system

Abstract

The system according to the embodiment aims to provide a prompt and appropriate recovery plan when a malfunction occurs in facility equipment. [Solution] A system according to an embodiment includes a collection unit, a storage unit, a generation unit, and a provision unit. The collection unit collects information related to facility equipment. The storage unit stores the information collected by the collection unit in a database. The generation unit analyzes the information stored by the storage unit and generates an appropriate recovery plan in the event of an emergency. The provision unit provides the recovery plan generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide a quick and appropriate recovery plan when a malfunction occurs in facility equipment.

[0005] The system according to the embodiment aims to provide a prompt and appropriate recovery plan when a malfunction occurs in facility equipment. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a storage unit, a generation unit, and a provision unit. The collection unit collects information related to facility equipment. The storage unit stores the information collected by the collection unit in a database. The generation unit analyzes the information stored by the storage unit and generates an appropriate recovery plan in the event of an emergency. The provision unit provides the recovery plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a prompt and appropriate recovery plan when a malfunction occurs in facility equipment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A stable operation system for communication network equipment according to an embodiment of the present invention collects information about facility equipment, stores it in a database, and formats it in a format that is easy for a generation AI to analyze. This system utilizes the generation AI to generate optimal recovery plans in emergencies, enabling quick and efficient responses. For example, the system collects information about facility equipment, such as specifications, procedures, and past cases, and stores it in a database. The collected information is then formatted in a format that is easy for the generation AI to analyze. The generation AI then analyzes this information and generates the optimal recovery plan in emergencies. For example, if a malfunction occurs in power supply equipment, the generation AI proposes the optimal response procedure based on past cases and procedures. This mechanism ensures stable operation of communication network equipment and enables quick and efficient responses in emergencies. As a result, the stable operation system for communication network equipment efficiently collects, stores, and analyzes information about facility equipment, allowing it to provide optimal recovery plans in emergencies.

[0029] A stable operation system for a communication network device according to an embodiment includes a collection unit, a storage unit, a generation unit, and a provision unit. The collection unit collects information related to facility equipment. The collection unit collects information such as specifications, procedures, and past cases related to the facility equipment. The collection unit can also automatically collect information using sensors. For example, the collection unit can collect information about the operation status of air conditioning equipment using temperature and humidity sensors. The collection unit can also manually input information. For example, the collection unit can collect information manually entered by maintenance staff. The storage unit stores the information collected by the collection unit in a database. The storage unit can store information using, for example, a relational database or a NoSQL database. The storage unit can also manage information based on a storage format and a storage period. For example, the storage unit can store collected information in JSON format and automatically delete it after a certain period of time. The generation unit analyzes the information stored by the storage unit and generates an optimal recovery plan in an emergency. The generation unit can analyze the information using, for example, data mining, statistical analysis, or a machine learning algorithm. The generation unit can also generate a recovery plan using a generation AI. For example, the generation unit can input past cases and procedure manuals into the generation AI to generate an optimal recovery plan. The provision unit provides the recovery plan generated by the generation unit to a user. The provision unit can provide the recovery plan by methods such as email, dashboard display, and alert notification. The provision unit can also customize the recovery plan according to the user's position and skill level. For example, the provision unit can provide an overview-level recovery plan to managers and a recovery plan including detailed procedures to technical staff. As a result, the stable operation system for a communication network device according to the embodiment can efficiently collect, accumulate, and analyze information about facility equipment and provide an optimal recovery plan in an emergency.

[0030] The collection unit can collect information on specifications or procedures related to facility equipment, as well as information on past cases. For example, the collection unit collects specifications related to facility equipment. For example, the collection unit can automatically download specifications in PDF format and store them in a database. The collection unit can also collect procedures. For example, the collection unit can scan paper procedures, convert them into digital data, and store them in a database. The collection unit can also collect information on past cases. For example, the collection unit can collect past failure histories and maintenance records and store them in a database. In this way, the collection unit can collect detailed information about facility equipment, enabling quick and accurate response in emergencies.

[0031] The storage unit can store the information collected by the collection unit in a database. For example, the storage unit stores the information collected by the collection unit in a relational database. For example, the storage unit can save the collected information in a table format and search it using an SQL query. The storage unit can also store the information in a NoSQL database. For example, the storage unit can save the collected information in JSON format and manage it in a key-value database. Furthermore, the storage unit can manage the information based on the storage format and storage period. For example, the storage unit can be set to automatically delete collected information after a certain period of time. This allows the storage unit to centrally manage the information by storing the collected information in a database.

[0032] The generation unit can analyze the information accumulated by the accumulation unit and generate an appropriate recovery plan in an emergency. The generation unit, for example, analyzes the information accumulated by the accumulation unit using data mining technology. For example, the generation unit can analyze past failure histories and maintenance records to identify the causes and patterns of failures. The generation unit can also analyze the information using statistical analysis. For example, the generation unit can statistically analyze collected data and predict the probability of failure occurrence and the scope of its impact. Furthermore, the generation unit can analyze the information using a machine learning algorithm. For example, the generation unit can use a machine learning model to predict failures and propose optimal recovery procedures. In this way, the generation unit can generate an optimal recovery plan in an emergency by analyzing the accumulated information.

[0033] The providing unit can provide the recovery plan generated by the generating unit to the user. The providing unit, for example, provides the recovery plan generated by the generating unit to the user by email. For example, the providing unit can attach the generated recovery plan as a PDF file and send it to the user. The providing unit can also provide the recovery plan using a dashboard display. For example, the providing unit can display the recovery plan through a web application so that the user can check it in real time. Furthermore, the providing unit can provide the recovery plan using an alert notification. For example, the providing unit can send an SMS or push notification in the event of an emergency to quickly provide the user with the recovery plan. In this way, the providing unit can provide the generated recovery plan to the user, enabling a quick response.

[0034] The collection unit can monitor the operating status of facility equipment in real time and automatically collect information when an abnormality is detected. The collection unit, for example, uses sensors to monitor the operating status of facility equipment in real time. For example, the collection unit can use voltage sensors and current sensors to monitor the voltage and current of power supply equipment. The collection unit can also use temperature sensors and humidity sensors to monitor the temperature and humidity of air conditioning equipment. Furthermore, the collection unit can use network monitoring tools to monitor the connection status of communication network devices. For example, the collection unit can use network monitoring tools to monitor the connection status and data transfer speed of communication network devices. This allows the collection unit to detect abnormalities in facility equipment in real time and automatically collect information for rapid response.

