System

Abstract

To provide a system according to an embodiment that optimizes a cooling method and energy efficiency in a data center.SOLUTION: A system comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information on weather, and information on energy consumption in a data center. The analysis unit analyzes the information collected by the collection uni. The proposal unit proposes a cooling method in the data center on the basis of an analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately optimize data center cooling methods and energy efficiency, leaving room for improvement.

[0005] The system according to the embodiment aims to optimize the cooling method and energy efficiency of a data center. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information related to weather and information related to energy consumption in a data center. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a cooling method for the data center based on the analysis result obtained by the analysis unit. Effect of the Invention

[0007] An embodiment of the system can optimize cooling methods and energy efficiency in a data center. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that utilizes AI and big data to optimize the cooling and energy efficiency of a data center. The system monitors climate change and energy consumption in real time and automatically proposes optimal operation methods. This reduces energy consumption and carbon dioxide emissions. The system also corresponds to the goals of the SDGs and reduces the burden that data centers have on the environment. For example, the system collects information about the climate and information about energy consumption in the data center. This includes sensors that collect data on temperature, humidity, and power consumption. Next, AI analyzes the collected information and proposes a cooling method for the data center. The proposal includes a cooling method that takes into account the efficiency of energy consumption and a specific operation method that uses the outside air temperature. In addition, a specific execution method for automatically changing the settings of the cooling system is also proposed. This reduces energy consumption and carbon dioxide emissions. The system also has a function of estimating the user's emotions and adjusting the frequency and priority of data collection based on the estimated emotions. This makes data center operations more efficient and flexible. Furthermore, when collecting data, the timing of collection can be selected based on the data center's past operation history, and detailed data can be collected by focusing on a specific area. The system meets the SDGs goals and reduces the burden that data centers place on the environment, allowing the system to reduce energy consumption and lower carbon dioxide emissions.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information on the climate and information on energy consumption in the data center. The information on the climate includes, but is not limited to, for example, temperature, humidity, and wind speed. The information on the energy consumption includes, but is not limited to, for example, power consumption and the type of energy consumed. The collection unit can collect this information using, for example, a temperature sensor, a humidity sensor, and a power meter. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data using, for example, AI. The AI ​​can analyze the data using, for example, an algorithm such as a neural network or deep learning. The proposal unit proposes a cooling method in the data center based on the analysis result obtained by the analysis unit. The proposal unit can propose, for example, a cooling method that takes into account the efficiency of energy consumption and a specific operation method that utilizes the outside air temperature. This allows the system to optimize the cooling and energy efficiency of the data center. For example, the collection unit uses temperature sensors to monitor the temperature inside a data center in real time, the analysis unit analyzes the collected temperature data using AI, and the proposal unit proposes the optimal cooling method based on the analysis results. This allows the system to reduce energy consumption and lower carbon dioxide emissions.

[0030] The collection unit collects information on the climate and information on energy consumption in the data center. The climate information includes, but is not limited to, for example, temperature, humidity, wind speed, and the like. These pieces of information are very important because they directly affect the cooling efficiency of the data center. The temperature sensor measures the temperature inside and outside the data center in real time, and the humidity sensor detects the amount of moisture in the air. The wind speed sensor is used to measure the wind speed outside and evaluate the possibility of natural cooling. The energy consumption information includes, but is not limited to, for example, power consumption, the type of energy consumed, and the like. The power consumption indicates how much power each device and system in the data center is using, and the type of energy consumed identifies the energy source being used (e.g., electricity, gas, renewable energy, and the like). The collection unit can collect these pieces of information using, for example, a temperature sensor, a humidity sensor, a power meter, and the like. The temperature sensor is installed near each server rack and cooling device in the data center to provide a detailed temperature profile. The humidity sensor is incorporated into the air conditioning system in the data center to provide data to maintain an optimal humidity level. The power meter monitors the power consumption of each device and system in real time, providing basic data for evaluating energy efficiency. This allows the collection department to grasp the operation status of the data center in detail and provide the data necessary to propose efficient cooling methods. Furthermore, the collection department can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis department and proposal department can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data using, for example, AI. The AI ​​can analyze the data using algorithms such as neural networks and deep learning. Specifically, the AI ​​integrates data such as temperature, humidity, wind speed, and power consumption to analyze the cooling efficiency and energy consumption patterns of the data center. For example, a neural network can learn from past data and predict future cooling demand based on the current situation. A deep learning algorithm analyzes complex data patterns and provides insights to identify optimal cooling methods. The analysis unit makes full use of these AI techniques to generate specific proposals to maximize the operational efficiency of the data center. In addition, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past weather data, it predicts fluctuations in cooling demand during specific seasons and time periods and plans future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis unit continuously monitors the operational status of the data center and updates the analysis results as necessary. This allows the analysis unit to always provide highly accurate analysis based on the latest information, optimizing the operational efficiency of the data center.

[0032] The proposal unit proposes a cooling method for the data center based on the analysis results obtained by the analysis unit. The proposal unit can propose, for example, a cooling method that takes into account the efficiency of energy consumption or a specific operation method that uses the outside air temperature. Specifically, the proposal unit proposes a specific method for optimizing the operation of the cooling system of the data center based on the data provided by the analysis unit. For example, when the outside air temperature is low, natural cooling using the outside air is recommended to reduce energy consumption. In addition, the proposal unit also proposes adjusting the operation schedule of the cooling system to avoid peak energy consumption in a specific time period. Furthermore, the proposal unit also makes proposals for optimizing the layout of the data center and the arrangement of the equipment. For example, the proposal unit proposes a layout change to appropriately arrange the equipment that serves as a heat source and improve the cooling efficiency. In addition, the proposal unit also makes proposals for optimizing the maintenance schedule of the cooling system and improving the reliability of the system. In this way, the proposal unit provides a specific method for optimizing the cooling and energy efficiency of the data center, and can reduce operation costs and environmental loads. Furthermore, the proposal unit can predict future fluctuations in cooling demand based on the analysis results and develop a long-term operation plan. This enables the Proposal Department to provide strategic proposals for continuously improving the operational efficiency of the data center and optimize the performance of the entire system.

