unit, a calculation unit, and a procedure manual creation unit. The input unit inputs facility drawings and equipment information. For example, the input unit scans facility drawings and saves them as digital data. The input unit can also obtain equipment information from a database. For example, the input unit scans facility drawings with a high-resolution scanner and converts them into digital data. Equipment information can be obtained from a database stored in a specific format. Based on the information input by the input unit, the calculation unit automatically calculates the optimal rack layout and wiring, taking into account power supply systems, thermal management, and load balancing. For example, the calculation unit analyzes the power consumption and heat generation of each piece of equipment to determine the optimal layout. The calculation unit can also optimize wiring length and route to prevent wiring errors. For example, the calculation unit calculates the optimal rack layout based on the power consumption of each piece of equipment. The calculation unit analyzes heat generation to determine the layout that maximizes cooling efficiency. The calculation unit calculates a route that minimizes wiring length and prevents wiring errors. The procedure manual creation unit creates operational procedure manuals based on data generated by the calculation unit. For example, the procedure manual creation unit creates procedure manuals that describe specific work procedures based on the generated data. The procedure manual creation unit can also provide procedure manuals in a format that is easy for workers to understand. For example, the procedure manual creation unit creates procedure manuals that describe work procedures step by step based on the generated data. The procedure manual creation unit also provides procedure manuals that visually explain work procedures using diagrams and illustrations. As a result, the data center support AI agent according to this embodiment streamlines data center construction by taking in facility drawings and equipment information, automatically calculating the optimal rack layout and wiring, and creating operational procedure manuals.

[0030] The data acquisition unit acquires facility drawings and equipment information. For example, the unit scans facility drawings and saves them as digital data. The unit can also retrieve equipment information from a database. Specifically, when scanning facility drawings with a high-resolution scanner and converting them to digital data, the scanner's resolution and scanning speed can be adjusted to accurately capture even the finest details of the drawings. The acquired digital data is then processed using image processing software to remove noise and correct imperfections, and saved as clear drawing data. When acquiring equipment information, the unit retrieves information stored in a specific format from the database. For example, it extracts necessary information from a database containing information such as equipment model numbers, power consumption, heat generation, and installation locations, and manages it centrally. The unit integrates this information to provide foundational data for understanding the overall picture of the data center. Furthermore, the unit regularly updates the database to reflect new equipment information and changes in drawings, ensuring that it always maintains the latest information. This allows the unit to accurately and efficiently acquire the information necessary for the design and operation of the data center.

[0031] The calculation unit automatically calculates the optimal rack layout and wiring based on the information acquired by the data acquisition unit, taking into account power supply systems, thermal management, and load balancing. For example, the calculation unit analyzes the power consumption and heat generation of each piece of equipment to determine the optimal placement. Specifically, based on the power consumption data of each piece of equipment, the calculation unit calculates a layout that evenly distributes the load on the power supply system. This prevents excessive load on specific power supply systems and ensures a stable power supply. The calculation unit also analyzes the heat generation of each piece of equipment to determine a layout that maximizes cooling efficiency. For example, it places equipment that generates a lot of heat near cooling devices to improve cooling efficiency. Furthermore, the calculation unit can optimize the length and route of wiring to prevent wiring errors. Specifically, minimizing the length of wiring reduces signal delay and loss. Also, optimizing the wiring route prevents wiring congestion and improves ease of maintenance. The calculation unit comprehensively considers these factors and automatically calculates the optimal rack layout and wiring. In addition, the calculation unit has a simulation function that can simulate the operation of a virtual data center based on the calculation results. This allows for verification of the validity of calculation results and adjustments to placement and wiring as needed. In this way, the computing unit supports the efficient design and operation of data centers.

[0032] The Procedure Manual Creation Department creates operational procedures based on data generated by the Calculation Department. For example, the Procedure Manual Creation Department creates procedures that describe specific work steps based on the generated data. Specifically, the Procedure Manual Creation Department describes each work step in detail based on data on optimal rack placement and wiring routes provided by the Calculation Department. For example, it describes rack installation procedures, equipment placement methods, and wiring connection procedures step by step so that workers can easily understand them. The Procedure Manual Creation Department also provides procedures that visually explain work procedures using diagrams and illustrations. For example, it inserts diagrams showing rack installation locations and wiring routes so that workers can understand them intuitively. Furthermore, the Procedure Manual Creation Department provides work procedures in digital format so that workers can view them on devices such as tablets and smartphones. This allows workers to proceed with their work while checking the procedures on-site, improving work efficiency. In addition, the Procedure Manual Creation Department can easily update and revise work procedures. For example, if new equipment is introduced or the layout is changed, the Procedure Manual Creation Department can quickly update the procedures to provide the latest information. This allows the procedure manual creation department to efficiently create the procedure manuals necessary for data center operations, thereby reducing the burden on workers.

[0033] The calculation unit can analyze the power consumption and heat generation of each device and determine the optimal placement. For example, the calculation unit measures the power consumption of each device and calculates the optimal rack placement. The calculation unit can also analyze heat generation and determine the placement that maximizes cooling efficiency. The calculation unit calculates the optimal placement based on power consumption and heat generation data. In this way, by analyzing the power consumption and heat generation of each device, the optimal placement is determined, enabling the construction of an efficient data center. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input power consumption and heat generation data for each device into a generating AI and have the generating AI perform the calculation of the optimal placement.

[0034] The calculation unit can optimize wiring length and route to prevent wiring errors. For example, the calculation unit can minimize wiring length and calculate the optimal route. The calculation unit can also optimize wiring routes to prevent wiring errors. Based on wiring length and route data, the calculation unit formulates an optimal wiring plan. This prevents wiring errors by optimizing wiring length and route, enabling the construction of an efficient data center. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input wiring length and route data into a generating AI and have the generating AI formulate an optimal wiring plan.

