system

Abstract

The system according to this embodiment aims to calculate the probability of server failure and propose preventive maintenance. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a plan generation unit, and an evaluation unit. The collection unit collects server log data and at least one of the following information: traffic, temperature, and abnormal noise. The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. The proposal unit proposes preventive maintenance based on the probability of failure calculated by the analysis unit. The plan generation unit generates a replacement plan based on the preventive maintenance proposed by the proposal unit. The evaluation unit evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit and improves the predictive data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the prediction of server failures and the proposal of preventive maintenance are not sufficiently carried out, and there is a risk of sudden failure response being required.

[0005] The system according to the embodiment aims to calculate the failure probability of a server and propose preventive maintenance.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a plan generation unit, and an evaluation unit. The collection unit collects server log data and at least one of the following: traffic, temperature, and abnormal noise. The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. The proposal unit proposes preventive maintenance based on the probability of failure calculated by the analysis unit. The plan generation unit generates a replacement plan based on the preventive maintenance proposed by the proposal unit. The evaluation unit evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit and improves the predictive data. [Effects of the Invention]

[0007] The system according to this embodiment can calculate the probability of server failure and propose preventive maintenance. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses an AI agent to streamline server failure prediction and maintenance response. This system collects information such as server log data, traffic, temperature, and unusual noises from a wide range of sensors. Next, the AI ​​agent analyzes the collected data and calculates the probability of failure based on past failure knowledge. If the probability of failure exceeds a certain level, the AI ​​agent proposes preventive maintenance and generates a replacement plan. This replacement plan includes checking spare parts, coordinating with vendors, and creating work tickets. Furthermore, the system evaluates the condition and operating status of the equipment after replacement as feedback and optimizes the prediction data to improve the accuracy of future predictions. This mechanism can reduce sudden replacement responses, streamline maintenance response, and reduce the risk of service impact. For example, information such as server log data, traffic, temperature, and unusual noises is collected from a wide range of sensors. At this time, a long-term dataset is constructed that includes data fluctuations over time. For example, data is collected when the server temperature exceeds a certain range or when unusual noises occur. This allows for a detailed understanding of the server's condition. Next, the AI ​​agent analyzes the collected data. The AI ​​agent calculates the probability of failure based on past failure knowledge. For example, if a specific pattern is detected, the probability of an anomaly occurring is calculated based on that pattern. This allows for the prediction of future failures. If the failure probability exceeds a certain level, the AI ​​agent proposes preventive maintenance. Specifically, it automatically performs tasks such as checking spare parts, coordinating with vendors, and creating work tickets. This optimizes the preparation and timing of replacement work, minimizing human intervention. For example, for a server predicted to fail, the system checks the inventory of spare parts and places orders with vendors if necessary. It also creates a replacement work schedule and issues work tickets. This reduces the need for sudden replacements and improves the efficiency of maintenance. Furthermore, the system evaluates the status and operational performance of equipment after replacement as feedback to optimize predictive data. For example, it monitors the operational status of a server after replacement and collects data if an anomaly occurs.This allows for continuous improvement of the predictive model, thereby enhancing future prediction accuracy. This mechanism reduces the need for unexpected replacements, streamlines maintenance, and mitigates the risk of service disruption. For example, preventive maintenance reduces the number of alarms triggered, lessening the burden on monitoring teams. It also reduces the burden on maintenance teams in emergency response and replacement planning. This enables stable service delivery, providing users with peace of mind. As a result, systems using AI agents can streamline server failure prediction and maintenance, reducing the risk of service disruption.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a plan generation unit, and an evaluation unit. The collection unit collects server log data and at least one of the following information: traffic, temperature, and abnormal noise. For example, the collection unit collects server log data. The collection unit may also collect server traffic data. For example, the collection unit may collect server temperature data. For example, the collection unit may collect server abnormal noise data. The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. For example, the analysis unit analyzes the collected log data and calculates the probability of failure. For example, the analysis unit may also analyze the collected traffic data and calculate the probability of failure. For example, the analysis unit may also analyze the collected temperature data and calculate the probability of failure. For example, the analysis unit may also analyze the collected abnormal noise data and calculate the probability of failure. The proposal unit proposes preventive maintenance based on the probability of failure calculated by the analysis unit. For example, the proposal unit proposes preventive maintenance when the probability of failure exceeds a certain level. The proposal unit can, for example, propose preventive maintenance when the probability of failure is high. The proposal unit can, for example, propose preventive maintenance when the probability of failure is moderate. The plan generation unit generates a replacement plan based on the preventive maintenance proposed by the proposal unit. The plan generation unit can, for example, verify spare parts. The plan generation unit can, for example, coordinate with vendors. The plan generation unit can, for example, create work tickets. The evaluation unit evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit and improves the predictive data. The evaluation unit can, for example, evaluate the condition of the equipment after replacement. The evaluation unit can, for example, evaluate the operating status of the equipment after replacement. The evaluation unit can, for example, evaluate the condition and operating status of the equipment after replacement and improve the predictive data. As a result, the system according to the embodiment can streamline server failure prediction and maintenance response and reduce the risk of service impact.