[0033] The proposal unit can propose a cooling method that takes into account energy consumption in the data center. The proposal unit, for example, proposes an optimal cooling method that takes into account energy consumption in the data center. For example, the proposal unit can propose a cooling method that aims to reduce power consumption and improve energy efficiency. As a result, the proposal unit can reduce energy consumption by proposing a cooling method that takes into account energy consumption efficiency. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze energy consumption data using AI and propose an optimal cooling method.

[0034] The collection unit may include sensors that collect data on temperature, humidity, and power consumption. The collection unit collects data on temperature, humidity, and power consumption, for example, using a temperature sensor, a humidity sensor, a power meter, and the like. For example, the collection unit can monitor the temperature in the data center in real time using a temperature sensor, monitor humidity using a humidity sensor, and monitor power consumption using a power meter. This allows the collection unit to collect detailed environmental data of the data center. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze sensor data using AI to optimize the frequency and timing of data collection.

[0035] The analysis unit may include an algorithm used by the AI. The analysis unit may analyze data using an AI algorithm such as a neural network or deep learning. For example, the analysis unit may analyze energy consumption data of a data center using a neural network and analyze weather data using deep learning. This allows the analysis unit to improve analysis accuracy by using an AI algorithm. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit may analyze data using an AI and provide data for proposing an optimal cooling method.

[0036] The proposal unit can propose a detailed operation method of the cooling method using the outside air temperature. The proposal unit proposes, for example, a specific operation method for improving the cooling efficiency of the data center by using the outside air temperature. For example, the proposal unit can set the timing of introducing outside air and a threshold value of the outside air temperature and propose an optimal cooling method. In this way, the proposal unit can improve the cooling efficiency by using the outside air temperature. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze outside air temperature data using AI and propose an optimal cooling method.

[0037] The system further includes an execution unit having a detailed execution method for automatically changing the cooling system settings. The execution unit, for example, includes a specific method for automatically changing the cooling system settings. For example, the execution unit can monitor the cooling system settings in real time and change the settings as necessary. In this way, the execution unit can improve energy efficiency by automatically changing the cooling system settings. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can optimize the cooling system settings using AI to improve energy efficiency.

[0038] The collection unit can select the collection timing based on the past operation history of the data center. The collection unit selects the optimal data collection timing based on, for example, the past operation history of the data center. For example, the collection unit can select a timing to avoid data collection during peak hours from the past operation history. The collection unit can also analyze the past operation history and select the most efficient data collection timing. Furthermore, the collection unit can select the data collection timing before and after the occurrence of a specific event based on the past operation history. This allows the collection unit to select the optimal collection timing based on the past operation history, thereby enabling efficient data collection. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze past operation data using AI and select the optimal data collection timing.

[0039] The collection unit can collect detailed data by focusing on a specific area of ​​the data center. For example, when an abnormality occurs in a specific area in the data center, the collection unit collects detailed data of the area. For example, the collection unit can collect detailed data of an area where the cooling systems of the data center are concentrated. In addition, the collection unit can collect detailed data of an area where energy consumption of the data center is high. This enables the collection unit to collect detailed data by focusing on a specific area. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze data of a specific area using AI and collect detailed data.

[0040] The collection unit can acquire external weather data in real time and collect information for maximizing the cooling efficiency of the data center. The collection unit can, for example, acquire real-time temperature data and optimize the settings of the cooling system. For example, the collection unit can acquire real-time humidity data and improve the efficiency of the cooling system. The collection unit can also acquire real-time wind speed data and suggest a cooling method using outside air. Thereby, the collection unit can maximize the cooling efficiency by acquiring the external weather data in real time. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can analyze weather data using AI and collect information for suggesting an optimal cooling method.

[0041] The collection unit can monitor the operation status of the equipment in the data center and collect data for predicting the peak of energy consumption. The collection unit can, for example, monitor the operation status of the server in the data center and predict the peak of energy consumption. For example, the collection unit can monitor the operation status of the cooling system in the data center and predict the peak of energy consumption. In addition, the collection unit can monitor the operation status of the lighting system in the data center and predict the peak of energy consumption. In this way, the collection unit can predict the peak of energy consumption by monitoring the operation status of the equipment. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the operation data of the equipment using AI and predict the peak of energy consumption.

[0042] The analysis unit can optimize the analysis algorithm by referring to the past operation data of the data center. The analysis unit selects an optimal analysis algorithm based on, for example, the past operation data of the data center. For example, the analysis unit can analyze the past operation data and improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can adjust parameters of the analysis algorithm by referring to the past operation data. In this way, the analysis unit can improve the accuracy of the analysis algorithm by referring to the past operation data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the past operation data using AI and select an optimal analysis algorithm.

[0043] The analysis unit can analyze the energy consumption pattern of the data center and detect anomalies. The analysis unit, for example, analyzes the energy consumption pattern and detects abnormal consumption. For example, the analysis unit can detect abnormal peaks based on the energy consumption pattern. Furthermore, the analysis unit can analyze the energy consumption pattern and detect abnormal fluctuations. In this way, the analysis unit can detect anomalies by analyzing the energy consumption pattern. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze energy consumption data using AI and detect anomalies.

[0044] The analysis unit can derive an optimal cooling strategy using performance data of the cooling system of the data center. The analysis unit derives an optimal cooling strategy based on, for example, the performance data of the cooling system. For example, the analysis unit can analyze the performance data of the cooling system to derive an efficient cooling strategy. Furthermore, the analysis unit can derive a cooling strategy with high energy efficiency using the performance data of the cooling system. Thereby, the analysis unit can derive an optimal cooling strategy by using the performance data of the cooling system. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the performance data of the cooling system using AI to derive an optimal cooling strategy.

[0045] The analysis unit can combine the energy consumption of the data center with the external environment data to propose measures to improve energy efficiency. The analysis unit can, for example, combine the energy consumption data with the external environment data to propose measures to improve energy efficiency. For example, the analysis unit can analyze the energy consumption data and the external environment data to propose an efficient operation method. Furthermore, the analysis unit can propose an operation strategy with high energy efficiency based on the energy consumption data and the external environment data. This allows the analysis unit to propose measures to improve energy efficiency by combining the energy consumption and the external environment data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the energy consumption data and the external environment data using AI to propose optimal measures to improve energy efficiency.