[0035] The collection unit can dynamically change the type of information to be collected depending on the type and importance of the equipment. The collection unit, for example, dynamically changes the type of information to be collected depending on the type of equipment. For example, the collection unit can prioritize collecting detailed specifications and procedure manuals for information about power supply equipment that is highly important. The collection unit can also collect information about air conditioning equipment, mainly temperature and humidity data. Furthermore, the collection unit can collect information about connection status and data transfer speeds for information about communication network devices. In this way, the collection unit can efficiently collect necessary information by dynamically changing the information to be collected depending on the type and importance of the equipment.

[0036] The collection unit can also collect information from other communication network devices and perform mutual comparisons. The collection unit, for example, collects the operation statuses of other communication network devices and compares them with a device in which an abnormality has occurred. For example, the collection unit can monitor the operation statuses of other communication network devices and collect related information when an abnormality occurs. The collection unit can also collect past cases of other communication network devices and compare them with the current status. For example, the collection unit can collect failure histories and maintenance records of other communication network devices and compare them with the current status. Furthermore, the collection unit can collect procedure manuals of other communication network devices and select an optimal procedure. For example, the collection unit can collect procedure manuals of other communication network devices and select an optimal response procedure. As a result, the collection unit can collect information from other communication network devices and perform mutual comparisons to select an optimal response procedure.

[0037] The collection unit can collect relevant information from social media and news sites to understand trends in facility equipment. The collection unit, for example, collects the latest trend information on facility equipment from social media. For example, the collection unit can collect posts about facility equipment from social media such as X (formerly Twitter (registered trademark)) and Facebook (registered trademark) to understand trends. The collection unit can also collect the latest technical information on facility equipment from news sites. For example, the collection unit can monitor news feeds to collect the latest technical information on facility equipment. Furthermore, the collection unit can identify areas for improvement in facility equipment based on the information collected from social media and news sites. For example, the collection unit can analyze the information collected from social media and news sites to identify areas for improvement in facility equipment. In this way, the collection unit can understand the latest trends in facility equipment by collecting information from social media and news sites.

[0038] The storage unit can automatically classify the stored information and organize it in an easy-to-search format. For example, the storage unit can classify the collected information by type of equipment and organize it in an easy-to-search format. For example, the storage unit can categorize the collected information into categories such as power supply equipment, air conditioning equipment, and communication network equipment and organize it in an easy-to-search format. The storage unit can also classify the collected information by importance to enable prioritized retrieval. For example, the storage unit can classify the collected information into information of high importance, medium importance, and low importance and enable prioritized retrieval. Furthermore, the storage unit can classify the collected information in chronological order to enable easy retrieval of past cases. For example, the storage unit can classify the collected information by year, month, and date to enable easy retrieval of past cases. In this way, the storage unit automatically classifies the stored information, making it easier to search for information and enabling efficient information use.

[0039] The storage unit can evaluate the reliability of the stored information and preferentially store information with high reliability. The storage unit, for example, evaluates the source of the collected information and preferentially stores information with high reliability. For example, the storage unit can check the source of the collected information and preferentially store information with high reliability. The storage unit can also evaluate the content of the collected information and preferentially store information with high accuracy. For example, the storage unit can analyze the content of the collected information and preferentially store information with high accuracy. Furthermore, the storage unit can evaluate the update frequency of the collected information and preferentially store the latest information. For example, the storage unit can check the update frequency of the collected information and preferentially store the latest information. In this way, the storage unit can evaluate the reliability of the stored information and preferentially store information with high reliability, thereby improving the quality of data.

[0040] The storage unit can link the stored information with other systems and make it mutually available. The storage unit, for example, shares the stored information with other communication network devices and makes it mutually available. For example, the storage unit can share information by API linkage with other communication network devices. The storage unit can also link the stored information with other facility equipment to achieve efficient operation. For example, the storage unit can import data with other facility equipment and share information. Furthermore, the storage unit can link the stored information with other management systems and achieve integrated management. For example, the storage unit can share files with other management systems and share information. As a result, the storage unit can link the stored information with other systems, enabling information sharing and efficient operation.

[0041] The storage unit can periodically back up the stored information to prevent data loss. The storage unit, for example, periodically backs up the stored information to external storage. For example, the storage unit can periodically copy collected information to external storage to prevent data loss. The storage unit can also back up the stored information to cloud storage. For example, the storage unit can periodically upload collected information to cloud storage to prevent data loss. Furthermore, the storage unit can also back up the stored information to multiple locations. For example, the storage unit can periodically copy collected information to multiple physical locations to prevent data loss. In this way, by periodically backing up the stored information, the storage unit can prevent data loss and ensure the safety of the information.

[0042] The generation unit can construct a feedback loop to evaluate the effectiveness of past recovery plans and generate an optimal plan. The generation unit, for example, evaluates the execution results of past recovery plans and prioritizes the generation of highly effective plans. For example, the generation unit can store the execution results of past recovery plans in a database and evaluate their effectiveness. The generation unit can also analyze past failure cases of recovery plans and generate plans that reflect improvements. For example, the generation unit can analyze past failure cases of recovery plans, identify improvements, and generate plans that reflect them. Furthermore, the generation unit can evaluate the execution time of past recovery plans and generate plans that allow for quick response. For example, the generation unit can evaluate the execution time of past recovery plans and prioritize the generation of plans that allow for quick response. In this way, the generation unit can generate more effective recovery plans by evaluating the effectiveness of past recovery plans.

[0043] The generation unit can generate multiple recovery plans according to the emergency situation and select the optimal plan. The generation unit can generate multiple recovery plans according to the emergency situation and select the optimal plan. For example, the generation unit can generate multiple recovery plans including different procedures according to the emergency situation and select the optimal plan from among them. The generation unit can also generate multiple recovery plans taking into account resource usage according to the emergency situation. For example, the generation unit can generate the optimal recovery plan taking into account available resources according to the emergency situation. Furthermore, the generation unit can simulate multiple recovery plans according to the emergency situation and select the optimal plan based on the simulation results. For example, the generation unit can simulate multiple recovery plans and select the optimal plan based on the simulation results. This allows the generation unit to select the optimal recovery plan according to the emergency situation, thereby enabling a quick and effective response.