[0035] The procedure manual creation unit creates operational procedures based on the generated data, allowing workers to proceed with deployment based on specific instructions. For example, the procedure manual creation unit creates procedures that describe specific work steps based on the generated data. The procedure manual creation unit can also provide procedures in a format that is easy for workers to understand. The procedure manual creation unit creates procedures that describe work steps step by step based on the generated data. The procedure manual creation unit provides procedures that visually explain work steps using diagrams and illustrations. As a result, by creating operational procedures based on the generated data, workers can proceed with deployment based on specific instructions, thereby realizing the construction of an efficient data center. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or not. For example, the procedure manual creation unit can input the generated data into a generation AI and have the generation AI create the operational procedures.

[0036] The calculation unit can optimize the power supply system, thereby improving construction accuracy and operational efficiency. For example, the calculation unit calculates the optimal power supply placement considering the stability of the power supply system. The calculation unit can also determine the placement that maximizes the efficiency of the power supply system. The calculation unit calculates the optimal placement based on power supply system data. By optimizing the power supply system in this way, it improves construction accuracy and operational efficiency. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input power supply system data into a generating AI and have the generating AI perform the calculation of the optimal power supply placement.

[0037] The calculation unit can analyze equipment information and environmental data to optimize placement and automatically formulate a wiring plan. For example, the calculation unit can analyze equipment information and calculate the optimal rack placement. The calculation unit can also analyze environmental data and determine the placement that maximizes cooling efficiency. The calculation unit calculates the optimal placement based on the equipment information and environmental data. This enables the construction of an efficient data center by analyzing equipment information and environmental data to optimize placement and automatically formulating a wiring plan. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input equipment information and environmental data into a generating AI and have the generating AI perform the calculation of the optimal placement.

[0038] The data acquisition unit can automatically verify the accuracy of facility drawings and equipment information during data acquisition and correct errors. For example, during data acquisition, the AI ​​can check the consistency of facility drawings and equipment information, detect and correct errors. The data acquisition unit can also use AI to detect and correct abnormal data by comparing it with past data during data acquisition. The data acquisition unit can also use AI to verify the accuracy of the data in real time during data acquisition and correct errors. This enables accurate data acquisition by automatically verifying the accuracy of the information and correcting errors during data acquisition. Some or all of the above processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input facility drawings and equipment information data into a generating AI and have the generating AI perform accuracy verification and error correction.

[0039] The import unit can maintain up-to-date information by performing version control of facility drawings and equipment information during import. For example, the import unit can use AI to manage the versions of facility drawings and equipment information during import and maintain up-to-date information. The import unit can also use AI to record changes by comparing with past versions during import and maintain up-to-date information. The import unit can also use AI to maintain up-to-date information during import. This ensures accurate information management by performing version control during import and maintaining up-to-date information at all times. Some or all of the above processes in the import unit may be performed using AI, for example, or without AI. For example, the import unit can input version data of facility drawings and equipment information into a generating AI and have the generating AI perform version control.

[0040] The data acquisition unit can analyze the relationship between facility drawings and equipment information during data acquisition and prioritize the acquisition of highly important information. For example, during data acquisition, the AI ​​analyzes the relationship between facility drawings and equipment information and prioritizes the acquisition of highly important information. The data acquisition unit can also achieve efficient data management by having the AI ​​prioritize the acquisition of highly relevant information during data acquisition. The data acquisition unit can also maintain data integrity by having the AI ​​prioritize the acquisition of highly important information during data acquisition. This enables efficient information management by analyzing the relationships between information during acquisition and prioritizing the acquisition of highly important information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input facility drawings and equipment information data into a generating AI and have the generating AI perform relationship analysis and acquire highly important information.

[0041] The data acquisition unit can optimize the method of acquiring facility drawings and equipment information based on the user's operation history during data acquisition. For example, during data acquisition, the AI ​​can analyze the user's operation history and propose the optimal acquisition method. The data acquisition unit can also optimize the acquisition method based on the user's past operation history during data acquisition. The data acquisition unit can also automatically adjust the acquisition method based on the user's operation history during data acquisition. This enables efficient information acquisition by optimizing the acquisition method based on the user's operation history during data acquisition. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input user operation history data into a generating AI and have the generating AI perform the optimization of the acquisition method.

[0042] The calculation unit can monitor fluctuations in power consumption and heat generation of each device in real time during calculations and dynamically recalculate the optimal placement. For example, during calculations, the calculation unit can use AI to monitor fluctuations in power consumption and heat generation of each device in real time and recalculate the optimal placement. The calculation unit can also use AI to acquire data in real time during calculations and dynamically recalculate the optimal placement. The calculation unit can also use AI to consider fluctuations in power consumption and heat generation during calculations and recalculate the optimal placement. This enables the construction of an efficient data center by monitoring fluctuations in power consumption and heat generation of each device in real time during calculations and dynamically recalculating the optimal placement. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input power consumption and heat generation data of each device into a generating AI and have the generating AI perform the recalculation of the optimal placement.

[0043] The calculation unit can perform simulations of rack placement and wiring during calculations and verify the optimal placement in advance. For example, the calculation unit can use AI to simulate rack placement and wiring during calculations and verify the optimal placement in advance. The calculation unit can also use AI to use simulations to verify the optimal placement during calculations. The calculation unit can also use AI to perform simulations during calculations and confirm the optimal placement in advance. This enables the construction of an efficient data center by simulating rack placement and wiring during calculations and verifying the optimal placement in advance. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input simulation data of rack placement and wiring into a generating AI and have the generating AI perform verification of the optimal placement.

[0044] The calculation unit can determine the optimal rack layout and wiring during calculations, taking into account the geographical and climatic conditions of the facility. For example, the calculation unit can use AI to determine the optimal rack layout and wiring during calculations, taking into account the geographical conditions of the facility. The calculation unit can also use AI to determine the optimal rack layout and wiring during calculations, taking into account climatic conditions. The calculation unit can also use AI to determine the optimal rack layout and wiring during calculations, taking into account both geographical and climatic conditions comprehensively. This enables the construction of an efficient data center by determining the optimal rack layout and wiring during calculations, taking into account the geographical and climatic conditions of the facility. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the geographical and climatic conditions of the facility into a generating AI and have the generating AI perform the determination of the optimal rack layout and wiring.