[0046] The analysis unit can improve the analysis accuracy by referring to the maintenance history of the cooling system of the data center. The analysis unit can improve the analysis accuracy by referring to, for example, the maintenance history of the cooling system. For example, the analysis unit can optimize the analysis algorithm based on the maintenance history of the cooling system. Furthermore, the analysis unit can analyze the maintenance history of the cooling system and improve the accuracy of the analysis result. Thereby, the analysis unit can improve the analysis accuracy by referring to the maintenance history of the cooling system. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the maintenance history using AI and select an optimal analysis algorithm.

[0047] The proposal unit can propose a cooling method in consideration of an operation schedule of the data center. The proposal unit proposes an optimal cooling method based on, for example, the operation schedule of the data center. For example, the proposal unit can propose an efficient cooling method in consideration of the operation schedule of the data center. Furthermore, the proposal unit can propose a cooling method with high energy efficiency by taking into account the operation schedule of the data center. In this way, the proposal unit can propose an efficient cooling method by taking into account the operation schedule. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze the operation schedule using AI and propose an optimal cooling method.

[0048] The proposal unit can propose a cooling method based on the energy supply source of the data center. The proposal unit, for example, proposes a cooling method using renewable energy. For example, the proposal unit can propose a cooling method using fossil fuels. Furthermore, the proposal unit can propose an optimal cooling method based on the energy supply source. This allows the proposal unit to propose an optimal cooling method based on the energy supply source. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can analyze the energy supply source using AI and propose an optimal cooling method.

[0049] The proposal unit can propose an optimal cooling method by referring to the maintenance history of the cooling system of the data center. The proposal unit proposes an optimal cooling method based on, for example, the maintenance history of the cooling system. For example, the proposal unit can propose an efficient cooling method by referring to the maintenance history of the cooling system. In addition, the proposal unit can propose a cooling method with high energy efficiency by taking into account the maintenance history of the cooling system. Thereby, the proposal unit can propose an optimal cooling method by referring to the maintenance history of the cooling system. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze the maintenance history using AI and propose an optimal cooling method.

[0050] The execution unit can make optimal setting changes by referring to past operation data of the data center. The execution unit makes optimal cooling system setting changes, for example, based on past operation data of the data center. For example, the execution unit can make efficient setting changes by referring to past operation data. Furthermore, the execution unit can analyze past operation data and make energy-efficient setting changes. This allows the execution unit to make optimal setting changes by referring to past operation data. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can analyze past operation data using AI and make optimal setting changes.

[0051] The execution unit can analyze the energy consumption pattern of the data center and change the settings. The execution unit, for example, analyzes the energy consumption pattern and makes an optimal change to the settings. For example, the execution unit can make an efficient change to the settings based on the energy consumption pattern. Furthermore, the execution unit can analyze the energy consumption pattern and make a setting change that is highly energy efficient. This enables the execution unit to make an efficient change to the settings by analyzing the energy consumption pattern. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can analyze energy consumption data using AI and make an optimal change to the settings.

[0052] The execution unit can change the settings using the performance data of the cooling system of the data center. The execution unit, for example, makes optimal setting changes based on the performance data of the cooling system. For example, the execution unit can analyze the performance data of the cooling system and make efficient setting changes. Furthermore, the execution unit can make energy-efficient setting changes using the performance data of the cooling system. This enables the execution unit to make optimal setting changes by using the performance data of the cooling system. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can analyze the performance data of the cooling system using AI and make optimal setting changes.

[0053] The execution unit can change the settings by combining the energy consumption of the data center and the external environment data. The execution unit can, for example, combine the energy consumption data and the external environment data to optimally change the settings. For example, the execution unit can analyze the energy consumption data and the external environment data to efficiently change the settings. Furthermore, the execution unit can change the settings to be energy efficient based on the energy consumption data and the external environment data. This enables the execution unit to efficiently change the settings by combining the energy consumption and the external environment data. Some or all of the above-mentioned processing in the execution unit can be performed, for example, using AI, or can be performed without using AI. For example, the execution unit can analyze the energy consumption data and the external environment data using AI to optimally change the settings.

[0054] The execution unit can change the settings by referring to the maintenance history of the cooling system of the data center. The execution unit, for example, makes an optimal setting change based on the maintenance history of the cooling system. For example, the execution unit can make an efficient setting change by referring to the maintenance history of the cooling system. Furthermore, the execution unit can make an energy-efficient setting change by taking into account the maintenance history of the cooling system. This allows the execution unit to make an optimal setting change by referring to the maintenance history of the cooling system. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can analyze the maintenance history using AI and make an optimal setting change.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The system may further include a prediction unit. The prediction unit may predict future energy consumption patterns based on the data obtained from the collection unit. For example, the prediction unit may analyze past data and predict seasonal variations in energy consumption. The prediction unit may also predict peaks in energy consumption based on specific events or maintenance schedules. Furthermore, the prediction unit may predict variations in energy consumption based on future weather conditions in combination with external environmental data. This allows the system to predict future energy consumption and create efficient operation plans.

[0057] The collection unit can have a function of 3D mapping the temperature distribution in each area in the data center. For example, the collection unit can measure the temperature in the data center in detail using multiple temperature sensors and generate a 3D map based on the data. The collection unit can also monitor fluctuations in temperature distribution in real time and detect abnormal temperature increases. Furthermore, the collection unit can provide the temperature distribution data to the analysis unit and use it to improve cooling efficiency. This allows the collection unit to more precisely manage the temperature in the data center.

[0058] The analysis unit can execute an anomaly detection algorithm based on the energy consumption data in the data center. For example, the analysis unit can detect abnormal consumption by comparing with normal energy consumption patterns. The analysis unit can also notify the provider of the results of the anomaly detection in real time to encourage a prompt response. Furthermore, the analysis unit can save the history of anomaly detection and use it as data to improve the accuracy of future anomaly detection. This allows the analysis unit to discover anomalies in the data center's energy consumption at an early stage and take appropriate measures.

[0059] The proposal unit can propose an optimal cooling method by combining the energy consumption of the data center with the external energy supply situation. For example, the proposal unit can propose a method of intensively operating the cooling system during times when there is a high supply of renewable energy. The proposal unit can also propose a cooling method that reduces energy consumption in preparation for an unstable energy supply. Furthermore, the proposal unit can propose a long-term cooling plan based on energy supply forecast data. This allows the proposal unit to propose an efficient cooling method according to the energy supply situation.