[0044] The generation unit can incorporate data from other systems to generate a more accurate recovery plan. The generation unit, for example, incorporates data from other communication network devices to generate a more accurate recovery plan. For example, the generation unit can incorporate operation status data from other communication network devices and generate a more accurate recovery plan based on the data. The generation unit can also incorporate data from other facility equipment to generate a more accurate recovery plan. For example, the generation unit can incorporate temperature and humidity data from other facility equipment and generate a more accurate recovery plan based on the data. Furthermore, the generation unit can incorporate data from other management systems to generate a more accurate recovery plan. For example, the generation unit can incorporate maintenance history data from other management systems and generate a more accurate recovery plan based on the data. In this way, the generation unit can generate a more accurate recovery plan by incorporating data from other systems.

[0045] The generation unit can simulate the generated recovery plan and verify its effectiveness before execution. The generation unit, for example, simulates the generated recovery plan and verify its effectiveness before execution. For example, the generation unit can simulate the generated recovery plan using a virtual environment and verify its effectiveness. The generation unit can also simulate the generated recovery plan using scenario testing. For example, the generation unit can set multiple scenarios, simulate the generated recovery plan based on the scenarios, and verify its effectiveness. Furthermore, the generation unit can simulate the generated recovery plan using modeling technology. For example, the generation unit can model the operating status of the equipment, simulate the generated recovery plan based on the model, and verify its effectiveness. In this way, the generation unit can simulate the generated recovery plan to verify its effectiveness before execution and select the optimal plan by simulating the generated recovery plan.

[0046] The provision unit can customize the recovery plan to be provided according to the user's position and skill level. The provision unit, for example, provides a recovery plan that includes only necessary information according to the user's position. For example, the provision unit can provide an overview recovery plan to managers and a recovery plan that includes detailed procedures to technical staff. The provision unit can also provide a recovery plan that includes detailed procedures according to the user's skill level. For example, the provision unit can provide a recovery plan that includes detailed procedures to beginners and a recovery plan that includes simple procedures to experienced staff. Furthermore, the provision unit can provide a recovery plan with an appropriate level of difficulty according to the user's position and skill level. For example, the provision unit can provide an overview recovery plan to managers and a recovery plan that includes detailed procedures to technical staff. This allows the provision unit to customize the recovery plan according to the user's position and skill level, thereby enabling more appropriate response.

[0047] The providing unit monitors the execution status of the provided recovery plan and can make corrections in real time as necessary. The providing unit, for example, monitors the execution status of the provided recovery plan in real time. For example, the providing unit can monitor the execution status of the recovery plan in real time using sensors or monitoring tools. The providing unit can also detect a problem that occurs during the execution of the provided recovery plan and make immediate corrections. For example, the providing unit can detect a problem that occurs during the execution of the recovery plan and make corrections in real time. Furthermore, the providing unit can make corrections to reflect the results of the execution of the provided recovery plan in the next plan based on the execution results of the provided recovery plan. For example, the providing unit can analyze the execution results of the recovery plan and make corrections to reflect the results in the next plan. In this way, the providing unit can monitor the execution status of the provided recovery plan and make corrections in real time as necessary, enabling a rapid response.

[0048] The providing unit can share the provided recovery plan with other users and respond jointly. The providing unit, for example, shares the provided recovery plan with team members and respond jointly. For example, the providing unit can share the provided recovery plan with team members and respond jointly. The providing unit can also share the provided recovery plan with other departments and respond in cooperation. For example, the providing unit can share the provided recovery plan with other departments and respond in cooperation. Furthermore, the providing unit can share the provided recovery plan with external experts and receive professional advice. For example, the providing unit can share the provided recovery plan with external experts and receive professional advice. In this way, the providing unit can share the provided recovery plan with other users, thereby responding quickly and effectively in cooperation.

[0049] The providing unit can save the history of the provided recovery plans for future reference. The providing unit, for example, saves the history of the provided recovery plans in a database for future reference. For example, the providing unit can save the history of the provided recovery plans in a database for future reference. The providing unit can also record the execution results of the provided recovery plans and reflect them in the next plan. For example, the providing unit can record the execution results of the provided recovery plans and reflect them in the next plan. Furthermore, the providing unit can analyze the history of the provided recovery plans and find areas for improvement. For example, the providing unit can analyze the history of the provided recovery plans and find areas for improvement. In this way, by saving the history of the provided recovery plans, the providing unit can use it for future reference and enable more effective responses.

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

[0051] The collection unit can monitor the operating status of facility equipment in real time and automatically collect information when an abnormality is detected. For example, the collection unit can use voltage sensors and current sensors to monitor the voltage and current of power supply equipment. The collection unit can also use temperature sensors and humidity sensors to monitor the temperature and humidity of air conditioning equipment. Furthermore, the collection unit can use network monitoring tools to monitor the connection status of communication network devices. In this way, the collection unit can detect abnormalities in facility equipment in real time and automatically collect information to respond quickly.

[0052] The storage unit can automatically classify the stored information and arrange it in a format that is easy to search. For example, the storage unit can classify collected information by type of equipment and arrange it in a format that is easy to search. The storage unit can also classify collected information by importance so that it can be searched preferentially. Furthermore, the storage unit can classify collected information in chronological order so that past cases can be easily searched. In this way, the storage unit can automatically classify stored information, making it easier to search for information and enabling efficient information use.

[0053] The providing unit can monitor the execution status of the provided recovery plan and make corrections in real time as necessary. For example, the providing unit can monitor the execution status of the recovery plan in real time using sensors and monitoring tools. The providing unit can also detect problems that occur during the execution of the provided recovery plan and make corrections immediately. Furthermore, the providing unit can make corrections to be reflected in the next plan based on the execution results of the provided recovery plan. This allows the providing unit to monitor the execution status of the provided recovery plan and make corrections in real time as necessary, enabling rapid response.

