System

Abstract

The purpose of the system of the present embodiment is to enable an enterprise to perform optimal renewable energy investment using energy consumption data. A system according to an embodiment includes a collection unit, a storage unit, an analysis unit, an import unit, a provision unit, and an execution support unit. The collecting part is used for collecting energy consumption data. The storage unit stores the data collected by the collection unit to a cloud. The analysis unit analyzes the data stored by the storage unit. The introduction unit introduces the latest energy trend or investment object information on the basis of the data analyzed by the analysis unit. The provision unit provides a renewable energy investment scheme on the basis of the information obtained by the introduction unit. The execution support unit provides specific steps required for executing the investment scheme provided by the provision unit.

Description

Technical Field

[0001] The technology disclosed herein relates to a system. Background Technology

[0002] Patent Document 1 discloses a personalized chatbot control method executed by at least one processor, the method comprising: receiving user speech; adding the user speech to a prompt containing instructions related to a chatbot role; encoding the prompt; and inputting the encoded prompt into a language model to generate chatbot speech in response to the user speech.

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2022-180282. Summary of the Invention

[0004] In existing technologies, there is a problem that enterprises find it difficult to effectively utilize energy consumption data for renewable energy investment.

[0005] The purpose of this technical solution is to enable enterprises to make optimal investments in renewable energy using energy consumption data.

[0006] This technical solution's system comprises a collection unit, a storage unit, an analysis unit, an import unit, a provision unit, and an execution support unit. The collection unit collects energy consumption data. The storage unit saves the data collected by the collection unit to the cloud. The analysis unit analyzes the data stored by the storage unit. The import unit, based on the data analyzed by the analysis unit, imports the latest energy trends or investment target information. The provision unit, based on the information obtained by the import unit, provides renewable energy investment plans. The execution support unit provides the specific steps required to implement the investment plans provided by the provision unit.

[0007] The system of this technical solution enables enterprises to make optimal investments in renewable energy using energy consumption data. Attached Figure Description

[0008] Figure 1 This is a conceptual diagram illustrating an example of the configuration of a data processing system according to the first embodiment.

[0009] Figure 2 This is a conceptual diagram illustrating an example of the main functions of the data processing apparatus and smart device according to the first embodiment.

[0010] Figure 3 This is a conceptual diagram illustrating an example of the data processing system configuration in the second embodiment.

[0011] Figure 4 This is a conceptual diagram illustrating an example of the main functions of the data processing device and smart glasses according to the second embodiment.

[0012] Figure 5 This is a conceptual diagram illustrating an example of the data processing system configuration in the third embodiment.

[0013] Figure 6 This is a conceptual diagram illustrating an example of the main functions of the data processing device and head-mounted terminal according to the third embodiment.

[0014] Figure 7 This is a conceptual diagram illustrating an example of the data processing system configuration in the fourth embodiment.

[0015] Figure 8 This is a conceptual diagram illustrating an example of the functions of the main parts of the data processing device and robot according to the fourth embodiment.

[0016] Figure 9 It represents an emotion graph that maps multiple emotions.

[0017] Figure 10 It represents an emotion graph that maps multiple emotions.

[0018] Explanation of reference numerals in the attached figures

[0019] Data processing systems 10, 210, 310, and 410

[0020] 12 Data processing device

[0021] 14 Smart devices

[0022] 214 Smart Glasses

[0023] 314 Head-mounted terminal

[0024] 414 Robot. Detailed Implementation

[0025] Hereinafter, an example of an implementation of the system involved in this disclosure will be described with reference to the accompanying drawings.

[0026] First, let's explain the terms used in the following description.

[0027] In the following embodiments, the processor (hereinafter referred to as "processor"), as indicated by the reference numerals, can be a single computing device or a combination of multiple computing devices. Furthermore, a processor can be a single computing device or a combination of multiple computing devices. Examples of computing devices include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose Computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit), etc.

[0028] In the following embodiments, RAM (Random Access Memory), as indicated in the figures, is a memory for temporary storage of information, which is used by the processor as working memory.

[0029] In the following embodiments, the memory, as indicated by the reference numerals, is one or more non-volatile storage devices used to store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), disk (e.g., hard disk), or magnetic tape, etc.

[0030] In the following embodiments, the communication I / F (Interface) with reference numerals is an interface including a communication processor and an antenna, etc. The communication I / F is responsible for communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0031] In the following implementation, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, only B, or a combination of A and B. Furthermore, in this specification, when "and / or" connects more than three items, the same approach as "A and / or B" applies.

[0032] First Implementation Method

[0033] Figure 1 An example of the configuration of the data processing system 10 according to the first embodiment is shown.

[0034] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0035] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. In addition, the receiver 38, output device 40, and camera 42 are also connected to the bus 52.

[0037] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A sends data representing user input received via the touchscreen 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, a specific processing unit 290 (see reference...) Figure 2 Get the data that represents the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, which present data to the user by outputting data in a user-perceptible format (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0040] Figure 2 An example of the main functions of the data processing device 12 and the smart device 14 is shown.

[0041] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0042] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0043] In the smart device 14, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The specific processing program 60 is used in conjunction with the data processing system 10. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart device 14 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0044] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. Furthermore, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0045] Implementation Method 1

[0046] The energy consumption optimization system described in this invention is used to optimize enterprise energy consumption and support renewable energy investment. This system continuously monitors an enterprise's energy consumption through IoT devices and saves the data to the cloud. Subsequently, generative AI, based on this information and combined with the latest energy trends and investment target information, provides the enterprise with the optimal renewable energy investment plan. This enables enterprises to efficiently reduce CO2 emissions and achieve a transition to a sustainable business model. For example, IoT devices installed in factories or office buildings can collect data such as electricity and gas consumption in real time. This data is saved to the cloud for subsequent analysis by generative AI. The generative AI analyzes the energy consumption data saved in the cloud, imports the latest energy trends and investment target information, and generates the most suitable renewable energy investment plan for the enterprise. For example, it proposes investment recommendations for renewable energy sources such as solar power and wind power. This recommendation takes into account the enterprise's energy consumption patterns and market trends. Furthermore, the generative AI also provides the enterprise with specific steps required to implement the proposed investment plan. For example, it provides detailed suggestions on what equipment to introduce and what contracts to sign. Thus, enterprises can efficiently invest in renewable energy and achieve CO2 emission reduction. Through this system, enterprises can obtain support for transitioning to a sustainable business model. For example, it is expected to reduce energy costs and environmental impact, thereby enhancing corporate competitiveness. Furthermore, through renewable energy investments, companies can fulfill their social responsibility (CSR) obligations. Therefore, energy consumption optimization systems can optimize a company's energy consumption and support renewable energy investments.

[0047] The energy consumption optimization system described in this embodiment includes a collection unit, a storage unit, an analysis unit, an import unit, a provision unit, and an execution support unit. The collection unit collects energy consumption data from the enterprise. For example, it collects data such as electricity consumption and gas consumption in real time through IoT devices installed in factories or office buildings. The storage unit stores the data collected by the collection unit in the cloud. For example, the collected data is stored in cloud services such as AWS, Google Cloud, and Microsoft Azure. The analysis unit analyzes the energy consumption data stored in the cloud using generative AI. For example, generative AI uses technologies such as deep learning and neural networks to analyze energy consumption data. The import unit imports the latest energy trends and investment opportunities using generative AI. For example, it imports information such as renewable energy price trends and the status of new technology introductions. The provision unit, using generative AI, considers the enterprise's energy consumption patterns and market trends to provide optimal renewable energy investment solutions. For example, it proposes investment solutions such as solar power generation and wind power generation. The execution support unit, using generative AI, provides the enterprise with specific steps required to implement the proposed investment solutions. For example, it provides detailed suggestions on what equipment should be introduced and what contracts should be signed. Therefore, the energy consumption optimization system involved in this embodiment can optimize the energy consumption of enterprises and support renewable energy investment.

[0048] The data collection unit can gather energy consumption data in real time through IoT devices installed in factories or office buildings. For example, the collection unit can utilize IoT devices such as smart meters, sensors, and gateways to collect energy consumption data in the factory or office building in real time. Smart meters can measure electricity consumption in real time and send the data to the collection unit. Sensors can measure gas consumption or temperature data and send the data to the collection unit. Furthermore, gateways can integrate data from multiple IoT devices and send it to the collection unit. Thus, the collection unit can collect energy consumption data from the factory or office building in real time. Some or all of the processing in the collection unit can be implemented using AI, or it can be done without AI. For example, the collection unit can input data obtained from IoT devices into generative AI to optimize the timing and frequency of data collection.

[0049] The analysis unit can analyze energy consumption data stored in the cloud using generative AI. For example, the analysis unit can utilize generative AI technologies such as deep learning and neural networks to analyze energy consumption data stored in the cloud. Deep learning, for instance, is a technique that builds neural networks with advanced pattern recognition capabilities by learning from large amounts of data. Neural networks are networks composed of multiple layers of artificial neurons, capable of processing input data and generating outputs. The analysis unit can utilize these technologies to improve the accuracy of energy consumption data analysis. For example, the analysis unit can use a generative AI model that takes energy consumption data as input and outputs consumption patterns or anomaly detection. Thus, the analysis unit can analyze energy consumption data stored in the cloud using generative AI. Some or all of the processing described above by the analysis unit can be implemented using AI, or AI may not be used.