[0060] The execution unit can automatically adjust the settings of the cooling system based on the temperature data of each area in the data center. For example, the execution unit can strengthen cooling in areas with high temperatures and ease cooling in areas with low temperatures. The execution unit can also monitor the temperature data in real time and respond quickly when an abnormal temperature rise occurs. Furthermore, the execution unit can provide the temperature data to the analysis unit and use it to optimize the cooling system. This allows the execution unit to efficiently manage the temperature in the data center.

[0061] The collection unit can identify areas with low energy efficiency based on the energy consumption data of each area in the data center. For example, the collection unit can monitor areas with high energy consumption in real time and provide the data to the analysis unit. The collection unit can also collect detailed data on areas with low energy efficiency and provide data for proposing improvement measures. Furthermore, the collection unit can notify the provision unit of information useful for improving energy efficiency based on the energy consumption data. This allows the collection unit to collect data to improve energy efficiency in the data center.

[0062] The collection unit can optimize humidity management based on the humidity data of each area in the data center. For example, the collection unit can strengthen dehumidification in areas with high humidity and humidify areas with low humidity. The collection unit can also monitor the humidity data in real time and respond quickly when an abnormal humidity fluctuation occurs. Furthermore, the collection unit can provide the humidity data to the analysis unit to help optimize humidity management. This allows the collection unit to efficiently manage humidity in the data center.

[0063] The collection unit can optimize the operation of the cooling system of the data center based on the external weather data. For example, the collection unit can adjust the settings of the cooling system based on the external temperature data. The collection unit can also improve the efficiency of the cooling system based on the external humidity data. Furthermore, the collection unit can suggest a cooling method that utilizes outside air based on the external wind speed data. In this way, the collection unit can maximize the cooling efficiency of the data center by utilizing the external weather data.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The collection unit collects weather-related information and information on energy consumption in the data center. The weather-related information includes temperature, humidity, wind speed, etc., and the energy consumption-related information includes power consumption and the type of energy consumed, etc. The collection unit collects this information using a temperature sensor, a humidity sensor, a power meter, etc. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected data using AI and can use algorithms such as neural networks and deep learning. Step 3: The proposal unit proposes a cooling method for the data center based on the analysis results obtained by the analysis unit. The proposal unit can propose a cooling method that takes into account the efficiency of energy consumption and a specific operation method that utilizes the outside air temperature.

[0066] (Example 2) A system according to an embodiment of the present invention is a system that utilizes AI and big data to optimize the cooling and energy efficiency of a data center. The system monitors climate change and energy consumption in real time and automatically proposes optimal operation methods. This reduces energy consumption and carbon dioxide emissions. The system also corresponds to the goals of the SDGs and reduces the burden that data centers have on the environment. For example, the system collects information about the climate and information about energy consumption in the data center. This includes sensors that collect data on temperature, humidity, and power consumption. Next, AI analyzes the collected information and proposes a cooling method for the data center. The proposal includes a cooling method that takes into account the efficiency of energy consumption and a specific operation method that uses the outside air temperature. In addition, a specific execution method for automatically changing the settings of the cooling system is also proposed. This reduces energy consumption and carbon dioxide emissions. The system also has a function of estimating the user's emotions and adjusting the frequency and priority of data collection based on the estimated emotions. This makes data center operations more efficient and flexible. Furthermore, when collecting data, the timing of collection can be selected based on the data center's past operation history, and detailed data can be collected by focusing on a specific area. The system meets the SDGs goals and reduces the burden that data centers place on the environment, allowing the system to reduce energy consumption and lower carbon dioxide emissions.

[0067] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information on the climate and information on energy consumption in the data center. The information on the climate includes, but is not limited to, for example, temperature, humidity, and wind speed. The information on the energy consumption includes, but is not limited to, for example, power consumption and the type of energy consumed. The collection unit can collect this information using, for example, a temperature sensor, a humidity sensor, and a power meter. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data using, for example, AI. The AI ​​can analyze the data using, for example, an algorithm such as a neural network or deep learning. The proposal unit proposes a cooling method in the data center based on the analysis result obtained by the analysis unit. The proposal unit can propose, for example, a cooling method that takes into account the efficiency of energy consumption and a specific operation method that utilizes the outside air temperature. This allows the system to optimize the cooling and energy efficiency of the data center. For example, the collection unit uses temperature sensors to monitor the temperature inside a data center in real time, the analysis unit analyzes the collected temperature data using AI, and the proposal unit proposes the optimal cooling method based on the analysis results. This allows the system to reduce energy consumption and lower carbon dioxide emissions.

[0068] The collection unit collects information on the climate and information on energy consumption in the data center. The climate information includes, but is not limited to, for example, temperature, humidity, wind speed, and the like. These pieces of information are very important because they directly affect the cooling efficiency of the data center. The temperature sensor measures the temperature inside and outside the data center in real time, and the humidity sensor detects the amount of moisture in the air. The wind speed sensor is used to measure the wind speed outside and evaluate the possibility of natural cooling. The energy consumption information includes, but is not limited to, for example, power consumption, the type of energy consumed, and the like. The power consumption indicates how much power each device and system in the data center is using, and the type of energy consumed identifies the energy source being used (e.g., electricity, gas, renewable energy, and the like). The collection unit can collect these pieces of information using, for example, a temperature sensor, a humidity sensor, a power meter, and the like. The temperature sensor is installed near each server rack and cooling device in the data center to provide a detailed temperature profile. The humidity sensor is incorporated into the air conditioning system in the data center to provide data to maintain an optimal humidity level. The power meter monitors the power consumption of each device and system in real time, providing basic data for evaluating energy efficiency. This allows the collection department to grasp the operation status of the data center in detail and provide the data necessary to propose efficient cooling methods. Furthermore, the collection department can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis department and proposal department can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to efficiently and effectively collect data and improve the performance of the entire system.

[0069] The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data using, for example, AI. The AI ​​can analyze the data using algorithms such as neural networks and deep learning. Specifically, the AI ​​integrates data such as temperature, humidity, wind speed, and power consumption to analyze the cooling efficiency and energy consumption patterns of the data center. For example, a neural network can learn from past data and predict future cooling demand based on the current situation. A deep learning algorithm analyzes complex data patterns and provides insights to identify optimal cooling methods. The analysis unit makes full use of these AI techniques to generate specific proposals to maximize the operational efficiency of the data center. In addition, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past weather data, it predicts fluctuations in cooling demand during specific seasons and time periods and plans future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis unit continuously monitors the operational status of the data center and updates the analysis results as necessary. This allows the analysis unit to always provide highly accurate analysis based on the latest information, optimizing the operational efficiency of the data center.