[0054] The collection department can collect relevant information from social media and news sites to understand trends in facility equipment. For example, the collection department can collect posts about facility equipment from social media such as X (formerly Twitter) and Facebook to understand trends. The collection department can also collect the latest technical information about facility equipment from news sites. Furthermore, the collection department can also identify areas for improvement in facility equipment based on the information collected from social media and news sites. In this way, the collection department can understand the latest trends in facility equipment by collecting information from social media and news sites.

[0055] The generation unit can construct a feedback loop to evaluate the effectiveness of past recovery plans and generate an optimal plan. For example, the generation unit can evaluate the execution results of past recovery plans and generate highly effective plans with priority. The generation unit can also analyze failure cases of past recovery plans and generate plans that reflect improvements. Furthermore, the generation unit can evaluate the execution time of past recovery plans and generate plans that allow for quick response. In this way, the generation unit can generate more effective recovery plans by evaluating the effectiveness of past recovery plans.

[0056] The provision department can share the provided recovery plan with other users and work together to address the issue. For example, the provision department can share the provided recovery plan with members of its team and work together to address the issue. The provision department can also share the provided recovery plan with other departments and work together to address the issue. Furthermore, the provision department can share the provided recovery plan with external experts and receive expert advice. In this way, by sharing the provided recovery plan with other users, the provision department can work together to address the issue quickly and effectively.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects information about facility equipment. The collection unit collects information such as specifications, procedures, and past cases about facility equipment. The collection unit can also collect information automatically using sensors. For example, the collection unit can collect information about the operating status of air conditioning equipment using temperature and humidity sensors. The collection unit can also manually input information. For example, the collection unit can collect information manually entered by maintenance staff. Step 2: The storage unit stores the information collected by the collection unit in a database. The storage unit can store information using, for example, a relational database or a NoSQL database. The storage unit can also manage the information based on the storage format and storage period. For example, the storage unit can store the collected information in JSON format and automatically delete it after a certain period of time. Step 3: The generation unit analyzes the information accumulated by the accumulation unit and generates an optimal recovery plan in the event of an emergency. The generation unit can analyze the information using, for example, data mining, statistical analysis, or machine learning algorithms. The generation unit can also generate a recovery plan using a generation AI. For example, the generation unit can input past cases and procedure manuals into the generation AI to generate an optimal recovery plan. Step 4: The provisioning unit provides the recovery plan generated by the generation unit to the user. The provisioning unit can provide the recovery plan by methods such as email, dashboard display, and alert notification. The provisioning unit can also customize the recovery plan according to the user's position and skill level. For example, the provisioning unit can provide a high-level recovery plan to managers and a recovery plan with detailed procedures to technical staff.

[0059] (Example 2) A stable operation system for communication network equipment according to an embodiment of the present invention collects information about facility equipment, stores it in a database, and formats it in a format that is easy for a generation AI to analyze. This system utilizes the generation AI to generate optimal recovery plans in emergencies, enabling quick and efficient responses. For example, the system collects information about facility equipment, such as specifications, procedures, and past cases, and stores it in a database. The collected information is then formatted in a format that is easy for the generation AI to analyze. The generation AI then analyzes this information and generates the optimal recovery plan in emergencies. For example, if a malfunction occurs in power supply equipment, the generation AI proposes the optimal response procedure based on past cases and procedures. This mechanism ensures stable operation of communication network equipment and enables quick and efficient responses in emergencies. As a result, the stable operation system for communication network equipment efficiently collects, stores, and analyzes information about facility equipment, allowing it to provide optimal recovery plans in emergencies.

[0060] A stable operation system for a communication network device according to an embodiment includes a collection unit, a storage unit, a generation unit, and a provision unit. The collection unit collects information related to facility equipment. The collection unit collects information such as specifications, procedures, and past cases related to the facility equipment. The collection unit can also automatically collect information using sensors. For example, the collection unit can collect information about the operation status of air conditioning equipment using temperature and humidity sensors. The collection unit can also manually input information. For example, the collection unit can collect information manually entered by maintenance staff. The storage unit stores the information collected by the collection unit in a database. The storage unit can store information using, for example, a relational database or a NoSQL database. The storage unit can also manage information based on a storage format and a storage period. For example, the storage unit can store collected information in JSON format and automatically delete it after a certain period of time. The generation unit analyzes the information stored by the storage unit and generates an optimal recovery plan in an emergency. The generation unit can analyze the information using, for example, data mining, statistical analysis, or a machine learning algorithm. The generation unit can also generate a recovery plan using a generation AI. For example, the generation unit can input past cases and procedure manuals into the generation AI to generate an optimal recovery plan. The provision unit provides the recovery plan generated by the generation unit to a user. The provision unit can provide the recovery plan by methods such as email, dashboard display, and alert notification. The provision unit can also customize the recovery plan according to the user's position and skill level. For example, the provision unit can provide an overview-level recovery plan to managers and a recovery plan including detailed procedures to technical staff. As a result, the stable operation system for a communication network device according to the embodiment can efficiently collect, accumulate, and analyze information about facility equipment and provide an optimal recovery plan in an emergency.

[0061] The collection unit can collect information on specifications or procedures related to facility equipment, as well as information on past cases. For example, the collection unit collects specifications related to facility equipment. For example, the collection unit can automatically download specifications in PDF format and store them in a database. The collection unit can also collect procedures. For example, the collection unit can scan paper procedures, convert them into digital data, and store them in a database. The collection unit can also collect information on past cases. For example, the collection unit can collect past failure histories and maintenance records and store them in a database. In this way, the collection unit can collect detailed information about facility equipment, enabling quick and accurate response in emergencies.

[0062] The storage unit can store the information collected by the collection unit in a database. For example, the storage unit stores the information collected by the collection unit in a relational database. For example, the storage unit can save the collected information in a table format and search it using an SQL query. The storage unit can also store the information in a NoSQL database. For example, the storage unit can save the collected information in JSON format and manage it in a key-value database. Furthermore, the storage unit can manage the information based on the storage format and storage period. For example, the storage unit can be set to automatically delete collected information after a certain period of time. This allows the storage unit to centrally manage the information by storing the collected information in a database.