[0045] The calculation unit can refer to the location data of other data centers during calculations and compare and consider the optimal location. For example, during calculations, the calculation unit can have the AI ​​refer to the location data of other data centers and compare and consider the optimal location. The calculation unit can also have the AI ​​propose the optimal location based on the data of other data centers during calculations. The calculation unit can also have the AI ​​analyze the location data of other data centers and determine the optimal location during calculations. This allows for the comparison and consideration of the optimal location by referring to the location data of other data centers. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the location data of other data centers into a generating AI and have the generating AI perform a comparison and consideration of the optimal location.

[0046] The procedure manual creation unit can adjust the level of detail in the procedure manual according to the worker's skill level when creating it. For example, the procedure manual creation unit can use AI to analyze the worker's skill level and adjust the level of detail when creating the procedure manual. The procedure manual creation unit can also use AI to provide a detailed procedure manual according to the worker's skill level when creating it. The procedure manual creation unit can also use AI to create an optimal procedure manual considering the worker's skill level when creating it. This enables efficient work instructions by adjusting the level of detail in the procedure manual according to the worker's skill level. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without AI. For example, the procedure manual creation unit can input worker skill level data into a generating AI and have the generating AI perform the adjustment of the level of detail in the procedure manual.

[0047] The procedure manual creation unit can improve the accuracy of procedure manuals by incorporating feedback from past operational procedures during the creation process. For example, the procedure manual creation unit can use AI to analyze past feedback during procedure manual creation to improve the accuracy of the procedure manual. The procedure manual creation unit can also use AI to incorporate feedback from past procedure manuals during procedure manual creation to create the optimal procedure manual. The procedure manual creation unit can also use AI to improve the accuracy of procedure manuals based on feedback during procedure manual creation. This allows for improved accuracy of procedure manuals by incorporating feedback from past operational procedures. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without AI. For example, the procedure manual creation unit can input past feedback data into a generating AI and have the generating AI perform the procedure manual accuracy improvement.

[0048] The procedure manual creation unit can customize the optimal procedure manual by referring to the worker's work history when creating it. For example, when creating a procedure manual, the procedure manual creation unit can use AI to analyze the worker's work history and provide the optimal procedure manual. The procedure manual creation unit can also use AI to customize the procedure manual based on the worker's past work history when creating it. The procedure manual creation unit can also use AI to create the optimal procedure manual by referring to the work history when creating it. In this way, the optimal procedure manual can be customized by referring to the worker's work history. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without using AI. For example, the procedure manual creation unit can input the worker's work history data into a generating AI and have the generating AI perform the customization of the procedure manual.

[0049] The procedure manual creation unit can create the optimal procedure manual by referring to the operational procedure manuals of other data centers when creating a procedure manual. For example, the procedure manual creation unit can use AI to refer to the operational procedure manuals of other data centers when creating a procedure manual and create the optimal one. The procedure manual creation unit can also use AI to analyze the operational procedure manuals of other data centers and provide the optimal one. The procedure manual creation unit can also use AI to customize a procedure manual based on the operational procedure manuals of other data centers when creating a procedure manual. This allows the creation of the optimal procedure manual by referring to the operational procedure manuals of other data centers. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without AI. For example, the procedure manual creation unit can input operational procedure manual data from other data centers into a generating AI and have the generating AI create the optimal procedure manual.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data center support AI agent can also be equipped with a security monitoring unit. This unit analyzes data from surveillance cameras and sensors within the facility in real time to detect anomalies. For example, it can detect suspicious activity within the facility and issue an alarm. It can also detect unauthorized access to equipment and immediately notify administrators. Furthermore, it can detect abnormalities in temperature and humidity within the facility and take appropriate countermeasures. This enhances data center security and improves operational safety.

[0052] The data center support AI agent can also include an energy management unit. This unit monitors and optimizes energy consumption within the facility in real time. For example, it can monitor the power consumption of each piece of equipment and make adjustments to maximize energy efficiency. It can also adjust equipment operating schedules to reduce peak energy consumption. Furthermore, the energy management unit can promote the use of renewable energy and reduce environmental impact. This improves the energy efficiency of the data center and reduces operating costs.

[0053] The data center support AI agent can also be equipped with a predictive analytics unit. This unit predicts future trends based on historical data and proposes optimal operational plans. For example, it can analyze past equipment failure data to identify equipment at high risk of failure. It can also predict energy consumption trends and propose measures to reduce peak energy consumption. Furthermore, it can predict data center utilization and propose optimal resource allocation. This improves data center operational efficiency and reduces costs.

[0054] The data center support AI agent can also be equipped with an environmental monitoring unit. This unit monitors environmental data such as temperature, humidity, and air quality within the facility in real time to maintain optimal environmental conditions. For example, the environmental monitoring unit can monitor the temperature within the facility using a temperature sensor and adjust the cooling system accordingly. It can also monitor the humidity within the facility using a humidity sensor and adjust humidifiers and dehumidifiers. Furthermore, it can monitor the air quality within the facility using an air quality sensor and adjust the ventilation system. This optimizes the environmental conditions of the data center and ensures stable operation of the equipment.

[0055] The data center support AI agent can also be equipped with a resource management unit. This unit monitors facility resources (power, cooling, network bandwidth, etc.) in real time and optimizes resource allocation. For example, it can monitor the power consumption of each piece of equipment and reallocate resources to prevent power overload. It can also monitor cooling resources and reallocate them to maximize cooling efficiency. Furthermore, it can monitor network bandwidth and reallocate network resources to prevent bandwidth overload. This allows for optimal management of data center resources and improved operational efficiency.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data acquisition unit captures facility drawings and equipment information. For example, it scans facility drawings with a high-resolution scanner and converts them into digital data. The data acquisition unit can also retrieve equipment information from a database. Step 2: The calculation unit automatically calculates the optimal rack layout and wiring based on the information acquired by the data acquisition unit, taking into account the power supply system, thermal management, and load balancing. For example, it analyzes the power consumption and heat generation of each piece of equipment to determine the optimal placement. It also optimizes the length and route of wiring to prevent wiring errors. Step 3: The procedure manual creation unit creates operational procedure manuals based on the data generated by the calculation unit. For example, it creates procedure manuals that describe specific work procedures based on the generated data. It also provides the procedure manuals in a format that is easy for workers to understand.