[0070] The proposal unit proposes a cooling method for the data center based on the analysis results obtained by the analysis unit. The proposal unit can propose, for example, a cooling method that takes into account the efficiency of energy consumption or a specific operation method that uses the outside air temperature. Specifically, the proposal unit proposes a specific method for optimizing the operation of the cooling system of the data center based on the data provided by the analysis unit. For example, when the outside air temperature is low, natural cooling using the outside air is recommended to reduce energy consumption. In addition, the proposal unit also proposes adjusting the operation schedule of the cooling system to avoid peak energy consumption in a specific time period. Furthermore, the proposal unit also makes proposals for optimizing the layout of the data center and the arrangement of the equipment. For example, the proposal unit proposes a layout change to appropriately arrange the equipment that serves as a heat source and improve the cooling efficiency. In addition, the proposal unit also makes proposals for optimizing the maintenance schedule of the cooling system and improving the reliability of the system. In this way, the proposal unit provides a specific method for optimizing the cooling and energy efficiency of the data center, and can reduce operation costs and environmental loads. Furthermore, the proposal unit can predict future fluctuations in cooling demand based on the analysis results and develop a long-term operation plan. This enables the Proposal Department to provide strategic proposals for continuously improving the operational efficiency of the data center and optimize the performance of the entire system.

[0071] The proposal unit can propose a cooling method that takes into account energy consumption in the data center. The proposal unit, for example, proposes an optimal cooling method that takes into account energy consumption in the data center. For example, the proposal unit can propose a cooling method that aims to reduce power consumption and improve energy efficiency. As a result, the proposal unit can reduce energy consumption by proposing a cooling method that takes into account energy consumption efficiency. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze energy consumption data using AI and propose an optimal cooling method.

[0072] The collection unit may include sensors that collect data on temperature, humidity, and power consumption. The collection unit collects data on temperature, humidity, and power consumption, for example, using a temperature sensor, a humidity sensor, a power meter, and the like. For example, the collection unit can monitor the temperature in the data center in real time using a temperature sensor, monitor humidity using a humidity sensor, and monitor power consumption using a power meter. This allows the collection unit to collect detailed environmental data of the data center. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze sensor data using AI to optimize the frequency and timing of data collection.

[0073] The analysis unit may include an algorithm used by the AI. The analysis unit may analyze data using an AI algorithm such as a neural network or deep learning. For example, the analysis unit may analyze energy consumption data of a data center using a neural network and analyze weather data using deep learning. This allows the analysis unit to improve analysis accuracy by using an AI algorithm. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit may analyze data using an AI and provide data for proposing an optimal cooling method.

[0074] The proposal unit can propose a detailed operation method of the cooling method using the outside air temperature. The proposal unit proposes, for example, a specific operation method for improving the cooling efficiency of the data center by using the outside air temperature. For example, the proposal unit can set the timing of introducing outside air and a threshold value of the outside air temperature and propose an optimal cooling method. In this way, the proposal unit can improve the cooling efficiency by using the outside air temperature. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze outside air temperature data using AI and propose an optimal cooling method.

[0075] The system further includes an execution unit having a detailed execution method for automatically changing the cooling system settings. The execution unit, for example, includes a specific method for automatically changing the cooling system settings. For example, the execution unit can monitor the cooling system settings in real time and change the settings as necessary. In this way, the execution unit can improve energy efficiency by automatically changing the cooling system settings. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can optimize the cooling system settings using AI to improve energy efficiency.

[0076] The collection unit can estimate the user's emotion and adjust the frequency of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the frequency of data collection based on the estimated emotion. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the load on the system. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can preferentially collect only important data and perform analysis quickly. Thereby, the collection unit can adjust the frequency of data collection according to the user's emotion and reduce the load on the system. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input image data of the user taken by a camera to the generation AI and cause the generation AI to execute an estimation of the user's emotion.

[0077] The collection unit can select the collection timing based on the past operation history of the data center. The collection unit selects the optimal data collection timing based on, for example, the past operation history of the data center. For example, the collection unit can select a timing to avoid collecting data during peak hours from the past operation history. The collection unit can also analyze the past operation history and select the most efficient data collection timing. Furthermore, the collection unit can select the data collection timing before and after the occurrence of a specific event based on the past operation history. This allows the collection unit to select the optimal collection timing based on the past operation history, thereby enabling efficient data collection. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze past operation data using AI and select the optimal data collection timing.

[0078] The collection unit can collect detailed data by focusing on a specific area of ​​the data center. For example, when an abnormality occurs in a specific area in the data center, the collection unit collects detailed data of the area. For example, the collection unit can collect detailed data of an area where the cooling systems of the data center are concentrated. In addition, the collection unit can collect detailed data of an area where energy consumption of the data center is high. This enables the collection unit to collect detailed data by focusing on a specific area. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze data of a specific area using AI and collect detailed data.

[0079] The collection unit can acquire external weather data in real time and collect information for maximizing the cooling efficiency of the data center. The collection unit can, for example, acquire real-time temperature data and optimize the settings of the cooling system. For example, the collection unit can acquire real-time humidity data and improve the efficiency of the cooling system. The collection unit can also acquire real-time wind speed data and suggest a cooling method using outside air. Thereby, the collection unit can maximize the cooling efficiency by acquiring the external weather data in real time. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can analyze weather data using AI and collect information for suggesting an optimal cooling method.

[0080] The collection unit can monitor the operation status of the equipment in the data center and collect data for predicting the peak of energy consumption. The collection unit can, for example, monitor the operation status of the server in the data center and predict the peak of energy consumption. For example, the collection unit can monitor the operation status of the cooling system in the data center and predict the peak of energy consumption. In addition, the collection unit can monitor the operation status of the lighting system in the data center and predict the peak of energy consumption. In this way, the collection unit can predict the peak of energy consumption by monitoring the operation status of the equipment. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the operation data of the equipment using AI and predict the peak of energy consumption.