[0063] The generation unit can analyze the information accumulated by the accumulation unit and generate an appropriate recovery plan in an emergency. The generation unit, for example, analyzes the information accumulated by the accumulation unit using data mining technology. For example, the generation unit can analyze past failure histories and maintenance records to identify the causes and patterns of failures. The generation unit can also analyze the information using statistical analysis. For example, the generation unit can statistically analyze collected data and predict the probability of failure occurrence and the scope of its impact. Furthermore, the generation unit can analyze the information using a machine learning algorithm. For example, the generation unit can use a machine learning model to predict failures and propose optimal recovery procedures. In this way, the generation unit can generate an optimal recovery plan in an emergency by analyzing the accumulated information.

[0064] The providing unit can provide the recovery plan generated by the generating unit to the user. The providing unit, for example, provides the recovery plan generated by the generating unit to the user by email. For example, the providing unit can attach the generated recovery plan as a PDF file and send it to the user. The providing unit can also provide the recovery plan using a dashboard display. For example, the providing unit can display the recovery plan through a web application so that the user can check it in real time. Furthermore, the providing unit can provide the recovery plan using an alert notification. For example, the providing unit can send an SMS or push notification in the event of an emergency to quickly provide the user with the recovery plan. In this way, the providing unit can provide the generated recovery plan to the user, enabling a quick response.

[0065] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit can analyze the user's facial expressions captured by a camera to estimate the user's stress or relaxation state. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the user's emotional state. Furthermore, the collection unit can estimate the user's emotions using survey results. For example, the collection unit can analyze the content of a survey answered by the user to estimate the user's emotional state. In this way, the collection unit can adjust the timing of information collection according to the user's emotions, thereby reducing the user's burden and enabling efficient information collection.

[0066] The collection unit can monitor the operating status of facility equipment in real time and automatically collect information when an abnormality is detected. The collection unit, for example, uses sensors to monitor the operating status of facility equipment in real time. For example, the collection unit can use voltage sensors and current sensors to monitor the voltage and current of power supply equipment. The collection unit can also use temperature sensors and humidity sensors to monitor the temperature and humidity of air conditioning equipment. Furthermore, the collection unit can use network monitoring tools to monitor the connection status of communication network devices. For example, the collection unit can use network monitoring tools to monitor the connection status and data transfer speed of communication network devices. This allows the collection unit to detect abnormalities in facility equipment in real time and automatically collect information for rapid response.

[0067] The collection unit can dynamically change the type of information to be collected depending on the type and importance of the equipment. The collection unit, for example, dynamically changes the type of information to be collected depending on the type of equipment. For example, the collection unit can prioritize collecting detailed specifications and procedure manuals for information about power supply equipment that is highly important. The collection unit can also collect information about air conditioning equipment, mainly temperature and humidity data. Furthermore, the collection unit can collect information about connection status and data transfer speeds for information about communication network devices. In this way, the collection unit can efficiently collect necessary information by dynamically changing the information to be collected depending on the type and importance of the equipment.

[0068] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit can analyze the user's facial expressions captured by a camera and estimate a state of tension or relaxation. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice and estimate the emotional state. Furthermore, the collection unit can estimate the user's emotions using survey results. For example, the collection unit can analyze the content of a survey answered by the user and estimate the emotional state. In this way, the collection unit can prioritize the collection of important information by determining the priority of information to be collected according to the user's emotions.

[0069] The collection unit can also collect information from other communication network devices and perform mutual comparisons. The collection unit, for example, collects the operation statuses of other communication network devices and compares them with a device in which an abnormality has occurred. For example, the collection unit can monitor the operation statuses of other communication network devices and collect related information when an abnormality occurs. The collection unit can also collect past cases of other communication network devices and compare them with the current status. For example, the collection unit can collect failure histories and maintenance records of other communication network devices and compare them with the current status. Furthermore, the collection unit can collect procedure manuals of other communication network devices and select an optimal procedure. For example, the collection unit can collect procedure manuals of other communication network devices and select an optimal response procedure. As a result, the collection unit can collect information from other communication network devices and perform mutual comparisons to select an optimal response procedure.

[0070] The collection unit can collect relevant information from social media and news sites to understand trends in facility equipment. The collection unit, for example, collects the latest trend information on facility equipment from social media. For example, the collection unit can collect posts about facility equipment from social media such as X (formerly Twitter) and Facebook to understand trends. The collection unit can also collect the latest technical information on facility equipment from news sites. For example, the collection unit can monitor news feeds to collect the latest technical information on facility equipment. Furthermore, the collection unit can identify areas for improvement in facility equipment based on the information collected from social media and news sites. For example, the collection unit can analyze the information collected from social media and news sites to identify areas for improvement in facility equipment. In this way, the collection unit can understand the latest trends in facility equipment by collecting information from social media and news sites.

[0071] The storage unit can estimate the user's emotions and adjust the update frequency of the database based on the estimated user's emotions. The storage unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the storage unit can analyze the user's facial expressions captured by a camera to estimate the user's stress or relaxation state. The storage unit can also estimate the user's emotions using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice to estimate the user's emotional state. Furthermore, the storage unit can estimate the user's emotions using survey results. For example, the storage unit can analyze the content of a survey answered by the user to estimate the user's emotional state. In this way, the storage unit can adjust the update frequency of the database according to the user's emotions, thereby reducing the burden on the user and enabling efficient data management.

[0072] The storage unit can automatically classify the stored information and organize it in an easy-to-search format. For example, the storage unit can classify the collected information by type of equipment and organize it in an easy-to-search format. For example, the storage unit can categorize the collected information into categories such as power supply equipment, air conditioning equipment, and communication network equipment and organize it in an easy-to-search format. The storage unit can also classify the collected information by importance to enable prioritized retrieval. For example, the storage unit can classify the collected information into information of high importance, medium importance, and low importance and enable prioritized retrieval. Furthermore, the storage unit can classify the collected information in chronological order to enable easy retrieval of past cases. For example, the storage unit can classify the collected information by year, month, and date to enable easy retrieval of past cases. In this way, the storage unit automatically classifies the stored information, making it easier to search for information and enabling efficient information use.