[0050] The import department can import the latest energy trends and investment target information through generative AI. For example, generative AI can automatically collect information such as renewable energy price trends and the status of new technology introductions, and provide it to the import department. Based on this information, the import department generates optimal renewable energy investment plans for companies. For example, generative AI can collect the latest energy trends from news reports, academic papers, government reports, etc. on the internet. Furthermore, generative AI can also collect investment target information such as the company's financial situation and project progress, and provide it to the import department. Thus, the import department can efficiently import the latest energy trends and investment target information through generative AI. Some or all of the above-mentioned processes in the import department can be implemented using AI, or AI can be used without it.

[0051] The supply department can utilize generative AI to consider a company's energy consumption patterns and market trends, providing renewable energy investment solutions. For example, generative AI can analyze a company's energy consumption data to identify peak-hour consumption patterns and seasonal variations. Furthermore, generative AI can consider market trends such as energy price fluctuations and policy changes to generate optimal investment plans. For instance, the supply department can propose investment recommendations for renewable energy sources such as solar power, wind power, and biomass power. Thus, the supply department can provide companies with optimal renewable energy investment solutions. Some or all of the above processing by the supply department can be achieved through AI, or it can be done without AI. For example, the supply department can provide companies with investment plans generated by generative AI, proposing optimal investment plans that consider the company's energy consumption patterns and market trends.

[0052] The Execution Support Department can use generative AI to provide companies with specific steps needed to implement proposed investment plans. For example, generative AI can provide detailed suggestions on what equipment to introduce and what contracts to sign. For instance, the Execution Support Department can specify the steps for introducing solar power equipment and the contract details for wind power projects. Furthermore, the Execution Support Department can provide support on fundraising methods and project schedule management. Thus, the Execution Support Department can provide companies with specific steps for efficiently implementing renewable energy investments. Some or all of the above-mentioned processes in the Execution Support Department can be implemented using AI, or they can be implemented without AI. For example, the Execution Support Department can provide companies with specific steps generated by generative AI to support the efficient implementation of proposed investment plans.

[0053] The data collection unit can adjust the data collection frequency based on specific time periods or seasons when collecting energy consumption data. For example, it can increase the data collection frequency during peak summer months when air conditioning use increases. Conversely, it can decrease the data collection frequency during nighttime hours when electricity consumption is lower. Furthermore, it can reduce the data collection frequency on weekends or holidays compared to normal times. Thus, the data collection unit can obtain more accurate data by adjusting the data collection frequency according to specific time periods or seasons. Some or all of the above processing by the data collection unit can be implemented using AI, or it can be done without AI. For example, the data collection unit can allow generative AI to perform data collection frequency optimization based on specific time periods or seasons.

[0054] The data collection unit can be equipped with the function of detecting abnormal consumption patterns and issuing alarms when collecting energy consumption data. For example, it can issue an alarm when a sudden surge in electricity consumption is detected. Similarly, it can issue an alarm when gas consumption increases abnormally. Furthermore, it can issue an alarm when the consumption pattern deviates significantly from normal. Thus, the data collection unit can respond quickly by detecting abnormal consumption patterns and issuing alarms. Some or all of the above processing in the data collection unit can be implemented using AI, or it can be done without AI. For example, the data collection unit can use generative AI to perform the detection of abnormal consumption patterns and the issuance of alarms.

[0055] The data collection department can incorporate geographic location information when collecting energy consumption data. For example, it can collect energy consumption data for different regions based on factory location information. Furthermore, it can compare energy consumption data between urban and suburban areas based on office building location information. Moreover, it can analyze energy consumption patterns in different regions based on geographic location information. Thus, the data collection department can analyze regional energy consumption patterns by considering geographic location information during data collection. Some or all of the above processing by the data collection department can be implemented using AI, or it can be done without AI. For example, the data collection department can input geographic location information into generative AI, which will then perform location-based data collection.

[0056] The data collection unit can integrate data from other IoT devices while collecting energy consumption data. For example, it can integrate data from lighting system IoT devices, HVAC system IoT devices, and security system IoT devices. Thus, by integrating data from other IoT devices, the collection unit can achieve more comprehensive data collection. Some or all of the processing in the collection unit can be implemented using AI, or it can be done without AI. For example, the collection unit can input data from other IoT devices into generative AI, which will then perform the data integration and collection.

[0057] The data storage unit can determine the storage priority based on the importance of the data during storage. For example, it can prioritize storing important energy consumption data. Less important data can be stored later. Furthermore, highly important data can be stored immediately, while less important data can be stored later. Thus, the storage unit can prioritize storing important data by determining its storage priority based on its importance. Some or all of the above processing in the storage unit can be implemented using AI, or it can be done without AI. For example, the storage unit can input the importance of the data into a generative AI, which will then determine the storage priority based on importance.

[0058] The data storage department can use different storage formats based on the type of data when saving data. For example, electricity consumption data can be saved in CSV format. Gas consumption data can be saved in JSON format. Furthermore, temperature data can be saved in XML format. Thus, by using different storage formats based on the type of data, the storage department can facilitate data management. Some or all of the above processing in the storage department can be implemented using AI, or it can be done without AI. For example, the storage department can input the data type into a generative AI, which will then perform the selection of storage formats according to the type.

[0059] The storage unit can determine the storage priority based on the data's submission time when saving data. For example, it can prioritize saving the most recently submitted data. Furthermore, earlier data can be saved later. Even further, data with more recent submission times can be saved immediately. Thus, the storage unit can prioritize saving data with more recent submission times by determining the storage priority based on the data's submission time. Some or all of the above processing by the storage unit can be implemented using AI, or it can be done without AI. For example, the storage unit can input the data's submission time into a generative AI, which will then determine the storage priority based on the submission time.

[0060] The data storage unit can adjust the storage order based on the relevance of the data during storage. For example, it can prioritize storing data with high relevance, while deferred storing data with low relevance. Furthermore, it can analyze the data relevance to determine the optimal storage order. Thus, the storage unit can prioritize storing highly relevant data by adjusting the storage order based on data relevance. Some or all of the above processing by the storage unit can be implemented using AI, or it can be done without AI. For example, the storage unit can input the data relevance into a generative AI, which will then adjust the storage order based on relevance.

[0061] The analysis unit can predict current data by referring to past data during analysis. For example, it can predict current consumption based on past electricity consumption data. Furthermore, it can predict current consumption based on past gas consumption data. Moreover, it can predict current temperature based on past temperature data. Thus, by referring to past data, the analysis unit can predict current data more accurately. Some or all of the above processing by the analysis unit can be implemented using AI, or it can be implemented without AI. For example, the analysis unit can input past data into a generative AI, which can then perform the prediction of current data based on the past data.

[0062] The parsing unit can employ different parsing algorithms based on the data category during parsing. For example, it can use time-series analysis algorithms for electricity consumption data, regression analysis algorithms for gas consumption data, and clustering algorithms for temperature data. Thus, the parsing unit can improve parsing accuracy by using the optimal parsing algorithm based on the data category. Some or all of the above processing by the parsing unit can be implemented using AI, or it can be done without AI. For example, the parsing unit can input the data categories into a generative AI, which will then execute different parsing algorithms according to the category.

[0063] The parsing unit can determine the parsing priority based on the data's submission time during parsing. For example, it can prioritize parsing the most recently submitted data. Furthermore, earlier data can be parsed later. Moreover, data with more recent submission times can be parsed immediately. Thus, the parsing unit can prioritize parsing data with more recent submission times by determining the parsing priority based on the data's submission time. Some or all of the above processing by the parsing unit can be implemented using AI, or it can be done without AI. For example, the parsing unit can input the data's submission time into a generative AI, which then determines the parsing priority based on the submission time.

[0064] The parsing unit can adjust the parsing order based on the relevance of the data during parsing. For example, it can prioritize parsing data with high relevance, while deferring the parsing of data with low relevance. Furthermore, it can analyze the data's relevance to determine the optimal parsing order. Thus, the parsing unit can prioritize parsing highly relevant data by adjusting the parsing order based on its relevance. Some or all of the above processing by the parsing unit can be implemented using AI, or it can be done without AI. For example, the parsing unit can input the data's relevance into a generative AI, which then adjusts the parsing order based on the relevance.

[0065] The import unit can update the latest energy trends in real time during the import process. For example, it can import the latest solar power generation technology information in real time. Furthermore, it can also import the latest wind power generation technology information in real time. Additionally, it can import the latest biomass energy technology information in real time. Thus, the import unit can always import the latest information by updating the latest energy trends in real time. Some or all of the above processing in the import unit can be implemented using AI, or it can be done without AI. For example, the import unit can input the latest energy trend information into a generative AI, which will then perform real-time information updates.

[0066] The import unit can assess the reliability of investment target information during the import process and prioritize the import of highly reliable information. For example, the import unit can assess the reliability of investment target information during import and prioritize the import of highly reliable information. Furthermore, information with low reliability can be imported later. Further, the reliability of investment target information can be assessed to import the optimal information. Thus, the import unit can prioritize the import of highly reliable information by assessing its reliability. Some or all of the above-described processing in the import unit can be implemented using AI, or it can be implemented without AI. For example, the import unit can input the reliability of the investment target information into a generative AI, which will then determine the information priority based on reliability.