[0081] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated emotion. For example, when the user is stressed, the collection unit can preferentially collect only important data. Furthermore, when the user is relaxed, the collection unit can preferentially collect detailed data. Furthermore, when the user is in a hurry, the collection unit can preferentially collect data that requires rapid analysis. Thereby, the collection unit can preferentially collect important data by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input image data of the user taken by a camera to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated emotion. For example, the analysis unit can provide a simple and highly visible display method when the user is nervous. Furthermore, the analysis unit can provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can provide a display method that focuses on the main points when the user is in a hurry. Thereby, the analysis unit can improve visibility by adjusting the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input image data of the user taken by a camera to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0083] The analysis unit can optimize the analysis algorithm by referring to the past operation data of the data center. The analysis unit selects an optimal analysis algorithm based on, for example, the past operation data of the data center. For example, the analysis unit can analyze the past operation data and improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can adjust parameters of the analysis algorithm by referring to the past operation data. In this way, the analysis unit can improve the accuracy of the analysis algorithm by referring to the past operation data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the past operation data using AI and select an optimal analysis algorithm.

[0084] The analysis unit can analyze the energy consumption pattern of the data center and detect anomalies. The analysis unit, for example, analyzes the energy consumption pattern and detects abnormal consumption. For example, the analysis unit can detect abnormal peaks based on the energy consumption pattern. Furthermore, the analysis unit can analyze the energy consumption pattern and detect abnormal fluctuations. In this way, the analysis unit can detect anomalies by analyzing the energy consumption pattern. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze energy consumption data using AI and detect anomalies.

[0085] The analysis unit can derive an optimal cooling strategy using performance data of the cooling system of the data center. The analysis unit derives an optimal cooling strategy based on, for example, the performance data of the cooling system. For example, the analysis unit can analyze the performance data of the cooling system to derive an efficient cooling strategy. Furthermore, the analysis unit can derive a cooling strategy with high energy efficiency using the performance data of the cooling system. Thereby, the analysis unit can derive an optimal cooling strategy by using the performance data of the cooling system. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the performance data of the cooling system using AI to derive an optimal cooling strategy.

[0086] The analysis unit can combine the energy consumption of the data center with the external environment data to propose measures to improve energy efficiency. The analysis unit can, for example, combine the energy consumption data with the external environment data to propose measures to improve energy efficiency. For example, the analysis unit can analyze the energy consumption data and the external environment data to propose an efficient operation method. Furthermore, the analysis unit can propose an operation strategy with high energy efficiency based on the energy consumption data and the external environment data. This allows the analysis unit to propose measures to improve energy efficiency by combining the energy consumption and the external environment data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the energy consumption data and the external environment data using AI to propose optimal measures to improve energy efficiency.

[0087] The analysis unit can improve the analysis accuracy by referring to the maintenance history of the cooling system of the data center. The analysis unit can improve the analysis accuracy by referring to, for example, the maintenance history of the cooling system. For example, the analysis unit can optimize the analysis algorithm based on the maintenance history of the cooling system. Furthermore, the analysis unit can analyze the maintenance history of the cooling system and improve the accuracy of the analysis result. Thereby, the analysis unit can improve the analysis accuracy by referring to the maintenance history of the cooling system. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the maintenance history using AI and select an optimal analysis algorithm.

[0088] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the expression method of the proposal based on the estimated emotion. For example, the suggestion unit can make a simple and highly visible proposal when the user is nervous. Furthermore, the suggestion unit can make a proposal including detailed information when the user is relaxed. Furthermore, the suggestion unit can make a proposal that focuses on the main points when the user is in a hurry. Thereby, the suggestion unit can improve visibility by adjusting the expression method of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input image data of the user taken by a camera to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0089] The proposal unit can propose a cooling method in consideration of an operation schedule of the data center. The proposal unit proposes an optimal cooling method based on, for example, the operation schedule of the data center. For example, the proposal unit can propose an efficient cooling method in consideration of the operation schedule of the data center. Furthermore, the proposal unit can propose a cooling method with high energy efficiency by taking into account the operation schedule of the data center. In this way, the proposal unit can propose an efficient cooling method by taking into account the operation schedule. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze the operation schedule using AI and propose an optimal cooling method.

[0090] The proposal unit can propose a cooling method based on the energy supply source of the data center. The proposal unit, for example, proposes a cooling method using renewable energy. For example, the proposal unit can propose a cooling method using fossil fuels. Furthermore, the proposal unit can propose an optimal cooling method based on the energy supply source. This allows the proposal unit to propose an optimal cooling method based on the energy supply source. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can analyze the energy supply source using AI and propose an optimal cooling method.

[0091] The proposal unit can propose an optimal cooling method by referring to the maintenance history of the cooling system of the data center. The proposal unit proposes an optimal cooling method based on, for example, the maintenance history of the cooling system. For example, the proposal unit can propose an efficient cooling method by referring to the maintenance history of the cooling system. In addition, the proposal unit can propose a cooling method with high energy efficiency by taking into account the maintenance history of the cooling system. Thereby, the proposal unit can propose an optimal cooling method by referring to the maintenance history of the cooling system. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can analyze the maintenance history using AI and propose an optimal cooling method.

[0092] The execution unit can estimate the user's emotion and automatically change the cooling system settings based on the estimated user's emotion. The execution unit, for example, estimates the user's emotion and automatically changes the cooling system settings based on the estimated emotion. For example, the execution unit can change the cooling system settings gently when the user is stressed. In addition, the execution unit can change the cooling system settings proactively when the user is relaxed. Furthermore, the execution unit can quickly change the cooling system settings when the user is in a hurry. As a result, the execution unit can automatically change the cooling system settings according to the user's emotion, thereby enabling efficient cooling. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input image data of the user taken by a camera to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0093] The execution unit can make optimal setting changes by referring to past operation data of the data center. The execution unit makes optimal cooling system setting changes, for example, based on past operation data of the data center. For example, the execution unit can make efficient setting changes by referring to past operation data. Furthermore, the execution unit can analyze past operation data and make energy-efficient setting changes. This allows the execution unit to make optimal setting changes by referring to past operation data. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can analyze past operation data using AI and make optimal setting changes.