[0073] The storage unit can evaluate the reliability of the stored information and preferentially store information with high reliability. The storage unit, for example, evaluates the source of the collected information and preferentially stores information with high reliability. For example, the storage unit can check the source of the collected information and preferentially store information with high reliability. The storage unit can also evaluate the content of the collected information and preferentially store information with high accuracy. For example, the storage unit can analyze the content of the collected information and preferentially store information with high accuracy. Furthermore, the storage unit can evaluate the update frequency of the collected information and preferentially store the latest information. For example, the storage unit can check the update frequency of the collected information and preferentially store the latest information. In this way, the storage unit can evaluate the reliability of the stored information and preferentially store information with high reliability, thereby improving the quality of data.

[0074] The storage unit can estimate the user's emotions and adjust the access authority of the database based on the estimated user's emotions. The storage unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the storage unit can analyze the user's facial expressions captured by a camera to estimate the user's state of tension or relaxation. The storage unit can also estimate the user's emotions using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice to estimate the user's emotional state. Furthermore, the storage unit can estimate the user's emotions using survey results. For example, the storage unit can analyze the content of a survey answered by the user to estimate the user's emotional state. In this way, the storage unit can adjust the access authority of the database according to the user's emotions, preventing erroneous operations and enabling efficient information use.

[0075] The storage unit can link the stored information with other systems and make it mutually available. The storage unit, for example, shares the stored information with other communication network devices and makes it mutually available. For example, the storage unit can share information by API linkage with other communication network devices. The storage unit can also link the stored information with other facility equipment to achieve efficient operation. For example, the storage unit can import data with other facility equipment and share information. Furthermore, the storage unit can link the stored information with other management systems and achieve integrated management. For example, the storage unit can share files with other management systems and share information. As a result, the storage unit can link the stored information with other systems, enabling information sharing and efficient operation.

[0076] The storage unit can periodically back up the stored information to prevent data loss. The storage unit, for example, periodically backs up the stored information to external storage. For example, the storage unit can periodically copy collected information to external storage to prevent data loss. The storage unit can also back up the stored information to cloud storage. For example, the storage unit can periodically upload collected information to cloud storage to prevent data loss. Furthermore, the storage unit can also back up the stored information to multiple locations. For example, the storage unit can periodically copy collected information to multiple physical locations to prevent data loss. In this way, by periodically backing up the stored information, the storage unit can prevent data loss and ensure the safety of the information.

[0077] The generation unit can estimate the user's emotions and adjust the generation method of the recovery plan based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the generation unit can analyze the user's facial expressions captured by a camera and estimate a state of relaxation or tension. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotional state. Furthermore, the generation unit can estimate the user's emotions using questionnaire results. For example, the generation unit can analyze the content of a questionnaire answered by the user and estimate the emotional state. In this way, the generation unit can adjust the generation method of the recovery plan according to the user's emotions, thereby providing a more appropriate recovery plan.

[0078] The generation unit can construct a feedback loop to evaluate the effectiveness of past recovery plans and generate an optimal plan. The generation unit, for example, evaluates the execution results of past recovery plans and prioritizes the generation of highly effective plans. For example, the generation unit can store the execution results of past recovery plans in a database and evaluate their effectiveness. The generation unit can also analyze past failure cases of recovery plans and generate plans that reflect improvements. For example, the generation unit can analyze past failure cases of recovery plans, identify improvements, and generate plans that reflect them. Furthermore, the generation unit can evaluate the execution time of past recovery plans and generate plans that allow for quick response. For example, the generation unit can evaluate the execution time of past recovery plans and prioritize the generation of plans that allow for quick response. In this way, the generation unit can generate more effective recovery plans by evaluating the effectiveness of past recovery plans.

[0079] The generation unit can generate multiple recovery plans according to the emergency situation and select the optimal plan. The generation unit can generate multiple recovery plans according to the emergency situation and select the optimal plan. For example, the generation unit can generate multiple recovery plans including different procedures according to the emergency situation and select the optimal plan from among them. The generation unit can also generate multiple recovery plans taking into account resource usage according to the emergency situation. For example, the generation unit can generate the optimal recovery plan taking into account available resources according to the emergency situation. Furthermore, the generation unit can simulate multiple recovery plans according to the emergency situation and select the optimal plan based on the simulation results. For example, the generation unit can simulate multiple recovery plans and select the optimal plan based on the simulation results. This allows the generation unit to select the optimal recovery plan according to the emergency situation, thereby enabling a quick and effective response.

[0080] The generation unit can estimate the user's emotions and adjust the level of detail of the recovery plan to be generated based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the generation unit can analyze the user's facial expressions captured by a camera and estimate a state of relaxation or tension. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotional state. Furthermore, the generation unit can estimate the user's emotions using questionnaire results. For example, the generation unit can analyze the content of a questionnaire answered by the user and estimate the emotional state. In this way, the generation unit can adjust the level of detail of the recovery plan according to the user's emotions, thereby providing a more appropriate recovery plan.

[0081] The generation unit can incorporate data from other systems to generate a more accurate recovery plan. The generation unit, for example, incorporates data from other communication network devices to generate a more accurate recovery plan. For example, the generation unit can incorporate operation status data from other communication network devices and generate a more accurate recovery plan based on the data. The generation unit can also incorporate data from other facility equipment to generate a more accurate recovery plan. For example, the generation unit can incorporate temperature and humidity data from other facility equipment and generate a more accurate recovery plan based on the data. Furthermore, the generation unit can incorporate data from other management systems to generate a more accurate recovery plan. For example, the generation unit can incorporate maintenance history data from other management systems and generate a more accurate recovery plan based on the data. In this way, the generation unit can generate a more accurate recovery plan by incorporating data from other systems.

[0082] The generation unit can simulate the generated recovery plan and verify its effectiveness before execution. The generation unit, for example, simulates the generated recovery plan and verify its effectiveness before execution. For example, the generation unit can simulate the generated recovery plan using a virtual environment and verify its effectiveness. The generation unit can also simulate the generated recovery plan using scenario testing. For example, the generation unit can set multiple scenarios, simulate the generated recovery plan based on the scenarios, and verify its effectiveness. Furthermore, the generation unit can simulate the generated recovery plan using modeling technology. For example, the generation unit can model the operating status of the equipment, simulate the generated recovery plan based on the model, and verify its effectiveness. In this way, the generation unit can simulate the generated recovery plan to verify its effectiveness before execution and select the optimal plan by simulating the generated recovery plan.