[0067] The import department can incorporate geographic location information into the import process. For example, it can import energy trend information for various regions based on the location information of a factory. Furthermore, it can compare energy trend information between urban and suburban areas based on the location information of office buildings. Moreover, it can analyze energy trends in different regions based on geographic location information. Thus, the import department can analyze regional energy trends by incorporating geographic location information into the import process. Some or all of the above-mentioned processing in the import department can be implemented using AI, or it can be done without AI. For example, the import department can input geographic location information into a generative AI, which will then perform location-based information import.

[0068] The import unit can integrate information from other data sources during the import process. For example, it can integrate information from lighting system data sources, HVAC system data sources, and security system data sources. Thus, by integrating information from other data sources, the import unit can achieve more comprehensive information collection. Some or all of the above processing in the import unit can be implemented using AI, or it can be done without AI. For example, the import unit can input information from other data sources into a generative AI, which will then perform the information integration and import.

[0069] The supply department can adjust the level of detail in investment plans based on a company's energy consumption patterns during the provision process. For example, it can provide detailed investment plans for companies with high energy consumption and concise plans for companies with low energy consumption. Furthermore, it can analyze energy consumption patterns to provide investment plans with optimal detail. Thus, the supply department can provide more suitable investment plans by adjusting the level of detail based on a company's energy consumption patterns. Some or all of the above processing by the supply department can be implemented using AI, or it can be done without AI. For example, the supply department can input the company's energy consumption patterns into a generative AI, which can then adjust the level of detail in the investment plan based on the consumption patterns.

[0070] The investment department can propose different investment plans based on the industry and size of the enterprise. For example, a manufacturing company could propose a solar power investment plan, while a service company could propose a wind power investment plan. Furthermore, suitable investment plans can be proposed based on the company's size. Thus, the investment department can provide more suitable investment plans by proposing different plans based on the enterprise's industry and size. Some or all of the above processing by the investment department can be implemented using AI, or it can be done without AI. For example, the investment department can input the enterprise's industry and size into a generative AI, which will then propose investment plans based on the industry and size.

[0071] The provisioning department can determine the priority of investment proposals based on their submission timing. For example, it can prioritize investment proposals with more recent submission times. Conversely, investment proposals with more distant submission times can be provided later. Furthermore, the optimal provisioning order can be determined based on the submission timing. Thus, the provisioning department can prioritize investment proposals with more recent submission times by determining their provisioning priority based on their submission timing. Some or all of the above-mentioned processing by the provisioning department can be implemented using AI, or it can be done without AI. For example, the provisioning department can input the submission timing of investment proposals into a generative AI, which will then perform the priority determination based on the submission timing.

[0072] The supply department can adjust the order of offering investment options based on their relevance. For example, it can prioritize offering highly relevant investment options, while delaying the offering of less relevant ones. Furthermore, the relevance of investment options can be analyzed to determine the optimal supply order. Thus, the supply department can prioritize offering highly relevant investment options by adjusting the supply order based on their relevance. Some or all of the above processing by the supply department can be implemented using AI, or it can be done without AI. For example, the supply department can input the relevance of investment options into a generative AI, which will then adjust the supply order based on relevance.

[0073] The Execution Support Department can select the optimal support method by referring to the company's past investment history when providing support. For example, it can select the optimal support method based on past successful investments, or select a risk-mitigation support method based on past unsuccessful investments. Furthermore, it can analyze past investment history to select the optimal support method. Thus, the Execution Support Department can select the optimal support method by referring to the company's past investment history. Some or all of the above-mentioned processing by the Execution Support Department can be implemented using AI, or it can be done without AI. For example, the Execution Support Department can input the company's past investment history into a generative AI, which will then select the optimal support method based on the history.

[0074] The Execution Support Department can customize support measures based on the company's current financial situation when providing support. For example, it can tailor support measures based on the company's current financial condition. For instance, it can provide aggressive investment support to companies with sound financial conditions, while offering lower-risk investment support to companies with strained financial situations. Furthermore, it can analyze the financial situation to customize the optimal support measures. Thus, the Execution Support Department can achieve more appropriate support by customizing support measures based on the company's financial condition. Some or all of the above-mentioned processes in the Execution Support Department can be implemented using AI, or they can be performed without AI. For example, the Execution Support Department can input the company's financial situation into a generative AI, which will then execute the customized support measures based on the financial situation.

[0075] The Execution Support Department can select the optimal support method by considering the company's geographical location information when providing support. For example, it can select the optimal support method for each region based on the factory's location information. Furthermore, it can compare the optimal support methods for urban and suburban areas based on the office building's location information. Moreover, it can analyze the optimal support methods for different regions based on geographical location information. Thus, the Execution Support Department can provide the optimal support method for each region by considering the company's geographical location information. Some or all of the above processing by the Execution Support Department can be implemented using AI, or it can be done without AI. For example, the Execution Support Department can input the company's geographical location information into a generative AI, which will then select the optimal support method based on the location information.

[0076] The Execution Support Department can analyze a company's social media activities and propose support measures during the execution of support tasks. For example, it can analyze the company's social media activities to propose optimal support measures based on the company's reputation on social media. It can also analyze the company's activities on social media to customize support measures. Furthermore, it can consider the company's influence on social media to propose support measures. Thus, the Execution Support Department can propose optimal support measures by analyzing the company's social media activities. Some or all of the above processing by the Execution Support Department can be implemented using AI, or it can be done without AI. For example, the Execution Support Department can input the company's social media activities into a generative AI, which will then propose support measures based on the activities.

[0077] The system described in this embodiment is not limited to the examples above. For example, various modifications can be made as follows.

[0078] The analytics department can compare energy consumption data with a company's historical energy consumption data to detect anomalies. For example, a sharp increase in consumption compared to past data can be detected as an anomaly and an alert can be issued. Furthermore, significant changes in consumption patterns compared to past data can also be detected as anomalies. Based on the anomaly detection results, specific countermeasures can be provided to the company. Thus, the analytics department can detect anomalies by comparing a company's energy consumption data with historical data, supporting the company's rapid response.

[0079] The data collection department can consider the company's production plans and operational status when collecting energy consumption data, adjusting the timing of data collection accordingly. For example, it can increase the frequency of data collection during peak production periods and decrease it during periods of lower activity. Furthermore, data collection for a specific production line can be temporarily suspended when that line is stopped. Moreover, the optimal timing for data collection can be automatically adjusted based on the company's production plan. Thus, the data collection department can optimize data collection timing according to the company's production plans and operational status.

[0080] The analytics department can link energy consumption data with a company's financial data to assess cost efficiency. For example, by combining energy consumption and financial data, the effectiveness of energy cost reductions can be evaluated. Furthermore, energy efficiency can be compared and analyzed with financial indicators. Moreover, the impact of improved energy consumption on the company's financial situation can be predicted. Therefore, by linking energy consumption and financial data, the analytics department can assess a company's cost efficiency and propose improvement suggestions.

[0081] The data import department can consider a company's environmental and CSR (Corporate Social Responsibility) goals when importing energy consumption data. For example, based on the company's CO2 emission reduction targets, relevant data can be imported first. Furthermore, by importing data related to the company's CSR activities, the achievement of environmental goals can be assessed. Moreover, the data import method can be optimized based on the company's environmental goals. Thus, the data import department can import data according to the company's environmental and CSR goals, supporting the achievement of these goals.

[0082] The provision department can tailor the content provided with energy consumption data based on the skill levels of the company's employees. For example, detailed technical information can be provided to employees with expertise in energy management, while basic information can be offered to general employees. Furthermore, the format of the information can be adjusted according to the employees' skill levels. Moreover, training or educational programs can be proposed based on the employees' skill levels. Thus, the provision department can customize content according to the skill levels of the company's employees, achieving efficient information delivery.

[0083] The following is a brief description of the processing flow of Implementation Method 1.

[0084] Step 1: The data collection department collects the company's energy consumption data. For example, by using IoT devices installed in factories or office buildings, data such as electricity consumption and gas consumption can be collected in real time.

[0085] Step 2: The storage department saves the data collected by the collection department to the cloud. For example, the collected data is stored in cloud services such as AWS, Google Cloud, and Microsoft Azure.

[0086] Step 3: The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud. For example, generative AI uses technologies such as deep learning and neural networks to analyze energy consumption data.

[0087] Step 4: The import department imports the latest energy trends and investment opportunities using generative AI. For example, it imports information such as renewable energy price trends and the status of new technology introductions.

[0088] Step 5: The department uses generative AI to consider the company's energy consumption patterns and market trends to provide optimal renewable energy investment solutions. For example, it proposes investment options for solar power generation and wind power generation.

[0089] Step 6: The Execution Support Department uses generative AI to provide the company with specific steps needed to implement the proposed investment plan. For example, it provides detailed suggestions on what equipment to introduce and what contracts to sign.