[0058] (Example of form 2) The data center support AI agent according to an embodiment of the present invention is a system that automates the physical rack placement and equipment wiring planning in the construction of a data center. The data center support AI agent takes in facility drawings and equipment information, and the AI ​​automatically calculates the optimal rack placement and wiring considering the power system, thermal management, and load balancing. Based on the generated data, it creates an operation manual, allowing workers to proceed with placement based on specific instructions. This system is particularly scalable and is designed to easily accommodate data centers of different sizes. For example, the data center support AI agent inputs facility drawings and equipment information into the AI. Next, the AI ​​automatically calculates the optimal rack placement and wiring considering the power system, thermal management, and load balancing. For example, the AI ​​analyzes the power consumption and heat generation of each piece of equipment to determine the optimal placement. Furthermore, the AI ​​optimizes the length and route of wiring to prevent wiring errors. Finally, it creates an operation manual based on the generated data, allowing workers to proceed with placement based on specific instructions. This system provides solutions to industry-specific challenges for data center operators, IT infrastructure managers, and system engineering companies. The AI ​​can optimize the power system, improving construction accuracy and operational efficiency. By utilizing generative AI, equipment information and environmental data can be analyzed to optimize placement and automatically formulate wiring plans. This enables a precise process and smooth operation. The data center market is currently worth hundreds of billions of yen annually and is expanding rapidly. With the spread of cloud computing and the increasing complexity of data center operations, efficiency and immediate response are increasingly demanded. Companies are seeking faster and more efficient ways to handle large amounts of data and achieve their business objectives. This invention aims to provide efficient data center operations and high-quality services. As a result, the data center support AI agent can streamline data center construction and create operational manuals, allowing workers to proceed with placement based on specific instructions.

[0059] The data center support AI agent according to this embodiment comprises an input unit, a calculation unit, and a procedure manual creation unit. The input unit inputs facility drawings and equipment information. For example, the input unit scans facility drawings and saves them as digital data. The input unit can also obtain equipment information from a database. For example, the input unit scans facility drawings with a high-resolution scanner and converts them into digital data. Equipment information can be obtained from a database stored in a specific format. Based on the information input by the input unit, the calculation unit automatically calculates the optimal rack layout and wiring, taking into account power supply systems, thermal management, and load balancing. For example, the calculation unit analyzes the power consumption and heat generation of each piece of equipment to determine the optimal layout. The calculation unit can also optimize wiring length and route to prevent wiring errors. For example, the calculation unit calculates the optimal rack layout based on the power consumption of each piece of equipment. The calculation unit analyzes heat generation to determine the layout that maximizes cooling efficiency. The calculation unit calculates a route that minimizes wiring length and prevents wiring errors. The procedure manual creation unit creates operational procedure manuals based on data generated by the calculation unit. For example, the procedure manual creation unit creates procedure manuals that describe specific work procedures based on the generated data. The procedure manual creation unit can also provide procedure manuals in a format that is easy for workers to understand. For example, the procedure manual creation unit creates procedure manuals that describe work procedures step by step based on the generated data. The procedure manual creation unit also provides procedure manuals that visually explain work procedures using diagrams and illustrations. As a result, the data center support AI agent according to this embodiment streamlines data center construction by taking in facility drawings and equipment information, automatically calculating the optimal rack layout and wiring, and creating operational procedure manuals.

[0060] The data acquisition unit acquires facility drawings and equipment information. For example, the unit scans facility drawings and saves them as digital data. The unit can also retrieve equipment information from a database. Specifically, when scanning facility drawings with a high-resolution scanner and converting them to digital data, the scanner's resolution and scanning speed can be adjusted to accurately capture even the finest details of the drawings. The acquired digital data is then processed using image processing software to remove noise and correct imperfections, and saved as clear drawing data. When acquiring equipment information, the unit retrieves information stored in a specific format from the database. For example, it extracts necessary information from a database containing information such as equipment model numbers, power consumption, heat generation, and installation locations, and manages it centrally. The unit integrates this information to provide foundational data for understanding the overall picture of the data center. Furthermore, the unit regularly updates the database to reflect new equipment information and changes in drawings, ensuring that it always maintains the latest information. This allows the unit to accurately and efficiently acquire the information necessary for the design and operation of the data center.

[0061] The calculation unit automatically calculates the optimal rack layout and wiring based on the information acquired by the data acquisition unit, taking into account power supply systems, thermal management, and load balancing. For example, the calculation unit analyzes the power consumption and heat generation of each piece of equipment to determine the optimal placement. Specifically, based on the power consumption data of each piece of equipment, the calculation unit calculates a layout that evenly distributes the load on the power supply system. This prevents excessive load on specific power supply systems and ensures a stable power supply. The calculation unit also analyzes the heat generation of each piece of equipment to determine a layout that maximizes cooling efficiency. For example, it places equipment that generates a lot of heat near cooling devices to improve cooling efficiency. Furthermore, the calculation unit can optimize the length and route of wiring to prevent wiring errors. Specifically, minimizing the length of wiring reduces signal delay and loss. Also, optimizing the wiring route prevents wiring congestion and improves ease of maintenance. The calculation unit comprehensively considers these factors and automatically calculates the optimal rack layout and wiring. In addition, the calculation unit has a simulation function that can simulate the operation of a virtual data center based on the calculation results. This allows for verification of the validity of calculation results and adjustments to placement and wiring as needed. In this way, the computing unit supports the efficient design and operation of data centers.