[0083] The providing unit can estimate the user's emotions and adjust the method of providing the recovery plan based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions captured by a camera to estimate a state of tension or relaxation. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotional state. Furthermore, the providing unit can estimate the user's emotions using questionnaire results. For example, the providing unit can analyze the content of a questionnaire answered by the user to estimate the emotional state. In this way, the providing unit can adjust the method of providing the recovery plan according to the user's emotions, thereby providing the recovery plan in a more appropriate format.

[0084] The provision unit can customize the recovery plan to be provided according to the user's position and skill level. The provision unit, for example, provides a recovery plan that includes only necessary information according to the user's position. For example, the provision unit can provide an overview recovery plan to managers and a recovery plan that includes detailed procedures to technical staff. The provision unit can also provide a recovery plan that includes detailed procedures according to the user's skill level. For example, the provision unit can provide a recovery plan that includes detailed procedures to beginners and a recovery plan that includes simple procedures to experienced staff. Furthermore, the provision unit can provide a recovery plan with an appropriate level of difficulty according to the user's position and skill level. For example, the provision unit can provide an overview recovery plan to managers and a recovery plan that includes detailed procedures to technical staff. This allows the provision unit to customize the recovery plan according to the user's position and skill level, thereby enabling more appropriate response.

[0085] The providing unit monitors the execution status of the provided recovery plan and can make corrections in real time as necessary. The providing unit, for example, monitors the execution status of the provided recovery plan in real time. For example, the providing unit can monitor the execution status of the recovery plan in real time using sensors or monitoring tools. The providing unit can also detect a problem that occurs during the execution of the provided recovery plan and make immediate corrections. For example, the providing unit can detect a problem that occurs during the execution of the recovery plan and make corrections in real time. Furthermore, the providing unit can make corrections to reflect the results of the execution of the provided recovery plan in the next plan based on the execution results of the provided recovery plan. For example, the providing unit can analyze the execution results of the recovery plan and make corrections to reflect the results in the next plan. In this way, the providing unit can monitor the execution status of the provided recovery plan and make corrections in real time as necessary, enabling a rapid response.

[0086] The providing unit can estimate the user's emotions and adjust the timing of providing the recovery plan based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions captured by a camera to estimate a state of tension or relaxation. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotional state. Furthermore, the providing unit can estimate the user's emotions using questionnaire results. For example, the providing unit can analyze the content of a questionnaire answered by the user to estimate the emotional state. In this way, the providing unit can adjust the timing of providing the recovery plan according to the user's emotions, thereby providing the recovery plan at a more appropriate timing.

[0087] The providing unit can share the provided recovery plan with other users and respond jointly. The providing unit, for example, shares the provided recovery plan with team members and respond jointly. For example, the providing unit can share the provided recovery plan with team members and respond jointly. The providing unit can also share the provided recovery plan with other departments and respond in cooperation. For example, the providing unit can share the provided recovery plan with other departments and respond in cooperation. Furthermore, the providing unit can share the provided recovery plan with external experts and receive professional advice. For example, the providing unit can share the provided recovery plan with external experts and receive professional advice. In this way, the providing unit can share the provided recovery plan with other users, thereby responding quickly and effectively in cooperation.

[0088] The providing unit can save the history of the provided recovery plans for future reference. The providing unit, for example, saves the history of the provided recovery plans in a database for future reference. For example, the providing unit can save the history of the provided recovery plans in a database for future reference. The providing unit can also record the execution results of the provided recovery plans and reflect them in the next plan. For example, the providing unit can record the execution results of the provided recovery plans and reflect them in the next plan. Furthermore, the providing unit can analyze the history of the provided recovery plans and find areas for improvement. For example, the providing unit can analyze the history of the provided recovery plans and find areas for improvement. In this way, by saving the history of the provided recovery plans, the providing unit can use it for future reference and enable more effective responses.

[0089] The providing unit can estimate the user's emotions and adjust the timing of providing the recovery plan based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions captured by a camera to estimate a state of tension or relaxation. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotional state. Furthermore, the providing unit can estimate the user's emotions using questionnaire results. For example, the providing unit can analyze the content of a questionnaire answered by the user to estimate the emotional state. In this way, the providing unit can adjust the timing of providing the recovery plan according to the user's emotions, thereby providing the recovery plan at a more appropriate timing. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, storage unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about facility equipment using sensors or a manual input device of the smart device 14. The storage unit stores the information in, for example, the database 24 of the data processing device 12. For example, the generation unit analyzes the information stored by the specific processing unit 290 of the data processing device 12 and generates an optimal recovery plan. The provision unit provides the recovery plan to the user, for example, using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, storage unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about facility equipment using a sensor or a manual input device of the smart glasses 214. The storage unit stores the information in, for example, the database 24 of the data processing device 12. For example, the generation unit analyzes the information stored by, for example, the specific processing unit 290 of the data processing device 12, and generates an optimal recovery plan. The provision unit provides the recovery plan to the user, for example, using an output device of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, storage unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information about facility equipment using a sensor or a manual input device of the headset type terminal 314. The storage unit stores the information in, for example, the database 24 of the data processing device 12. For example, the generation unit analyzes the information stored by the specific processing unit 290 of the data processing device 12 and generates an optimal recovery plan. The provision unit provides the recovery plan to the user, for example, using an output device of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, storage unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about facility equipment using sensors or manual input devices of the robot 414. The storage unit stores the information in, for example, the database 24 of the data processing device 12. For example, the generation unit analyzes the information stored by the specific processing unit 290 of the data processing device 12 and generates an optimal recovery plan. The provision unit provides the recovery plan to a user using, for example, an output device of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can monitor the operating status of facility equipment in real time and automatically collect information when an abnormality is detected. For example, the collection unit can use voltage sensors and current sensors to monitor the voltage and current of power supply equipment. The collection unit can also use temperature sensors and humidity sensors to monitor the temperature and humidity of air conditioning equipment. Furthermore, the collection unit can use network monitoring tools to monitor the connection status of communication network devices. In this way, the collection unit can detect abnormalities in facility equipment in real time and automatically collect information to respond quickly.