[0090] Implementation Method 2

[0091] The energy consumption optimization system described in this invention is used to optimize enterprise energy consumption and support renewable energy investment. This system continuously monitors an enterprise's energy consumption through IoT devices and saves the data to the cloud. Subsequently, generative AI, based on this information and combined with the latest energy trends and investment target information, provides the enterprise with the optimal renewable energy investment plan. This enables enterprises to efficiently reduce CO2 emissions and achieve a transition to a sustainable business model. For example, IoT devices installed in factories or office buildings can collect data such as electricity and gas consumption in real time. This data is saved to the cloud for subsequent analysis by generative AI. The generative AI analyzes the energy consumption data saved in the cloud, imports the latest energy trends and investment target information, and generates the most suitable renewable energy investment plan for the enterprise. For example, it proposes investment recommendations for renewable energy sources such as solar power and wind power. This recommendation takes into account the enterprise's energy consumption patterns and market trends. Furthermore, the generative AI also provides the enterprise with specific steps required to implement the proposed investment plan. For example, it provides detailed suggestions on what equipment to introduce and what contracts to sign. Thus, enterprises can efficiently invest in renewable energy and achieve CO2 emission reduction. Through this system, enterprises can obtain support for transitioning to a sustainable business model. For example, it is expected to reduce energy costs and environmental impact, thereby enhancing corporate competitiveness. Furthermore, through renewable energy investments, companies can fulfill their social responsibility (CSR) obligations. Therefore, energy consumption optimization systems can optimize a company's energy consumption and support renewable energy investments.

[0092] The energy consumption optimization system described in this embodiment includes a collection unit, a storage unit, an analysis unit, an import unit, a provision unit, and an execution support unit. The collection unit collects energy consumption data from the enterprise. For example, it collects data such as electricity consumption and gas consumption in real time through IoT devices installed in factories or office buildings. The storage unit stores the data collected by the collection unit in the cloud. For example, the collected data is stored in cloud services such as AWS, Google Cloud, and Microsoft Azure. The analysis unit analyzes the energy consumption data stored in the cloud using generative AI. For example, generative AI uses technologies such as deep learning and neural networks to analyze energy consumption data. The import unit imports the latest energy trends and investment target information using generative AI. For example, it imports information such as renewable energy price trends and the status of new technology introductions. The provision unit, using generative AI, considers the enterprise's energy consumption patterns and market trends to provide optimal renewable energy investment solutions. For example, it proposes investment solutions such as solar power generation and wind power generation. The execution support unit, using generative AI, provides the enterprise with specific steps required to implement the proposed investment solutions. For example, it provides detailed suggestions on what equipment should be introduced and what contracts should be signed. Therefore, the energy consumption optimization system involved in this embodiment can optimize the energy consumption of enterprises and support renewable energy investment.

[0093] The data collection unit can gather energy consumption data in real time through IoT devices installed in factories or office buildings. For example, the collection unit can utilize IoT devices such as smart meters, sensors, and gateways to collect energy consumption data in the factory or office building in real time. Smart meters can measure electricity consumption in real time and send the data to the collection unit. Sensors can measure gas consumption or temperature data and send the data to the collection unit. Furthermore, gateways can integrate data from multiple IoT devices and send it to the collection unit. Thus, the collection unit can collect energy consumption data from the factory or office building in real time. Some or all of the processing in the collection unit can be implemented using AI, or it can be done without AI. For example, the collection unit can input data obtained from IoT devices into generative AI to optimize the timing and frequency of data collection.

[0094] The analysis unit can analyze energy consumption data stored in the cloud using generative AI. For example, the analysis unit can utilize generative AI technologies such as deep learning and neural networks to analyze energy consumption data stored in the cloud. Deep learning, for instance, is a technique that builds neural networks with advanced pattern recognition capabilities by learning from large amounts of data. Neural networks are networks composed of multiple layers of artificial neurons, capable of processing input data and generating outputs. The analysis unit can utilize these technologies to improve the accuracy of energy consumption data analysis. For example, the analysis unit can use a generative AI model that takes energy consumption data as input and outputs consumption patterns or anomaly detection. Thus, the analysis unit can analyze energy consumption data stored in the cloud using generative AI. Some or all of the processing described above by the analysis unit can be implemented using AI, or AI may not be used.

[0095] The import department can import the latest energy trends and investment target information through generative AI. For example, generative AI can automatically collect information such as renewable energy price trends and the status of new technology introductions, and provide it to the import department. Based on this information, the import department generates optimal renewable energy investment plans for companies. For example, generative AI can collect the latest energy trends from news reports, academic papers, government reports, etc. on the internet. Furthermore, generative AI can also collect investment target information such as the company's financial situation and project progress, and provide it to the import department. Thus, the import department can efficiently import the latest energy trends and investment target information through generative AI. Some or all of the above-mentioned processes in the import department can be implemented using AI, or AI can be used without it.

[0096] The supply department can utilize generative AI to consider a company's energy consumption patterns and market trends, providing renewable energy investment solutions. For example, generative AI can analyze a company's energy consumption data to identify peak-hour consumption patterns and seasonal variations. Furthermore, generative AI can consider market trends such as energy price fluctuations and policy changes to generate optimal investment plans. For instance, the supply department can propose investment recommendations for renewable energy sources such as solar power, wind power, and biomass power. Thus, the supply department can provide companies with optimal renewable energy investment solutions. Some or all of the above processing by the supply department can be achieved through AI, or it can be done without AI. For example, the supply department can provide companies with investment plans generated by generative AI, proposing optimal investment plans that consider the company's energy consumption patterns and market trends.

[0097] The Execution Support Department can use generative AI to provide companies with specific steps needed to implement proposed investment plans. For example, generative AI can provide detailed suggestions on what equipment to introduce and what contracts to sign. For instance, the Execution Support Department can specify the steps for introducing solar power equipment and the contract details for wind power projects. Furthermore, the Execution Support Department can provide support on fundraising methods and project schedule management. Thus, the Execution Support Department can provide companies with specific steps for efficiently implementing renewable energy investments. Some or all of the above-mentioned processes in the Execution Support Department can be implemented using AI, or they can be implemented without AI. For example, the Execution Support Department can provide companies with specific steps generated by generative AI to support the efficient implementation of proposed investment plans.

[0098] The data collection unit can infer user emotions and adjust the timing of energy consumption data collection based on these inferred emotions. For example, when a user is stressed, the collection frequency can be reduced. When a user is relaxed, the collection frequency can be increased. Furthermore, when a user is anxious, the collection timing can be optimized to quickly collect the minimum necessary data. Thus, the collection unit can adjust the data collection timing according to user emotions, achieving more appropriate data collection. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing by the collection unit can be implemented using AI, or AI can be omitted. For example, the collection unit can input user emotion data into generative AI, which can then perform emotion-based data collection timing optimization.

[0099] The data collection unit can adjust the data collection frequency based on specific time periods or seasons when collecting energy consumption data. For example, it can increase the data collection frequency during peak summer months when air conditioning use increases. Conversely, it can decrease the data collection frequency during nighttime hours when electricity consumption is lower. Furthermore, it can reduce the data collection frequency on weekends or holidays compared to normal times. Thus, the data collection unit can obtain more accurate data by adjusting the data collection frequency according to specific time periods or seasons. Some or all of the above processing by the data collection unit can be implemented using AI, or it can be done without AI. For example, the data collection unit can allow generative AI to perform data collection frequency optimization based on specific time periods or seasons.

[0100] The data collection unit can be equipped with the function of detecting abnormal consumption patterns and issuing alarms when collecting energy consumption data. For example, it can issue an alarm when a sudden surge in electricity consumption is detected. Similarly, it can issue an alarm when gas consumption increases abnormally. Furthermore, it can issue an alarm when the consumption pattern deviates significantly from normal. Thus, the data collection unit can respond quickly by detecting abnormal consumption patterns and issuing alarms. Some or all of the above processing in the data collection unit can be implemented using AI, or it can be done without AI. For example, the data collection unit can use generative AI to perform the detection of abnormal consumption patterns and the issuance of alarms.

[0101] The data collection department can infer user emotions and determine the priority of data collection based on these inferred emotions. For example, when a user is stressed, important data can be collected first. When a user is relaxed, detailed data can be collected first. Furthermore, when a user is anxious, data that can be collected quickly can be collected first. Thus, the collection department can determine data priority based on user emotions, prioritizing the collection of important data. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing in the collection department can be implemented using AI, or AI can be used without it. For example, the collection department can input user emotion data into generative AI, which will then perform emotion-based data prioritization.

[0102] The data collection department can incorporate geographic location information when collecting energy consumption data. For example, it can collect energy consumption data for different regions based on factory location information. Furthermore, it can compare energy consumption data between urban and suburban areas based on office building location information. Moreover, it can analyze energy consumption patterns in different regions based on geographic location information. Thus, the data collection department can analyze regional energy consumption patterns by considering geographic location information during data collection. Some or all of the above processing by the data collection department can be implemented using AI, or it can be done without AI. For example, the data collection department can input geographic location information into generative AI, which will then perform location-based data collection.

[0103] The data collection unit can integrate data from other IoT devices while collecting energy consumption data. For example, it can integrate data from lighting system IoT devices, HVAC system IoT devices, and security system IoT devices. Thus, by integrating data from other IoT devices, the collection unit can achieve more comprehensive data collection. Some or all of the processing in the collection unit can be implemented using AI, or it can be done without AI. For example, the collection unit can input data from other IoT devices into generative AI, which will then perform the data integration and collection.

[0104] The data storage unit can infer user emotions and adjust the data storage method based on the inferred emotions. For example, it can infer user emotions and adjust the data storage method accordingly. For instance, when a user is stressed, a simple storage method can be provided. When the user is relaxed, detailed storage options can be provided. Furthermore, when the user is anxious, a quick storage method can be provided. Thus, the storage unit can adjust the data storage method according to the user's emotions, achieving more appropriate data storage. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing in the storage unit can be implemented using AI, or AI can be omitted. For example, the storage unit can input user emotion data into generative AI, which will then perform emotion-based adjustments to the data storage method.