[0062] The Procedure Manual Creation Department creates operational procedures based on data generated by the Calculation Department. For example, the Procedure Manual Creation Department creates procedures that describe specific work steps based on the generated data. Specifically, the Procedure Manual Creation Department describes each work step in detail based on data on optimal rack placement and wiring routes provided by the Calculation Department. For example, it describes rack installation procedures, equipment placement methods, and wiring connection procedures step by step so that workers can easily understand them. The Procedure Manual Creation Department also provides procedures that visually explain work procedures using diagrams and illustrations. For example, it inserts diagrams showing rack installation locations and wiring routes so that workers can understand them intuitively. Furthermore, the Procedure Manual Creation Department provides work procedures in digital format so that workers can view them on devices such as tablets and smartphones. This allows workers to proceed with their work while checking the procedures on-site, improving work efficiency. In addition, the Procedure Manual Creation Department can easily update and revise work procedures. For example, if new equipment is introduced or the layout is changed, the Procedure Manual Creation Department can quickly update the procedures to provide the latest information. This allows the procedure manual creation department to efficiently create the procedure manuals necessary for data center operations, thereby reducing the burden on workers.

[0063] The calculation unit can analyze the power consumption and heat generation of each device and determine the optimal placement. For example, the calculation unit measures the power consumption of each device and calculates the optimal rack placement. The calculation unit can also analyze heat generation and determine the placement that maximizes cooling efficiency. The calculation unit calculates the optimal placement based on power consumption and heat generation data. In this way, by analyzing the power consumption and heat generation of each device, the optimal placement is determined, enabling the construction of an efficient data center. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input power consumption and heat generation data for each device into a generating AI and have the generating AI perform the calculation of the optimal placement.

[0064] The calculation unit can optimize wiring length and route to prevent wiring errors. For example, the calculation unit can minimize wiring length and calculate the optimal route. The calculation unit can also optimize wiring routes to prevent wiring errors. Based on wiring length and route data, the calculation unit formulates an optimal wiring plan. This prevents wiring errors by optimizing wiring length and route, enabling the construction of an efficient data center. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input wiring length and route data into a generating AI and have the generating AI formulate an optimal wiring plan.

[0065] The procedure manual creation unit creates operational procedures based on the generated data, allowing workers to proceed with deployment based on specific instructions. For example, the procedure manual creation unit creates procedures that describe specific work steps based on the generated data. The procedure manual creation unit can also provide procedures in a format that is easy for workers to understand. The procedure manual creation unit creates procedures that describe work steps step by step based on the generated data. The procedure manual creation unit provides procedures that visually explain work steps using diagrams and illustrations. As a result, by creating operational procedures based on the generated data, workers can proceed with deployment based on specific instructions, thereby realizing the construction of an efficient data center. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or not. For example, the procedure manual creation unit can input the generated data into a generation AI and have the generation AI create the operational procedures.

[0066] The calculation unit can optimize the power supply system, thereby improving construction accuracy and operational efficiency. For example, the calculation unit calculates the optimal power supply placement considering the stability of the power supply system. The calculation unit can also determine the placement that maximizes the efficiency of the power supply system. The calculation unit calculates the optimal placement based on power supply system data. By optimizing the power supply system in this way, it improves construction accuracy and operational efficiency. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input power supply system data into a generating AI and have the generating AI perform the calculation of the optimal power supply placement.

[0067] The calculation unit can analyze equipment information and environmental data to optimize placement and automatically formulate a wiring plan. For example, the calculation unit can analyze equipment information and calculate the optimal rack placement. The calculation unit can also analyze environmental data and determine the placement that maximizes cooling efficiency. The calculation unit calculates the optimal placement based on the equipment information and environmental data. This enables the construction of an efficient data center by analyzing equipment information and environmental data to optimize placement and automatically formulating a wiring plan. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input equipment information and environmental data into a generating AI and have the generating AI perform the calculation of the optimal placement.

[0068] The data acquisition unit can estimate the user's emotions and adjust the timing of acquiring facility drawings and equipment information based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can delay the acquisition timing and wait until the user is relaxed. If the user is relaxed, the data acquisition unit can also speed up the acquisition timing to acquire information efficiently. If the user is in a hurry, the data acquisition unit can also optimize the acquisition timing to acquire information quickly. This ensures efficient information acquisition by adjusting the acquisition timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input user emotion data into the generative AI and have the generative AI adjust the acquisition timing.

[0069] The data acquisition unit can automatically verify the accuracy of facility drawings and equipment information during data acquisition and correct errors. For example, during data acquisition, the AI ​​can check the consistency of facility drawings and equipment information, detect and correct errors. The data acquisition unit can also use AI to detect and correct abnormal data by comparing it with past data during data acquisition. The data acquisition unit can also use AI to verify the accuracy of the data in real time during data acquisition and correct errors. This enables accurate data acquisition by automatically verifying the accuracy of the information and correcting errors during data acquisition. Some or all of the above processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input facility drawings and equipment information data into a generating AI and have the generating AI perform accuracy verification and error correction.

[0070] The import unit can maintain up-to-date information by performing version control of facility drawings and equipment information during import. For example, the import unit can use AI to manage the versions of facility drawings and equipment information during import and maintain up-to-date information. The import unit can also use AI to record changes by comparing with past versions during import and maintain up-to-date information. The import unit can also use AI to maintain up-to-date information during import. This ensures accurate information management by performing version control during import and maintaining up-to-date information at all times. Some or all of the above processes in the import unit may be performed using AI, for example, or without AI. For example, the import unit can input version data of facility drawings and equipment information into a generating AI and have the generating AI perform version control.

[0071] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring high-priority information and postpone less important information. If the user is relaxed, the data acquisition unit can also acquire all information equally. If the user is in a hurry, the data acquisition unit can also prioritize acquiring the most important information. This enables efficient information acquisition by determining the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0072] The data acquisition unit can analyze the relationship between facility drawings and equipment information during data acquisition and prioritize the acquisition of highly important information. For example, during data acquisition, the AI ​​analyzes the relationship between facility drawings and equipment information and prioritizes the acquisition of highly important information. The data acquisition unit can also achieve efficient data management by having the AI ​​prioritize the acquisition of highly relevant information during data acquisition. The data acquisition unit can also maintain data integrity by having the AI ​​prioritize the acquisition of highly important information during data acquisition. This enables efficient information management by analyzing the relationships between information during acquisition and prioritizing the acquisition of highly important information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input facility drawings and equipment information data into a generating AI and have the generating AI perform relationship analysis and acquire highly important information.