[0094] The execution unit can analyze the energy consumption pattern of the data center and change the settings. The execution unit, for example, analyzes the energy consumption pattern and makes an optimal change to the settings. For example, the execution unit can make an efficient change to the settings based on the energy consumption pattern. Furthermore, the execution unit can analyze the energy consumption pattern and make a setting change that is highly energy efficient. This enables the execution unit to make an efficient change to the settings by analyzing the energy consumption pattern. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can analyze energy consumption data using AI and make an optimal change to the settings.

[0095] The execution unit can change the settings using the performance data of the cooling system of the data center. The execution unit, for example, makes optimal setting changes based on the performance data of the cooling system. For example, the execution unit can analyze the performance data of the cooling system and make efficient setting changes. Furthermore, the execution unit can make energy-efficient setting changes using the performance data of the cooling system. This enables the execution unit to make optimal setting changes by using the performance data of the cooling system. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can analyze the performance data of the cooling system using AI and make optimal setting changes.

[0096] The execution unit can change the settings by combining the energy consumption of the data center and the external environment data. The execution unit can, for example, combine the energy consumption data and the external environment data to optimally change the settings. For example, the execution unit can analyze the energy consumption data and the external environment data to efficiently change the settings. Furthermore, the execution unit can change the settings to be energy efficient based on the energy consumption data and the external environment data. This enables the execution unit to efficiently change the settings by combining the energy consumption and the external environment data. Some or all of the above-mentioned processing in the execution unit can be performed, for example, using AI, or can be performed without using AI. For example, the execution unit can analyze the energy consumption data and the external environment data using AI to optimally change the settings.

[0097] The execution unit can change the settings by referring to the maintenance history of the cooling system of the data center. The execution unit, for example, makes an optimal setting change based on the maintenance history of the cooling system. For example, the execution unit can make an efficient setting change by referring to the maintenance history of the cooling system. Furthermore, the execution unit can make an energy-efficient setting change by taking into account the maintenance history of the cooling system. This allows the execution unit to make an optimal setting change by referring to the maintenance history of the cooling system. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can analyze the maintenance history using AI and make an optimal setting change.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The system may further include a prediction unit. The prediction unit may predict future energy consumption patterns based on the data obtained from the collection unit. For example, the prediction unit may analyze past data and predict seasonal variations in energy consumption. The prediction unit may also predict peaks in energy consumption based on specific events or maintenance schedules. Furthermore, the prediction unit may predict variations in energy consumption based on future weather conditions in combination with external environmental data. This allows the system to predict future energy consumption and create efficient operation plans.

[0100] The suggestion unit can estimate the user's emotion and adjust the suggested cooling method based on the estimated emotion. For example, the suggestion unit can suggest a simple and intuitive cooling method when the user is stressed. Also, when the user is relaxed, the suggestion unit can suggest multiple cooling methods along with detailed data. Furthermore, when the user is in a hurry, the suggestion unit can preferentially suggest a cooling method that can be quickly implemented. In this way, the suggestion unit can suggest an optimal cooling method according to the user's emotion.

[0101] The collection unit can have a function of 3D mapping the temperature distribution in each area in the data center. For example, the collection unit can measure the temperature in the data center in detail using multiple temperature sensors and generate a 3D map based on the data. The collection unit can also monitor fluctuations in temperature distribution in real time and detect abnormal temperature increases. Furthermore, the collection unit can provide the temperature distribution data to the analysis unit and use it to improve cooling efficiency. This allows the collection unit to more precisely manage the temperature in the data center.

[0102] The analysis unit can execute an anomaly detection algorithm based on the energy consumption data in the data center. For example, the analysis unit can detect abnormal consumption by comparing with normal energy consumption patterns. The analysis unit can also notify the provider of the results of the anomaly detection in real time to encourage a prompt response. Furthermore, the analysis unit can save the history of anomaly detection and use it as data to improve the accuracy of future anomaly detection. This allows the analysis unit to discover anomalies in the data center's energy consumption at an early stage and take appropriate measures.

[0103] The proposal unit can propose an optimal cooling method by combining the energy consumption of the data center with the external energy supply situation. For example, the proposal unit can propose a method of intensively operating the cooling system during times when there is a high supply of renewable energy. The proposal unit can also propose a cooling method that reduces energy consumption in preparation for an unstable energy supply. Furthermore, the proposal unit can propose a long-term cooling plan based on energy supply forecast data. This allows the proposal unit to propose an efficient cooling method according to the energy supply situation.

[0104] The execution unit can automatically adjust the settings of the cooling system based on the temperature data of each area in the data center. For example, the execution unit can strengthen cooling in areas with high temperatures and ease cooling in areas with low temperatures. The execution unit can also monitor the temperature data in real time and respond quickly when an abnormal temperature rise occurs. Furthermore, the execution unit can provide the temperature data to the analysis unit and use it to optimize the cooling system. This allows the execution unit to efficiently manage the temperature in the data center.

[0105] The collection unit can estimate the user's emotions and adjust the priority of data collection based on the estimated emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. Also, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that requires rapid analysis. In this way, the collection unit can achieve efficient data collection by adjusting the priority of data collection according to the user's emotions.

[0106] The collection unit can identify areas with low energy efficiency based on the energy consumption data of each area in the data center. For example, the collection unit can monitor areas with high energy consumption in real time and provide the data to the analysis unit. The collection unit can also collect detailed data on areas with low energy efficiency and provide data for proposing improvement measures. Furthermore, the collection unit can notify the provision unit of information useful for improving energy efficiency based on the energy consumption data. This allows the collection unit to collect data to improve energy efficiency in the data center.

[0107] The collection unit can optimize humidity management based on the humidity data of each area in the data center. For example, the collection unit can strengthen dehumidification in areas with high humidity and humidify areas with low humidity. The collection unit can also monitor the humidity data in real time and respond quickly when an abnormal humidity fluctuation occurs. Furthermore, the collection unit can provide the humidity data to the analysis unit to help optimize humidity management. This allows the collection unit to efficiently manage humidity in the data center.