[0105] The data storage unit can determine the storage priority based on the importance of the data during storage. For example, it can prioritize storing important energy consumption data. Less important data can be stored later. Furthermore, highly important data can be stored immediately, while less important data can be stored later. Thus, the storage unit can prioritize storing important data by determining its storage priority based on its importance. Some or all of the above processing in the storage unit can be implemented using AI, or it can be done without AI. For example, the storage unit can input the importance of the data into a generative AI, which will then determine the storage priority based on importance.

[0106] The data storage department can use different storage formats based on the type of data when saving data. For example, electricity consumption data can be saved in CSV format. Gas consumption data can be saved in JSON format. Furthermore, temperature data can be saved in XML format. Thus, by using different storage formats based on the type of data, the storage department can facilitate data management. Some or all of the above processing in the storage department can be implemented using AI, or it can be done without AI. For example, the storage department can input the data type into a generative AI, which will then perform the selection of storage formats according to the type.

[0107] The data retention department can infer user emotions and adjust the data retention period based on the inferred emotions. For example, it can infer user emotions and adjust the data retention period accordingly. For instance, when a user is stressed, short-term retention can be suggested. When a user is relaxed, long-term retention can be suggested. Furthermore, when a user is anxious, the retention period can be optimized. Thus, the retention department can adjust the data retention period according to user emotions, achieving more appropriate data retention. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing by the retention department can be implemented using AI, or AI can be omitted. For example, the retention department can input user emotion data into generative AI, which will then perform emotion-based data retention period adjustments.

[0108] The storage unit can determine the storage priority based on the data's submission time when saving data. For example, it can prioritize saving the most recently submitted data. Furthermore, earlier data can be saved later. Even further, data with more recent submission times can be saved immediately. Thus, the storage unit can prioritize saving data with more recent submission times by determining the storage priority based on the data's submission time. Some or all of the above processing by the storage unit can be implemented using AI, or it can be done without AI. For example, the storage unit can input the data's submission time into a generative AI, which will then determine the storage priority based on the submission time.

[0109] The data storage unit can adjust the storage order based on the relevance of the data during storage. For example, it can prioritize storing data with high relevance, while deferred storing data with low relevance. Furthermore, it can analyze the data relevance to determine the optimal storage order. Thus, the storage unit can prioritize storing highly relevant data by adjusting the storage order based on data relevance. Some or all of the above processing by the storage unit can be implemented using AI, or it can be done without AI. For example, the storage unit can input the data relevance into a generative AI, which will then adjust the storage order based on relevance.

[0110] The parsing unit can infer user emotions and adjust the parsing method based on the inferred emotions. For example, it can infer user emotions and adjust the parsing method accordingly. For instance, when a user is stressed, a simple parsing method can be provided. When the user is relaxed, a detailed parsing method can be provided. Furthermore, when the user is anxious, a method that allows for rapid parsing can be provided. Thus, the parsing unit can adjust the parsing method according to the user's emotions to achieve more appropriate data parsing. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the processing in the parsing unit can be implemented using AI, or AI can be omitted. For example, the parsing unit can input user emotion data into generative AI, which will then perform emotion-based parsing method adjustments.

[0111] The analysis unit can predict current data by referring to past data during analysis. For example, it can predict current consumption based on past electricity consumption data. Furthermore, it can predict current consumption based on past gas consumption data. Moreover, it can predict current temperature based on past temperature data. Thus, by referring to past data, the analysis unit can predict current data more accurately. Some or all of the above processing by the analysis unit can be implemented using AI, or it can be implemented without AI. For example, the analysis unit can input past data into a generative AI, which can then perform the prediction of current data based on the past data.

[0112] The parsing unit can employ different parsing algorithms based on the data category during parsing. For example, it can use time-series analysis algorithms for electricity consumption data, regression analysis algorithms for gas consumption data, and clustering algorithms for temperature data. Thus, the parsing unit can improve parsing accuracy by using the optimal parsing algorithm based on the data category. Some or all of the above processing by the parsing unit can be implemented using AI, or it can be done without AI. For example, the parsing unit can input the data categories into a generative AI, which will then execute different parsing algorithms according to the category.

[0113] The parsing unit can infer user emotions and determine parsing priorities based on these inferred emotions. For example, when a user is stressed, important data is parsed first. When a user is relaxed, detailed data is parsed first. Furthermore, when a user is anxious, data that can be parsed quickly is parsed first. Thus, the parsing unit can determine parsing priorities based on user emotions, prioritizing important data. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the processing in the parsing unit can be implemented using AI, or AI can be omitted. For example, the parsing unit can input user emotion data into generative AI, which then performs emotion-based parsing priority determination.

[0114] The parsing unit can determine the parsing priority based on the data's submission time during parsing. For example, it can prioritize parsing the most recently submitted data. Furthermore, earlier data can be parsed later. Moreover, data with more recent submission times can be parsed immediately. Thus, the parsing unit can prioritize parsing data with more recent submission times by determining the parsing priority based on the data's submission time. Some or all of the above processing by the parsing unit can be implemented using AI, or it can be done without AI. For example, the parsing unit can input the data's submission time into a generative AI, which then determines the parsing priority based on the submission time.

[0115] The parsing unit can adjust the parsing order based on the relevance of the data during parsing. For example, it can prioritize parsing data with high relevance, while deferring the parsing of data with low relevance. Furthermore, it can analyze the data's relevance to determine the optimal parsing order. Thus, the parsing unit can prioritize parsing highly relevant data by adjusting the parsing order based on its relevance. Some or all of the above processing by the parsing unit can be implemented using AI, or it can be done without AI. For example, the parsing unit can input the data's relevance into a generative AI, which then adjusts the parsing order based on the relevance.

[0116] The import unit can infer user emotions and determine the priority of imported information based on these inferred emotions. For example, when a user is stressed, important information can be prioritized. When a user is relaxed, detailed information can be prioritized. Furthermore, when a user is anxious, information that can be quickly imported can be prioritized. Thus, the import unit can determine information priority based on user emotions, prioritizing the import of important information. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing in the import unit can be implemented using AI, or AI can be omitted. For example, the import unit can input user emotion data into generative AI, which will then perform emotion-based information prioritization.

[0117] The import unit can update the latest energy trends in real time during the import process. For example, it can import the latest solar power generation technology information in real time. Furthermore, it can also import the latest wind power generation technology information in real time. Additionally, it can import the latest biomass energy technology information in real time. Thus, the import unit can always import the latest information by updating the latest energy trends in real time. Some or all of the above processing in the import unit can be implemented using AI, or it can be done without AI. For example, the import unit can input the latest energy trend information into a generative AI, which will then perform real-time information updates.

[0118] The import unit can assess the reliability of investment target information during the import process and prioritize the import of highly reliable information. For example, the import unit can assess the reliability of investment target information during import and prioritize the import of highly reliable information. Furthermore, information with low reliability can be imported later. Further, the reliability of investment target information can be assessed to import the optimal information. Thus, the import unit can prioritize the import of highly reliable information by assessing its reliability. Some or all of the above-described processing in the import unit can be implemented using AI, or it can be implemented without AI. For example, the import unit can input the reliability of the investment target information into a generative AI, which will then determine the information priority based on reliability.

[0119] The import department can infer user emotions and adjust the display of imported information based on these inferred emotions. For example, it can infer user emotions and adjust the display of imported information accordingly. For instance, when a user is stressed, a concise display can be provided. When the user is relaxed, a detailed display can be provided. Furthermore, when the user is anxious, a quick display method can be provided. Thus, the import department can adjust the information display according to the user's emotions, achieving more appropriate information display. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing in the import department can be implemented using AI, or AI can be omitted. For example, the import department can input user emotion data into generative AI, which will then perform emotion-based adjustments to the information display.

[0120] The import department can incorporate geographic location information into the import process. For example, it can import energy trend information for various regions based on the location information of a factory. Furthermore, it can compare energy trend information between urban and suburban areas based on the location information of office buildings. Moreover, it can analyze energy trends in different regions based on geographic location information. Thus, the import department can analyze regional energy trends by incorporating geographic location information into the import process. Some or all of the above-mentioned processing in the import department can be implemented using AI, or it can be done without AI. For example, the import department can input geographic location information into a generative AI, which will then perform location-based information import.

[0121] The import unit can integrate information from other data sources during the import process. For example, it can integrate information from lighting system data sources, HVAC system data sources, and security system data sources. Thus, by integrating information from other data sources, the import unit can achieve more comprehensive information collection. Some or all of the above processing in the import unit can be implemented using AI, or it can be done without AI. For example, the import unit can input information from other data sources into a generative AI, which will then perform the information integration and import.

[0122] The offering department can infer user emotions and adjust the expression of the offered investment solutions based on the inferred user emotions. For example, the offering department can infer user emotions and adjust the expression of the offered investment solutions based on the inferred user emotions. For instance, when a user is stressed, a concise and easy-to-understand expression can be provided. When a user is relaxed, an expression containing detailed information can be provided. Furthermore, when a user is anxious, an expression that can be quickly understood can be provided. Thus, the offering department can adjust the expression of the investment solutions according to user emotions, providing more suitable investment solutions. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing by the offering department can be implemented through AI, or AI can be used without it. For example, the offering department can input user emotion data into generative AI, which will then perform emotion-based adjustments to the expression of the investment solutions.