[0073] The data acquisition unit can optimize the method of acquiring facility drawings and equipment information based on the user's operation history during data acquisition. For example, during data acquisition, the AI ​​can analyze the user's operation history and propose the optimal acquisition method. The data acquisition unit can also optimize the acquisition method based on the user's past operation history during data acquisition. The data acquisition unit can also automatically adjust the acquisition method based on the user's operation history during data acquisition. This enables efficient information acquisition by optimizing the acquisition method based on the user's operation history during data acquisition. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input user operation history data into a generating AI and have the generating AI perform the optimization of the acquisition method.

[0074] The calculation unit can estimate the user's emotions and adjust the calculation method for optimal rack placement and wiring based on the estimated emotions. For example, if the user is stressed, the calculation unit can simplify the calculation method to reduce the user's burden. If the user is relaxed, the calculation unit can also use a detailed calculation method to suggest the optimal placement. If the user is in a hurry, the calculation unit can perform calculations quickly and suggest the optimal placement. This allows for efficient rack placement and wiring calculations by adjusting the calculation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI adjust the calculation method.

[0075] The calculation unit can monitor fluctuations in power consumption and heat generation of each device in real time during calculations and dynamically recalculate the optimal placement. For example, during calculations, the calculation unit can use AI to monitor fluctuations in power consumption and heat generation of each device in real time and recalculate the optimal placement. The calculation unit can also use AI to acquire data in real time during calculations and dynamically recalculate the optimal placement. The calculation unit can also use AI to consider fluctuations in power consumption and heat generation during calculations and recalculate the optimal placement. This enables the construction of an efficient data center by monitoring fluctuations in power consumption and heat generation of each device in real time during calculations and dynamically recalculating the optimal placement. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input power consumption and heat generation data of each device into a generating AI and have the generating AI perform the recalculation of the optimal placement.

[0076] The calculation unit can perform simulations of rack placement and wiring during calculations and verify the optimal placement in advance. For example, the calculation unit can use AI to simulate rack placement and wiring during calculations and verify the optimal placement in advance. The calculation unit can also use AI to use simulations to verify the optimal placement during calculations. The calculation unit can also use AI to perform simulations during calculations and confirm the optimal placement in advance. This enables the construction of an efficient data center by simulating rack placement and wiring during calculations and verifying the optimal placement in advance. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input simulation data of rack placement and wiring into a generating AI and have the generating AI perform verification of the optimal placement.

[0077] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated user emotions. For example, if the user is stressed, the calculation unit can provide a simple and highly visible display method. If the user is relaxed, the calculation unit can also provide a display method that includes detailed information. If the user is in a hurry, the calculation unit can also provide a display method that gets straight to the point. This enables efficient information delivery by adjusting the display method of the calculation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0078] The calculation unit can determine the optimal rack layout and wiring during calculations, taking into account the geographical and climatic conditions of the facility. For example, the calculation unit can use AI to determine the optimal rack layout and wiring during calculations, taking into account the geographical conditions of the facility. The calculation unit can also use AI to determine the optimal rack layout and wiring during calculations, taking into account climatic conditions. The calculation unit can also use AI to determine the optimal rack layout and wiring during calculations, taking into account both geographical and climatic conditions comprehensively. This enables the construction of an efficient data center by determining the optimal rack layout and wiring during calculations, taking into account the geographical and climatic conditions of the facility. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the geographical and climatic conditions of the facility into a generating AI and have the generating AI perform the determination of the optimal rack layout and wiring.

[0079] The calculation unit can refer to the location data of other data centers during calculations and compare and consider the optimal location. For example, during calculations, the calculation unit can have the AI ​​refer to the location data of other data centers and compare and consider the optimal location. The calculation unit can also have the AI ​​propose the optimal location based on the data of other data centers during calculations. The calculation unit can also have the AI ​​analyze the location data of other data centers and determine the optimal location during calculations. This allows for the comparison and consideration of the optimal location by referring to the location data of other data centers. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the location data of other data centers into a generating AI and have the generating AI perform a comparison and consideration of the optimal location.

[0080] The procedure manual creation unit can estimate the user's emotions and adjust the expression of the operational procedure manual based on the estimated user emotions. For example, if the user is stressed, the procedure manual creation unit can provide a simple and highly visible procedure manual. If the user is relaxed, the procedure manual creation unit can also provide a procedure manual that includes detailed information. If the user is in a hurry, the procedure manual creation unit can also provide a procedure manual that gets straight to the point. This enables efficient information delivery by adjusting the expression of the operational procedure manual based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the procedure manual creation unit may be performed using AI, for example, or not using AI. For example, the procedure manual creation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the expression.

[0081] The procedure manual creation unit can adjust the level of detail in the procedure manual according to the worker's skill level when creating it. For example, the procedure manual creation unit can use AI to analyze the worker's skill level and adjust the level of detail when creating the procedure manual. The procedure manual creation unit can also use AI to provide a detailed procedure manual according to the worker's skill level when creating it. The procedure manual creation unit can also use AI to create an optimal procedure manual considering the worker's skill level when creating it. This enables efficient work instructions by adjusting the level of detail in the procedure manual according to the worker's skill level. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without AI. For example, the procedure manual creation unit can input worker skill level data into a generating AI and have the generating AI perform the adjustment of the level of detail in the procedure manual.

[0082] The procedure manual creation unit can improve the accuracy of procedure manuals by incorporating feedback from past operational procedures during the creation process. For example, the procedure manual creation unit can use AI to analyze past feedback during procedure manual creation to improve the accuracy of the procedure manual. The procedure manual creation unit can also use AI to incorporate feedback from past procedure manuals during procedure manual creation to create the optimal procedure manual. The procedure manual creation unit can also use AI to improve the accuracy of procedure manuals based on feedback during procedure manual creation. This allows for improved accuracy of procedure manuals by incorporating feedback from past operational procedures. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without AI. For example, the procedure manual creation unit can input past feedback data into a generating AI and have the generating AI perform the procedure manual accuracy improvement.