[0092] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit can analyze the user's facial expressions captured by a camera to estimate the user's stress or relaxation state. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can also estimate the user's emotions using questionnaire results. This allows the collection unit to adjust the timing of information collection according to the user's emotions, thereby reducing the burden on the user and enabling efficient information collection.

[0093] The storage unit can automatically classify the stored information and arrange it in a format that is easy to search. For example, the storage unit can classify collected information by type of equipment and arrange it in a format that is easy to search. The storage unit can also classify collected information by importance so that it can be searched preferentially. Furthermore, the storage unit can classify collected information in chronological order so that past cases can be easily searched. In this way, the storage unit can automatically classify stored information, making it easier to search for information and enabling efficient information use.

[0094] The generation unit can estimate the user's emotions and adjust the generation method of the recovery plan based on the estimated user's emotions. For example, the generation unit can analyze the user's facial expressions captured by a camera to estimate a state of relaxation or tension. The generation unit can also estimate the user's emotions using voice analysis technology. Furthermore, the generation unit can estimate the user's emotions using questionnaire results. In this way, the generation unit can provide a more appropriate recovery plan by adjusting the generation method of the recovery plan according to the user's emotions.

[0095] The providing unit can monitor the execution status of the provided recovery plan and make corrections in real time as necessary. For example, the providing unit can monitor the execution status of the recovery plan in real time using sensors and monitoring tools. The providing unit can also detect problems that occur during the execution of the provided recovery plan and make corrections immediately. Furthermore, the providing unit can make corrections to be reflected in the next plan based on the execution results of the provided recovery plan. This allows the providing unit to monitor the execution status of the provided recovery plan and make corrections in real time as necessary, enabling rapid response.

[0096] The collection department can collect relevant information from social media and news sites to understand trends in facility equipment. For example, the collection department can collect posts about facility equipment from social media such as X (formerly Twitter) and Facebook to understand trends. The collection department can also collect the latest technical information about facility equipment from news sites. Furthermore, the collection department can also identify areas for improvement in facility equipment based on the information collected from social media and news sites. In this way, the collection department can understand the latest trends in facility equipment by collecting information from social media and news sites.

[0097] The storage unit can estimate the user's emotions and adjust the update frequency of the database based on the estimated user emotions. For example, the storage unit can analyze the user's facial expressions captured by a camera to estimate the user's state of stress or relaxation. The storage unit can also estimate the user's emotions using voice analysis technology. Furthermore, the storage unit can estimate the user's emotions using survey results. This allows the storage unit to adjust the update frequency of the database according to the user's emotions, reducing the burden on the user and enabling efficient data management.

[0098] The generation unit can construct a feedback loop to evaluate the effectiveness of past recovery plans and generate an optimal plan. For example, the generation unit can evaluate the execution results of past recovery plans and generate highly effective plans with priority. The generation unit can also analyze failure cases of past recovery plans and generate plans that reflect improvements. Furthermore, the generation unit can evaluate the execution time of past recovery plans and generate plans that allow for quick response. In this way, the generation unit can generate more effective recovery plans by evaluating the effectiveness of past recovery plans.

[0099] The providing unit can estimate the user's emotions and adjust the method of providing the recovery plan based on the estimated user's emotions. For example, the providing unit can analyze the user's facial expressions captured by a camera to estimate the user's state of tension or relaxation. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit can estimate the user's emotions using questionnaire results. In this way, the providing unit can adjust the method of providing the recovery plan according to the user's emotions, thereby providing the recovery plan in a more appropriate format.

[0100] The provision department can share the provided recovery plan with other users and work together to address the issue. For example, the provision department can share the provided recovery plan with members of its team and work together to address the issue. The provision department can also share the provided recovery plan with other departments and work together to address the issue. Furthermore, the provision department can share the provided recovery plan with external experts and receive expert advice. In this way, by sharing the provided recovery plan with other users, the provision department can work together to address the issue quickly and effectively.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects information about facility equipment. The collection unit collects information such as specifications, procedures, and past cases about facility equipment. The collection unit can also collect information automatically using sensors. For example, the collection unit can collect information about the operating status of air conditioning equipment using temperature and humidity sensors. The collection unit can also manually input information. For example, the collection unit can collect information manually entered by maintenance staff. Step 2: The storage unit stores the information collected by the collection unit in a database. The storage unit can store information using, for example, a relational database or a NoSQL database. The storage unit can also manage the information based on the storage format and storage period. For example, the storage unit can store the collected information in JSON format and automatically delete it after a certain period of time. Step 3: The generation unit analyzes the information accumulated by the accumulation unit and generates an optimal recovery plan in the event of an emergency. The generation unit can analyze the information using, for example, data mining, statistical analysis, or machine learning algorithms. The generation unit can also generate a recovery plan using a generation AI. For example, the generation unit can input past cases and procedure manuals into the generation AI to generate an optimal recovery plan. Step 4: The provisioning unit provides the recovery plan generated by the generation unit to the user. The provisioning unit can provide the recovery plan by methods such as email, dashboard display, and alert notification. The provisioning unit can also customize the recovery plan according to the user's position and skill level. For example, the provisioning unit can provide a high-level recovery plan to managers and a recovery plan with detailed procedures to technical staff.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed 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 an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed 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 an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about facility equipment; a storage unit that stores the information collected by the collection unit in a database; a generation unit that analyzes the information stored by the storage unit and generates an appropriate recovery plan in an emergency; a providing unit that provides the recovery plan generated by the generating unit. A system characterized by:

2. The collecting unit Collect facility equipment specifications or procedures, as well as information on past cases 2. The system of claim 1.

3. The storage unit is The information collected by the collection unit is stored in a database.

2. The system of claim 1.

4. The generation unit Analyzing the information stored by the storage unit and generating an appropriate recovery plan in the event of an emergency 2. The system of claim 1.

5. The providing unit The recovery plan generated by the generation unit is provided to a user.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Monitors the operation status of facility equipment in real time and automatically collects information when an abnormality is detected 2. The system of claim 1.

8. The collecting unit Dynamically change the type of information collected depending on the type and importance of the equipment 2. The system of claim 1.

Images