[0108] The collection unit can optimize the operation of the cooling system of the data center based on the external weather data. For example, the collection unit can adjust the settings of the cooling system based on the external temperature data. The collection unit can also improve the efficiency of the cooling system based on the external humidity data. Furthermore, the collection unit can suggest a cooling method that utilizes outside air based on the external wind speed data. In this way, the collection unit can maximize the cooling efficiency of the data center by utilizing the external weather data.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects weather-related information and information on energy consumption in the data center. The weather-related information includes temperature, humidity, wind speed, etc., and the energy consumption-related information includes power consumption and the type of energy consumed, etc. The collection unit collects this information using a temperature sensor, a humidity sensor, a power meter, etc. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected data using AI and can use algorithms such as neural networks and deep learning. Step 3: The proposal unit proposes a cooling method for the data center based on the analysis results obtained by the analysis unit. The proposal unit can propose a cooling method that takes into account the efficiency of energy consumption and a specific operation method that utilizes the outside air temperature.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] For example, the collection unit monitors the temperature and humidity in the data center in real time using a temperature sensor and a humidity sensor of the smart device 14, and analyzes the data collected by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using AI, and the proposal unit proposes an optimal cooling method based on the analysis result by the specific processing unit 290 of the data processing device 12. For example, the execution unit automatically changes the settings of the cooling system by the specific processing unit 290 of the data processing device 12 to improve energy efficiency. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] For example, the collection unit monitors the temperature and humidity in the data center in real time using the temperature sensor and humidity sensor of the smart glasses 214, and analyzes the data collected by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using AI, and the proposal unit proposes an optimal cooling method based on the analysis result by the specific processing unit 290 of the data processing device 12. For example, the execution unit automatically changes the settings of the cooling system by the specific processing unit 290 of the data processing device 12 to improve energy efficiency. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] For example, the collection unit monitors the temperature and humidity in the data center in real time using a temperature sensor and a humidity sensor of the headset terminal 314, and analyzes the data collected by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using AI, and the proposal unit proposes an optimal cooling method based on the analysis result by the specific processing unit 290 of the data processing device 12. For example, the execution unit automatically changes the settings of the cooling system by the specific processing unit 290 of the data processing device 12 to improve energy efficiency. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] For example, the collection unit monitors the temperature and humidity in the data center in real time using a temperature sensor and a humidity sensor of the robot 414, and analyzes the data collected by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using AI, and the proposal unit proposes an optimal cooling method based on the analysis result by the specific processing unit 290 of the data processing device 12. For example, the execution unit automatically changes the settings of the cooling system by the specific processing unit 290 of the data processing device 12 to improve energy efficiency. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) Information about the climate, A collection unit for collecting information regarding energy consumption in a data center; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes a cooling method for the data center based on the analysis result obtained by the analysis unit; and Equipped A system characterized by: (Appendix 2) The suggestion unit, Proposing a cooling method that takes into account energy consumption in data centers 2. The system of claim 1, (Appendix 3) The collecting unit includes: Equipped with sensors to collect data on temperature, humidity, and power consumption 2. The system of claim 1, (Appendix 4) The analysis unit is Equipped with algorithms used by AI 2. The system of claim 1, (Appendix 5) The suggestion unit, Propose detailed operation method of cooling method using outside air temperature 2. The system of claim 1, (Appendix 6) and an execution unit that includes detailed execution instructions for automatically changing the settings of the cooling system. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 8) The collecting unit includes: Select collection timing based on the data center's past operational history 2. The system of claim 1, (Appendix 9) The collecting unit includes: Focus on specific areas of your data center to gather detailed data 2. The system of claim 1, (Appendix 10) The collecting unit includes: Obtaining real-time external weather data and gathering information to maximize data center cooling efficiency 2. The system of claim 1, (Appendix 11) The collecting unit includes: Monitor the operation status of equipment in data centers and collect data to predict peak energy consumption 2. The system of claim 1, (Appendix 12) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is Optimize analysis algorithms by referencing historical data from data centers 2. The system of claim 1, (Appendix 15) The analysis unit is Analyzing data center energy consumption patterns and detecting anomalies 2. The system of claim 1, (Appendix 16) The analysis unit is Derive optimal cooling strategies using data from data center cooling system performance data 2. The system of claim 1, (Appendix 17) The analysis unit is Combining data on data center energy consumption and external environmental data to propose measures to improve energy efficiency 2. The system of claim 1, (Appendix 18) The analysis unit is Improve analysis accuracy by referencing the maintenance history of data center cooling systems 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, Propose cooling methods taking into account the data center operation schedule 2. The system of claim 1, (Appendix 21) The suggestion unit, Propose cooling methods based on the data center's energy source 2. The system of claim 1, (Appendix 22) The suggestion unit, Propose optimal cooling methods by referring to the maintenance history of the data center's cooling system 2. The system of claim 1, (Appendix 23) The execution unit is To estimate a user's emotions and automatically change the settings of a cooling system based on the estimated user's emotions. 2. The system of claim 1, (Appendix 24) The execution unit is Refer to the past operation data of the data center to make optimal configuration changes 2. The system of claim 1, (Appendix 25) The execution unit is Analyze data center energy consumption patterns and make configuration changes 2. The system of claim 1, (Appendix 26) The execution unit is Use performance data from the data center's cooling system to change settings 2. The system of claim 1, (Appendix 27) The execution unit is Combining data on data center energy consumption and external environmental data to change settings 2. The system of claim 1, (Appendix 28) The execution unit is View maintenance history of data center cooling systems and make configuration changes 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Information about the climate, A collection unit for collecting information regarding energy consumption in a data center; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes a cooling method for the data center based on the analysis result obtained by the analysis unit; and Equipped A system characterized by:

2. The suggestion unit, Proposing a cooling method that takes into account energy consumption in data centers 2. The system of claim 1 .

3. The collecting unit includes: Equipped with sensors to collect data on temperature, humidity, and power consumption 2. The system of claim 1 .

4. The analysis unit is Equipped with algorithms used by AI 2. The system of claim 1 .

5. The suggestion unit, Propose detailed operation method of cooling method using outside air temperature 2. The system of claim 1 .

6. and an execution unit that includes detailed execution instructions for automatically changing the settings of the cooling system.

2. The system of claim 1 .

7. The collecting unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1 .

8. The collecting unit includes: Select collection timing based on the data center's past operational history 2. The system of claim 1 .

9. The collecting unit includes: Focus on specific areas of your data center to gather detailed data 2. The system of claim 1 .

10. The collecting unit includes: Obtaining real-time external weather data and gathering information to maximize data center cooling efficiency 2. The system of claim 1 .

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