[0083] The procedure creation unit can estimate the user's emotions and determine the priority of procedure manuals based on the estimated emotions. For example, if the user is stressed, the procedure creation unit will prioritize providing high-priority procedure manuals. If the user is relaxed, the procedure creation unit can also provide all procedure manuals equally. If the user is in a hurry, the procedure creation unit can also prioritize providing the most important procedure manuals. This enables efficient work instructions by prioritizing procedure manuals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure creation unit may be performed using AI, for example, or not using AI. For example, the procedure creation unit can input user emotion data into a generative AI and have the generative AI determine the priority of procedure manuals.

[0084] The procedure manual creation unit can customize the optimal procedure manual by referring to the worker's work history when creating it. For example, when creating a procedure manual, the procedure manual creation unit can use AI to analyze the worker's work history and provide the optimal procedure manual. The procedure manual creation unit can also use AI to customize the procedure manual based on the worker's past work history when creating it. The procedure manual creation unit can also use AI to create the optimal procedure manual by referring to the work history when creating it. In this way, the optimal procedure manual can be customized by referring to the worker's work history. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without using AI. For example, the procedure manual creation unit can input the worker's work history data into a generating AI and have the generating AI perform the customization of the procedure manual.

[0085] The procedure manual creation unit can create the optimal procedure manual by referring to the operational procedure manuals of other data centers when creating a procedure manual. For example, the procedure manual creation unit can use AI to refer to the operational procedure manuals of other data centers when creating a procedure manual and create the optimal one. The procedure manual creation unit can also use AI to analyze the operational procedure manuals of other data centers and provide the optimal one. The procedure manual creation unit can also use AI to customize a procedure manual based on the operational procedure manuals of other data centers when creating a procedure manual. This allows the creation of the optimal procedure manual by referring to the operational procedure manuals of other data centers. Some or all of the above processes in the procedure manual creation unit may be performed using AI, for example, or without AI. For example, the procedure manual creation unit can input operational procedure manual data from other data centers into a generating AI and have the generating AI create the optimal procedure manual.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data center support AI agent can also be equipped with a security monitoring unit. This unit analyzes data from surveillance cameras and sensors within the facility in real time to detect anomalies. For example, it can detect suspicious activity within the facility and issue an alarm. It can also detect unauthorized access to equipment and immediately notify administrators. Furthermore, it can detect abnormalities in temperature and humidity within the facility and take appropriate countermeasures. This enhances data center security and improves operational safety.

[0088] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of calculation results based on the user's emotions, efficient information delivery is achieved.

[0089] The data center support AI agent can also include an energy management unit. This unit monitors and optimizes energy consumption within the facility in real time. For example, it can monitor the power consumption of each piece of equipment and make adjustments to maximize energy efficiency. It can also adjust equipment operating schedules to reduce peak energy consumption. Furthermore, the energy management unit can promote the use of renewable energy and reduce environmental impact. This improves the energy efficiency of the data center and reduces operating costs.

[0090] The data acquisition unit can estimate the user's emotions and adjust the timing of acquiring facility drawings and equipment information based on those emotions. For example, if the user is stressed, the acquisition timing can be delayed until the user is relaxed. If the user is relaxed, the acquisition timing can be sped up to acquire information efficiently. If the user is in a hurry, the acquisition timing can be optimized to acquire information quickly. In this way, efficient information acquisition is achieved by adjusting the acquisition timing based on the user's emotions.

[0091] The data center support AI agent can also be equipped with a predictive analytics unit. This unit predicts future trends based on historical data and proposes optimal operational plans. For example, it can analyze past equipment failure data to identify equipment at high risk of failure. It can also predict energy consumption trends and propose measures to reduce peak energy consumption. Furthermore, it can predict data center utilization and propose optimal resource allocation. This improves data center operational efficiency and reduces costs.

[0092] The calculation unit can estimate the user's emotions and adjust the optimal rack placement and wiring calculation method based on those emotions. For example, if the user is stressed, the calculation method can be simplified to reduce the user's burden. If the user is relaxed, a more detailed calculation method can be used to suggest the optimal placement. If the user is in a hurry, the calculation can be performed quickly and the optimal placement can be suggested. In this way, by adjusting the calculation method based on the user's emotions, efficient rack placement and wiring calculations are achieved.

[0093] The data center support AI agent can also be equipped with an environmental monitoring unit. This unit monitors environmental data such as temperature, humidity, and air quality within the facility in real time to maintain optimal environmental conditions. For example, the environmental monitoring unit can monitor the temperature within the facility using a temperature sensor and adjust the cooling system accordingly. It can also monitor the humidity within the facility using a humidity sensor and adjust humidifiers and dehumidifiers. Furthermore, it can monitor the air quality within the facility using an air quality sensor and adjust the ventilation system. This optimizes the environmental conditions of the data center and ensures stable operation of the equipment.

[0094] The procedure manual creation department can estimate the user's emotions and adjust the way the operational procedures are written based on those emotions. For example, if the user is stressed, a simple and highly visual procedure manual can be provided. If the user is relaxed, a procedure manual containing detailed information can be provided. If the user is in a hurry, a procedure manual that gets straight to the point can be provided. In this way, by adjusting the way the operational procedures are written based on the user's emotions, efficient information delivery is achieved.

[0095] The data center support AI agent can also be equipped with a resource management unit. This unit monitors facility resources (power, cooling, network bandwidth, etc.) in real time and optimizes resource allocation. For example, it can monitor the power consumption of each piece of equipment and reallocate resources to prevent power overload. It can also monitor cooling resources and reallocate them to maximize cooling efficiency. Furthermore, it can monitor network bandwidth and reallocate network resources to prevent bandwidth overload. This allows for optimal management of data center resources and improved operational efficiency.

[0096] The procedure creation unit can estimate the user's emotions and determine the priority of procedure manuals based on those emotions. For example, if the user is stressed, it can prioritize providing high-priority procedure manuals. If the user is relaxed, it can provide all procedure manuals equally. If the user is in a hurry, it can prioritize providing the most important procedure manuals. This allows for efficient work instructions by prioritizing procedure manuals based on the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data acquisition unit captures facility drawings and equipment information. For example, it scans facility drawings with a high-resolution scanner and converts them into digital data. The data acquisition unit can also retrieve equipment information from a database. Step 2: The calculation unit automatically calculates the optimal rack layout and wiring based on the information acquired by the data acquisition unit, taking into account the power supply system, thermal management, and load balancing. For example, it analyzes the power consumption and heat generation of each piece of equipment to determine the optimal placement. It also optimizes the length and route of wiring to prevent wiring errors. Step 3: The procedure manual creation unit creates operational procedure manuals based on the data generated by the calculation unit. For example, it creates procedure manuals that describe specific work procedures based on the generated data. It also provides the procedure manuals in a format that is easy for workers to understand.