[0123] The supply department can adjust the level of detail in investment plans based on a company's energy consumption patterns during the provision process. For example, it can provide detailed investment plans for companies with high energy consumption and concise plans for companies with low energy consumption. Furthermore, it can analyze energy consumption patterns to provide investment plans with optimal detail. Thus, the supply department can provide more suitable investment plans by adjusting the level of detail based on a company's energy consumption patterns. Some or all of the above processing by the supply department can be implemented using AI, or it can be done without AI. For example, the supply department can input the company's energy consumption patterns into a generative AI, which can then adjust the level of detail in the investment plan based on the consumption patterns.

[0124] The investment department can propose different investment plans based on the industry and size of the enterprise. For example, a manufacturing company could propose a solar power investment plan, while a service company could propose a wind power investment plan. Furthermore, suitable investment plans can be proposed based on the company's size. Thus, the investment department can provide more suitable investment plans by proposing different plans based on the enterprise's industry and size. Some or all of the above processing by the investment department can be implemented using AI, or it can be done without AI. For example, the investment department can input the enterprise's industry and size into a generative AI, which will then propose investment plans based on the industry and size.

[0125] The offering department can infer user emotions and adjust the length of the offered investment plans based on the inferred emotions. For example, it can infer user emotions and adjust the length of the offered investment plans accordingly. For instance, when a user is stressed, a short and concise investment plan can be provided. When a user is relaxed, a longer investment plan with detailed explanations can be provided. Furthermore, when a user is anxious, a short investment plan that can be quickly understood can be provided. Thus, the offering department can adjust the length of the investment plan according to the user's emotions, providing a more suitable investment plan. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing by the offering department can be implemented using AI, or it can be done without AI. For example, the offering department can input user emotion data into generative AI, which will then perform emotion-based adjustment of the investment plan length.

[0126] The provisioning department can determine the priority of investment proposals based on their submission timing. For example, it can prioritize investment proposals with more recent submission times. Conversely, investment proposals with more distant submission times can be provided later. Furthermore, the optimal provisioning order can be determined based on the submission timing. Thus, the provisioning department can prioritize investment proposals with more recent submission times by determining their provisioning priority based on their submission timing. Some or all of the above-mentioned processing by the provisioning department can be implemented using AI, or it can be done without AI. For example, the provisioning department can input the submission timing of investment proposals into a generative AI, which will then perform the priority determination based on the submission timing.

[0127] The supply department can adjust the order of offering investment options based on their relevance. For example, it can prioritize offering highly relevant investment options, while delaying the offering of less relevant ones. Furthermore, the relevance of investment options can be analyzed to determine the optimal supply order. Thus, the supply department can prioritize offering highly relevant investment options by adjusting the supply order based on their relevance. Some or all of the above processing by the supply department can be implemented using AI, or it can be done without AI. For example, the supply department can input the relevance of investment options into a generative AI, which will then adjust the supply order based on relevance.

[0128] The Execution Support Department can infer user emotions and adjust the execution support methods based on the inferred emotions. For example, it can infer user emotions and adjust the execution support methods accordingly. For instance, when a user is stressed, simple execution support methods can be provided. When the user is relaxed, detailed execution support methods can be provided. Furthermore, when the user is anxious, methods that can be executed quickly can be provided. Thus, the Execution Support Department can adjust the execution support methods according to the user's emotions to achieve more appropriate support. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing by the Execution Support Department can be implemented through AI, or AI can be omitted. For example, the Execution Support Department can input user emotion data into generative AI, which will then adjust the execution support methods based on emotions.

[0129] The Execution Support Department can select the optimal support method by referring to the company's past investment history when providing support. For example, it can select the optimal support method based on past successful investments, or select a risk-mitigation support method based on past unsuccessful investments. Furthermore, it can analyze past investment history to select the optimal support method. Thus, the Execution Support Department can select the optimal support method by referring to the company's past investment history. Some or all of the above-mentioned processing by the Execution Support Department can be implemented using AI, or it can be done without AI. For example, the Execution Support Department can input the company's past investment history into a generative AI, which will then select the optimal support method based on the history.

[0130] The Execution Support Department can customize support measures based on the company's current financial situation when providing support. For example, it can tailor support measures based on the company's current financial condition. For instance, it can provide aggressive investment support to companies with sound financial conditions, while offering lower-risk investment support to companies with strained financial situations. Furthermore, it can analyze the financial situation to customize the optimal support measures. Thus, the Execution Support Department can achieve more appropriate support by customizing support measures based on the company's financial condition. Some or all of the above-mentioned processes in the Execution Support Department can be implemented using AI, or they can be performed without AI. For example, the Execution Support Department can input the company's financial situation into a generative AI, which will then execute the customized support measures based on the financial situation.

[0131] The execution support department can infer user emotions and determine the priority of execution support based on the inferred user emotions. For example, the execution support department can infer user emotions and determine the priority of execution support based on the inferred user emotions. For instance, when a user is stressed, important support can be provided first. When a user is relaxed, detailed support can be provided first. Furthermore, when a user is anxious, support that can be executed quickly can be provided first. Thus, the execution support department can determine the priority of execution support based on user emotions, prioritizing important support. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above-mentioned processing of the execution support department can be implemented by AI, or AI can be not used. For example, the execution support department can input user emotion data into generative AI, which will then perform emotion-based execution support priority determination.

[0132] The Execution Support Department can select the optimal support method by considering the company's geographical location information when providing support. For example, it can select the optimal support method for each region based on the factory's location information. Furthermore, it can compare the optimal support methods for urban and suburban areas based on the office building's location information. Moreover, it can analyze the optimal support methods for different regions based on geographical location information. Thus, the Execution Support Department can provide the optimal support method for each region by considering the company's geographical location information. Some or all of the above processing by the Execution Support Department can be implemented using AI, or it can be done without AI. For example, the Execution Support Department can input the company's geographical location information into a generative AI, which will then select the optimal support method based on the location information.

[0133] The Execution Support Department can analyze a company's social media activities and propose support measures during the execution of support tasks. For example, it can analyze the company's social media activities to propose optimal support measures based on the company's reputation on social media. It can also analyze the company's activities on social media to customize support measures. Furthermore, it can consider the company's influence on social media to propose support measures. Thus, the Execution Support Department can propose optimal support measures by analyzing the company's social media activities. Some or all of the above processing by the Execution Support Department can be implemented using AI, or it can be done without AI. For example, the Execution Support Department can input the company's social media activities into a generative AI, which will then propose support measures based on the activities.

[0134] The system described in this embodiment is not limited to the examples above. For example, various modifications can be made as follows.

[0135] The analytics department can compare energy consumption data with a company's historical energy consumption data to detect anomalies. For example, a sharp increase in consumption compared to past data can be detected as an anomaly and an alert can be issued. Furthermore, significant changes in consumption patterns compared to past data can also be detected as anomalies. Based on the anomaly detection results, specific countermeasures can be provided to the company. Thus, the analytics department can detect anomalies by comparing a company's energy consumption data with historical data, supporting the company's rapid response.

[0136] The data collection department can consider the company's production plans and operational status when collecting energy consumption data, adjusting the timing of data collection accordingly. For example, it can increase the frequency of data collection during peak production periods and decrease it during periods of lower activity. Furthermore, data collection for a specific production line can be temporarily suspended when that line is stopped. Moreover, the optimal timing for data collection can be automatically adjusted based on the company's production plan. Thus, the data collection department can optimize data collection timing according to the company's production plans and operational status.

[0137] The analytics department can link energy consumption data with a company's financial data to assess cost efficiency. For example, by combining energy consumption and financial data, the effectiveness of energy cost reductions can be evaluated. Furthermore, energy efficiency can be compared and analyzed with financial indicators. Moreover, the impact of improved energy consumption on the company's financial situation can be predicted. Therefore, by linking energy consumption and financial data, the analytics department can assess a company's cost efficiency and propose improvement suggestions.

[0138] The data import department can consider a company's environmental and CSR (Corporate Social Responsibility) goals when importing energy consumption data. For example, based on the company's CO2 emission reduction targets, relevant data can be imported first. Furthermore, by importing data related to the company's CSR activities, the achievement of environmental goals can be assessed. Moreover, the data import method can be optimized based on the company's environmental goals. Thus, the data import department can import data according to the company's environmental and CSR goals, supporting the achievement of these goals.

[0139] The provision department can tailor the content provided with energy consumption data based on the skill levels of the company's employees. For example, detailed technical information can be provided to employees with expertise in energy management, while basic information can be offered to general employees. Furthermore, the format of the information can be adjusted according to the employees' skill levels. Moreover, training or educational programs can be proposed based on the employees' skill levels. Thus, the provision department can customize content according to the skill levels of the company's employees, achieving efficient information delivery.

[0140] The Execution Support Department can infer employee sentiment when providing support based on energy consumption data and adjust support methods accordingly. For example, when employees are stressed, simple and quick support methods can be provided. When employees are relaxed, support methods with detailed explanations can be offered. Furthermore, when employees are anxious, support methods that can be implemented quickly can be provided. Thus, the Execution Support Department can adjust support methods based on employee sentiment to provide efficient support.

[0141] The data collection department can infer user emotions while collecting energy consumption data and customize the data collection interface based on these inferred emotions. For example, a simple and intuitive interface is provided when the user is stressed. A detailed configurable interface is provided when the user is relaxed. Furthermore, a quick and easy-to-use interface is provided when the user is anxious. Thus, the data collection department can customize the data collection interface according to user emotions, improving user convenience.

[0142] The analysis unit can infer user emotions when analyzing energy consumption data and adjust the display of analysis results based on the inferred emotions. For example, when a user is stressed, a concise and key-point-highlighting display is provided. When the user is relaxed, detailed analysis results are provided. Furthermore, when the user is anxious, a display that is easy to understand is provided. Thus, the analysis unit can adjust the display of analysis results according to the user's emotions, promoting user comprehension.

[0143] The information delivery department can infer user emotions when providing energy consumption data and adjust the amount of information provided based on the inferred emotions. For example, when a user is stressed, the department provides the minimum necessary information. When a user is relaxed, detailed information can be provided. Furthermore, when a user is anxious, information that can be quickly understood can be provided. Thus, the department can adjust the amount of information provided according to the user's emotions, reducing the user's burden.

[0144] The Execution Support Department can infer user emotions when providing support based on energy consumption data and determine support priorities based on these inferred emotions. For example, when a user is stressed, important support can be prioritized. When a user is relaxed, detailed support can be prioritized. Furthermore, when a user is in a hurry, support that can be executed quickly can be prioritized. Thus, the Execution Support Department can determine support priorities based on user emotions, providing efficient support.

[0145] The following is a brief description of the processing flow of Implementation Method 2.

[0146] Step 1: The data collection department collects the company's energy consumption data. For example, by using IoT devices installed in factories or office buildings, data such as electricity consumption and gas consumption can be collected in real time.

[0147] Step 2: The storage department saves the data collected by the collection department to the cloud. For example, the collected data is stored in cloud services such as AWS, Google Cloud, and Microsoft Azure.

[0148] Step 3: The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud. For example, generative AI uses technologies such as deep learning and neural networks to analyze energy consumption data.

[0149] Step 4: The import department imports the latest energy trends and investment opportunities using generative AI. For example, it imports information such as renewable energy price trends and the status of new technology introductions.

[0150] Step 5: The department uses generative AI to consider the company's energy consumption patterns and market trends to provide optimal renewable energy investment solutions. For example, it proposes investment options for solar power generation and wind power generation.

[0151] Step 6: The Execution Support Department uses generative AI to provide the company with specific steps needed to implement the proposed investment plan. For example, it provides detailed suggestions on what equipment to introduce and what contracts to sign.

[0152] The specific processing unit 290 sends 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 voice representing the user's input to the result of the specific processing. The control unit 46A sends the voice data representing the user's 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.

[0153] Data generation model 58 is what is known as generative AI (Artificial Intelligence). An example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL:https: / / openai.com / blog / chatgpt> Generative AI, such as data generation model 58, is obtained by deep learning through a neural network. The data generation model 58 is input with a prompt containing instructions, and with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data of still images or data of moving images). The data generation model 58 infers the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the above-described specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0154] Furthermore, the processing performed by the aforementioned 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 it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0155] Each of the aforementioned elements—collection, storage, analysis, import, provision, and execution support—can be implemented, for example, through at least one of smart device 14 and data processing device 12. For instance, the collection unit uses the IoT device of smart device 14 to collect the company's energy consumption data in real time. The storage unit saves the collected data to cloud storage in data processing device 12. The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud through a specific processing unit 290 of data processing device 12. The import unit imports the latest energy trends and investment opportunities through the specific processing unit 290 of data processing device 12. The provision unit provides the company with optimal renewable energy investment plans through the specific processing unit 290 of data processing device 12. The execution support unit provides the company with the specific steps required to implement the proposed investment plan through the specific processing unit 290 of data processing device 12. The correspondence between the various units and the devices or control units is not limited to the above examples and can be varied.

[0156] Second Implementation Method

[0157] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0158] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0159] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN and / or LAN, etc.

[0160] 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, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0161] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge-Coupled Device) image sensor, used to capture the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0163] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0164] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0166] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0167] In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 sends 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 voice input representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's 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.

[0170] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0171] The data processing system 210 of the second embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed 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 it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0172] Each of the aforementioned elements—collection, storage, analysis, import, provision, and execution support—can be implemented, for example, through at least one of smart glasses 214 and data processing device 12. For instance, the collection unit uses the IoT device of smart glasses 214 to collect the company's energy consumption data in real time. The storage unit saves the collected data to cloud storage in data processing device 12. The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud through a specific processing unit 290 of data processing device 12. The import unit imports the latest energy trends and investment opportunities through the specific processing unit 290 of data processing device 12. The provision unit provides the company with optimal renewable energy investment plans through the specific processing unit 290 of data processing device 12. The execution support unit provides the company with the specific steps required to implement the proposed investment plan through the specific processing unit 290 of data processing device 12. The correspondence between the various units and the devices or control units is not limited to the above examples and can be varied.

[0173] Third Implementation Method

[0174] Figure 5 An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0175] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0176] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN and / or LAN, etc.

[0177] The head-mounted 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, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0178] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge-Coupled Device) image sensor, used to capture the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0180] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0181] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0183] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0184] In the head-mounted terminal 314, specific processing is performed by the processor 46. A specific program 60 is stored in the memory 50. The processor 46 reads the specific program 60 from the memory 50 and executes the read specific program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific program 60 executed on the RAM 48. Furthermore, the head-mounted terminal 314 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 sends the result of the specific processing to the head-mounted terminal 314. In the head-mounted terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires voice representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's 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.

[0187] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0188] The data processing system 310 of the third embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed 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 head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0189] Each of the aforementioned elements—collection, storage, analysis, import, provision, and execution support—can be implemented, for example, through at least one of the head-mounted terminal 314 and the data processing device 12. For instance, the collection unit uses the IoT device of the head-mounted terminal 314 to collect the company's energy consumption data in real time. The storage unit saves the collected data to the cloud storage of the data processing device 12. The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud through a specific processing unit 290 of the data processing device 12. The import unit imports the latest energy trends and investment opportunities information through the specific processing unit 290 of the data processing device 12. The provision unit provides the company with the optimal renewable energy investment plan through the specific processing unit 290 of the data processing device 12. The execution support unit provides the company with the specific steps required to implement the proposed investment plan through the specific processing unit 290 of the data processing device 12. The correspondence between the various units and the devices or control units is not limited to the above examples and can be varied.

[0190] Fourth Implementation Method

[0191] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0192] like Figure 7 As shown, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0193] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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. An example of the network 54 includes a WAN and / or LAN, etc.

[0194] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and control object 443 are also connected to the bus 52.

[0195] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0196] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0197] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0198] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0199] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0200] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0201] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function (emotion-specific function) using the emotion-specific model 59 includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0202] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in memory 50. Processor 46 reads the specific program 60 from memory 50 and executes the read specific program 60 on RAM 48. Specific processing is achieved by processor 46 acting as control unit 46A based on the specific program 60 executed on RAM 48. Furthermore, robot 414 may also have the same data generation model and emotion-specific model as data generation model 58 and emotion-specific model 59, and use these models to perform the same processing as specific processing unit 290.

[0203] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0204] The specific processing unit 290 sends the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires voice representing the user's input regarding the result of the specific processing. The control unit 46A sends the voice data representing 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.

[0205] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0206] The data processing system 410 of the fourth embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed 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 it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0207] Each of the aforementioned elements—collection, storage, analysis, import, provision, and execution support—can be implemented, for example, by at least one of robot 414 and data processing device 12. For instance, the collection unit uses the IoT device of robot 414 to collect the company's energy consumption data in real time. The storage unit saves the collected data to cloud storage in data processing device 12. The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud via a specific processing unit 290 of data processing device 12. The import unit imports the latest energy trends and investment opportunities information via the specific processing unit 290 of data processing device 12. The provision unit provides the company with optimal renewable energy investment plans via the specific processing unit 290 of data processing device 12. The execution support unit provides the company with the specific steps required to implement the proposed investment plan via the specific processing unit 290 of data processing device 12. The correspondence between the various units and the devices or control units is not limited to the above examples and can be varied.

[0208] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The robot's emotions can be determined by the emotion-specific model 59. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0209] Figure 9 This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0210] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0211] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0212] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a Brain Physiological Signal Analysis System for Speech Emotion Recognition and Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "response" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "situation" domain, where situational cognition is dominant, are arranged.

[0213] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0214] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, sentiment values ​​in nearby configurations are similar to each other. Figure 10 Examples show that multiple emotions such as "peace of mind", "stability", and "reassurance" have similar emotional values.

[0215] In the above embodiments, a specific processing is described by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing by multiple computers, including computer 22, is also possible.

[0216] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed into the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0217] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0218] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0219] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., programs. Additionally, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit). Every processor has built-in or connected memory, and every processor executes specific processing by using memory.

[0220] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0221] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources for performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors as hardware resources.

[0222] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0223] Furthermore, although the above examples have been described in terms of first to fourth embodiments, some or all of these embodiments can be combined. Additionally, the smart device 14, smart glasses 214, head-mounted terminal 314, and robot 414 are just examples and can be combined separately, or other devices may be used. Furthermore, although the above examples have been described in terms of morphological example 1 and morphological example 2, these can also be combined.

[0224] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0225] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described as incorporated by reference.