[0099] 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.

[0100] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data acquisition unit, calculation unit, and procedure manual creation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data acquisition unit is implemented by the function of the smart device 14 to acquire information from a scanner or database. The calculation unit is implemented by the specific processing unit 290 of the data processing device 12, which automatically calculates the optimal rack arrangement and wiring considering the power supply system, thermal management, and load balancing. The procedure manual creation unit is implemented by the specific processing unit 290 of the data processing device 12, which creates an operation manual based on the generated data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0106] 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.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0108] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data acquisition unit, calculation unit, and procedure manual creation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data acquisition unit is implemented by the function of acquiring information from the camera and database of the smart glasses 214. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically calculates the optimal rack arrangement and wiring considering the power supply system, thermal management, and load balancing. The procedure manual creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which creates an operation manual based on the generated data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0124] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] 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.

[0126] 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.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data acquisition unit, calculation unit, and procedure manual creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data acquisition unit is implemented by the function of acquiring information from the camera and database of the headset terminal 314. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically calculates the optimal rack arrangement and wiring considering the power supply system, thermal management, and load balancing. The procedure manual creation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates an operation manual based on the generated data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] 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.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data acquisition unit, calculation unit, and procedure manual creation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data acquisition unit is implemented by the robot 414's camera and database acquisition function. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically calculates the optimal rack arrangement and wiring considering the power supply system, thermal management, and load balancing. The procedure manual creation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates an operation manual based on the generated data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] Figure 9 shows the 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.

[0154] 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.

[0155] 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.

[0156] 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, and motorcycles, 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 based, for example, 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.

[0157] 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."

[0158] 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.

[0159] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] 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 other things 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.

[0169] 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.

[0170] (Note 1) An import unit that takes in facility drawings and equipment information, Based on the information acquired by the aforementioned input unit, a calculation unit automatically calculates the optimal rack layout and wiring, taking into account the power supply system, thermal management, and load balancing. The system includes a procedure manual creation unit that creates an operation manual based on the data generated by the calculation unit. A system characterized by the following features. (Note 2) The calculation unit, We analyze the power consumption and heat generation of each device to determine the optimal placement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The calculation unit, Optimize wiring length and routing to prevent wiring errors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned procedure manual creation unit, Based on the generated data, an operational procedure manual will be created, and workers will proceed with deployment based on specific instructions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The calculation unit, Optimizing the power supply system improves the accuracy of the construction and streamlines operations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The calculation unit, It analyzes equipment information and environmental data to optimize placement and automatically formulates a wiring plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned intake unit is The system estimates the user's emotions and adjusts the timing of facility layout and equipment information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned intake unit is During import, the system automatically verifies the accuracy of facility drawings and equipment information and corrects any errors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned intake unit is During the import process, version control is performed on facility drawings and equipment information to ensure that the latest information is always maintained. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned intake unit is It estimates the user's emotions and determines the priority of information to include based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned intake unit is During data import, the system analyzes the relationship between facility drawings and equipment information, prioritizing the import of highly important information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned intake unit is During import, the method for importing facility drawings and equipment information is optimized based on the user's operation history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The calculation unit, It estimates the user's emotions and adjusts the calculation method for optimal rack placement and wiring based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, During calculations, the system monitors fluctuations in power consumption and heat generation of each device in real time and dynamically recalculates the optimal placement. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, During the calculation process, the rack layout and wiring are simulated to verify the optimal placement in advance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, It estimates the user's emotions and adjusts how the calculation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, During calculations, the optimal rack layout and cabling are determined by considering the facility's geographical and climatic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, During calculations, the system references the placement data of other data centers to compare and consider the optimal placement. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned procedure manual creation unit, The system estimates the user's emotions and adjusts the wording of the operational procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned procedure manual creation unit, When creating procedure manuals, adjust the level of detail in the manuals according to the skill level of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned procedure manual creation unit, When creating procedure manuals, we incorporate feedback from past operational procedure manuals to improve their accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned procedure manual creation unit, The system estimates the user's emotions and determines the priority of the procedure manual based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned procedure manual creation unit, When creating procedure manuals, refer to the worker's work history and customize the manual to be optimal. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned procedure manual creation unit, When creating procedure manuals, refer to operational manuals from other data centers to create the most suitable manuals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An import unit that takes in facility drawings and equipment information, Based on the information acquired by the aforementioned input unit, a calculation unit automatically calculates the optimal rack layout and wiring, taking into account the power supply system, thermal management, and load balancing. The system includes a procedure manual creation unit that creates an operation manual based on the data generated by the calculation unit. A system characterized by the following features.

2. The calculation unit, We analyze the power consumption and heat generation of each device to determine the optimal placement. The system according to feature 1.

3. The calculation unit, Optimize wiring length and routing to prevent wiring errors. The system according to feature 1.

4. The aforementioned procedure manual creation unit, Based on the generated data, an operational procedure manual will be created, and workers will proceed with deployment based on specific instructions. The system according to feature 1.

5. The calculation unit, Optimizing the power supply system improves the accuracy of the construction and streamlines operations. The system according to feature 1.

6. The calculation unit, It analyzes equipment information and environmental data to optimize placement and automatically formulates a wiring plan. The system according to feature 1.

7. The aforementioned intake unit is The system estimates the user's emotions and adjusts the timing of facility layout and equipment information acquisition based on the estimated user emotions. The system according to feature 1.

8. The aforementioned intake unit is During import, the system automatically verifies the accuracy of facility drawings and equipment information and corrects any errors. The system according to feature 1.

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