[0226] [Postscript 1]

[0227] A system characterized by comprising:

[0228] The collection unit is used to collect energy consumption data;

[0229] A storage unit is used to save the data collected by the collection unit to the cloud.

[0230] The parsing unit is used to parse the data stored by the storage unit;

[0231] The import section is used to import the latest energy trends or investment target information based on the data parsed by the analysis section;

[0232] The providing department is used to provide renewable energy investment plans based on the information obtained by the importing department;

[0233] The Execution Support Department is responsible for providing specific steps for implementing the investment plans provided by the Support Department.

[0234] [Postscript 2]

[0235] The system as described in Appendix 1 is characterized in that,

[0236] The collection unit collects energy consumption data in real time through IoT devices installed in factories or office buildings.

[0237] [Postscript 3]

[0238] The system as described in Appendix 1 is characterized in that,

[0239] The analysis unit uses generative AI to analyze the energy consumption data stored in the cloud.

[0240] [Postscript 4]

[0241] The system as described in Appendix 1 is characterized in that,

[0242] The import section imports the latest energy trends and investment target information through generative AI.

[0243] [Postscript 5]

[0244] The system as described in Appendix 1 is characterized in that,

[0245] The provision department uses generative AI to consider a company's energy consumption patterns or market trends to provide renewable energy investment solutions.

[0246] [Postscript 6]

[0247] The system as described in Appendix 1 is characterized in that,

[0248] The execution support department uses generative AI to provide enterprises with the specific steps needed to implement the proposed investment plan.

[0249] [Postscript 7]

[0250] The system as described in Appendix 1 is characterized in that,

[0251] The data collection unit estimates user sentiment and adjusts the timing of energy consumption data collection based on the estimated user sentiment.

[0252] [Postscript 8]

[0253] The system as described in Appendix 1 is characterized in that,

[0254] The collection unit changes the data collection frequency based on specific time periods or seasons when collecting energy consumption data.

[0255] [Postscript 9]

[0256] The system as described in Appendix 1 is characterized in that,

[0257] When collecting energy consumption data, the collection unit adds the function of detecting abnormal consumption patterns and issuing alarms.

[0258] [Postscript 10]

[0259] The system as described in Appendix 1 is characterized in that,

[0260] The data collection unit estimates user sentiment and determines the priority of data collection based on the estimated user sentiment.

[0261] [Postscript 11]

[0262] The system as described in Appendix 1 is characterized in that,

[0263] When collecting energy consumption data, the collection unit takes geographical location information into account.

[0264] [Postscript 12]

[0265] The system as described in Appendix 1 is characterized in that,

[0266] The collection unit also integrates data from other IoT devices while collecting energy consumption data.

[0267] [Postscript 13]

[0268] The system as described in Appendix 1 is characterized in that,

[0269] The data storage unit estimates user emotions and adjusts the data storage method based on the estimated user emotions.

[0270] [Postscript 14]

[0271] The system as described in Appendix 1 is characterized in that,

[0272] When saving data, the storage unit determines the priority of saving based on the importance of the data.

[0273] [Postscript 15]

[0274] The system as described in Appendix 1 is characterized in that,

[0275] The storage unit uses different storage formats depending on the type of data when saving data.

[0276] [Postscript 16]

[0277] The system as described in Appendix 1 is characterized in that,

[0278] The storage unit estimates user sentiment and adjusts the data retention period based on the estimated user sentiment.

[0279] [Postscript 17]

[0280] The system as described in Appendix 1 is characterized in that,

[0281] When saving data, the storage unit determines the priority of saving based on the timing of data submission.

[0282] [Postscript 18]

[0283] The system as described in Appendix 1 is characterized in that,

[0284] The storage unit adjusts the storage order based on the relevance of the data during data storage.

[0285] [Postscript 19]

[0286] The system as described in Appendix 1 is characterized in that,

[0287] The parsing unit infers user sentiment and adjusts the parsing method based on the inferred user sentiment.

[0288] [Postscript 20]

[0289] The system as described in Appendix 1 is characterized in that,

[0290] During the analysis process, the analysis unit refers to past data to predict the current data.

[0291] [Postscript 21]

[0292] The system as described in Appendix 1 is characterized in that,

[0293] The parsing unit employs different parsing algorithms based on the data category during parsing.

[0294] [Postscript 22]

[0295] The system as described in Appendix 1 is characterized in that,

[0296] The parsing unit infers user sentiment and determines the parsing priority based on the inferred user sentiment.

[0297] [Postscript 23]

[0298] The system as described in Appendix 1 is characterized in that,

[0299] During parsing, the parsing unit determines the parsing priority based on the timing of data submission.

[0300] [Postscript 24]

[0301] The system as described in Appendix 1 is characterized in that,

[0302] During the parsing process, the parsing unit adjusts the parsing order based on the correlation of the data.

[0303] [Postscript 25]

[0304] The system as described in Appendix 1 is characterized in that,

[0305] The importing unit estimates the user's emotions and determines the priority of the imported information based on the estimated user emotions.

[0306] [Postscript 26]

[0307] The system as described in Appendix 1 is characterized in that,

[0308] The importing unit updates the latest energy trends in real time during the importing process.

[0309] [Postscript 27]

[0310] The system as described in Appendix 1 is characterized in that,

[0311] When importing information, the importing unit assesses the reliability of the investment target information and prioritizes importing information with high reliability.

[0312] [Postscript 28]

[0313] The system as described in Appendix 1 is characterized in that,

[0314] The import section estimates the user's emotions and adjusts the display method of the imported information based on the estimated user emotions.

[0315] [Postscript 29]

[0316] The system as described in Appendix 1 is characterized in that,

[0317] When importing information, the importing unit takes geographical location information into account.

[0318] [Postscript 30]

[0319] The system as described in Appendix 1 is characterized in that,

[0320] The import section also integrates information from other data sources during the import process.

[0321] [Postscript 31]

[0322] The system as described in Appendix 1 is characterized in that,

[0323] The provision department estimates user sentiment and adjusts the expression of the provided investment plan based on the estimated user sentiment.

[0324] [Postscript 32]

[0325] The system as described in Appendix 1 is characterized in that,

[0326] When providing the investment plan, the supply department adjusts the level of detail based on the company's energy consumption patterns.

[0327] [Postscript 33]

[0328] The system as described in Appendix 1 is characterized in that,

[0329] When providing investment solutions, the supply department proposes different options based on the company's industry and size.

[0330] [Postscript 34]

[0331] The system as described in Appendix 1 is characterized in that,

[0332] The provision department estimates user sentiment and adjusts the length of the offered investment plan based on the estimated user sentiment.

[0333] [Postscript 35]

[0334] The system as described in Appendix 1 is characterized in that,

[0335] When providing the investment proposal, the providing department determines the priority of the proposal based on the timing of its submission.

[0336] [Postscript 36]

[0337] The system as described in Appendix 1 is characterized in that,

[0338] When providing services, the providing department adjusts the order of provision based on the relevance of the investment plan.

[0339] [Postscript 37]

[0340] The system as described in Appendix 1 is characterized in that,

[0341] The execution support unit estimates user emotions and adjusts execution support methods based on the estimated user emotions.

[0342] [Postscript 38]

[0343] The system as described in Appendix 1 is characterized in that,

[0344] When providing support, the execution support department selects the optimal support method by referring to the company's past investment history.

[0345] [Postscript 39]

[0346] The system as described in Appendix 1 is characterized in that,

[0347] When providing support, the execution support department customizes support measures based on the company's current financial situation.

[0348] [Postscript 40]

[0349] The system as described in Appendix 1 is characterized in that,

[0350] The execution support unit estimates user sentiment and determines the priority of execution support based on the estimated user sentiment.

[0351] [Postscript 41]

[0352] The system as described in Appendix 1 is characterized in that,

[0353] When providing support, the execution support department considers the company's geographical location information to select the optimal support method.

[0354] [Postscript 42]

[0355] The system as described in Appendix 1 is characterized in that,

[0356] When providing support, the Executive Support Department analyzes the company's social media activities and proposes support measures.

Claims

1. A system, characterized by The system includes: a collection unit configured to collect energy consumption data; a storage unit configured to store the data collected by the collection unit in a cloud; an analysis unit configured to analyze the data stored by the storage unit; an import unit configured to import the latest energy trend or investment target information based on the data analyzed by the analysis unit; a provision unit configured to provide a renewable energy investment plan based on the information obtained by the import unit; and an execution support unit configured to provide specific steps for executing the investment plan provided by the provision unit. 2.The system of claim 1, wherein the collection unit collects energy consumption data in real time through IoT devices installed in a factory or an office building. 3.The system of claim 1, wherein the analysis unit analyzes the energy consumption data stored in the cloud using generative AI. 4.The system of claim 1, wherein the import unit imports the latest energy trend and investment target information using generative AI. 5.The system of claim 1, wherein the provision unit provides a renewable energy investment plan by considering the energy consumption pattern of a company or market trends using generative AI. 6.The system of claim 1, wherein the execution support unit provides specific steps required to execute the proposed investment plan to a company using generative AI. 7.The system of claim 1, wherein the collection unit estimates user sentiment and adjusts the timing of collecting energy consumption data based on the estimated user sentiment. 8.The system of claim 1, wherein the collection unit changes the frequency of collecting data according to a specific period or season when collecting energy consumption data. ​ ​ ​ ​ ​ ​ ​

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