[0030] The data collection unit collects server log data and at least one of the following information: traffic, temperature, and abnormal noise. Specifically, server log data includes system operation history, error messages, and user access history. This allows for a detailed understanding of the server's operating status and the occurrence of abnormalities. Traffic data shows the amount of network traffic and the status of packet transmission and reception, and is used to monitor the server's load status and network congestion. Temperature data is obtained from temperature sensors inside the server and is important for evaluating the server's cooling status and the risk of overheating. Abnormal noise data detects abnormal noises generated from mechanical parts such as fans and hard drives inside the server and is used to detect signs of mechanical failure early. The data collection unit collects this data in real time and transmits it to a central database. Data collection is performed at regular intervals, and also immediately when an abnormality is detected. This allows the data collection unit to always have up-to-date information on the server's operating status and to respond quickly. Furthermore, the data collection unit centrally manages the data and can share information in cooperation with other systems and departments. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. Specifically, it analyzes the collected log data and analyzes the system's operation history and error message patterns to detect signs of failure. For example, it can find precursors to failure from past data, such as when a specific error message occurs frequently or when an anomaly occurs after a specific operation is performed. In the analysis of traffic data, it analyzes the network traffic volume and packet transmission and reception status to evaluate the server load state and network congestion. This makes it possible to predict the risk of failure due to overload. In the analysis of temperature data, it monitors temperature fluctuations inside the server and evaluates the risk of overheating. If an abnormal temperature rise is detected, it may be due to a cooling system failure or overload, allowing for early countermeasures to be taken. In the analysis of abnormal noise data, it detects abnormal noises generated from mechanical parts inside the server to detect signs of mechanical failure early. For example, if abnormal noises from a fan or hard disk are detected, failure of these parts can be predicted. The analysis unit comprehensively analyzes this data and calculates the probability of failure. By utilizing AI-powered machine learning algorithms, it is possible to learn failure patterns from past data and make more accurate failure predictions. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the server failure risk in real time.

[0032] The proposal department proposes preventive maintenance based on the failure probability calculated by the analysis department. Specifically, it proposes preventive maintenance when the failure probability exceeds a certain level. For example, if the failure probability is high, it can propose immediate inspection or replacement of parts. If the failure probability is moderate, it can propose regular inspections or enhanced monitoring. Based on the failure probability data provided by the analysis department, the proposal department selects the optimal preventive maintenance measures and presents a concrete action plan. For example, if replacement of a specific part is necessary, it provides the timing of the replacement and a list of necessary parts. Also, if regular inspections are necessary, it proposes an inspection schedule and inspection items. When proposing preventive maintenance, the proposal department also considers past maintenance history and recommendations from vendors. This allows the proposal department to propose optimal preventive maintenance measures and reduce the risk of server failure. Furthermore, the proposal department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. For example, after implementing preventive maintenance, it reviews and improves the proposals based on user feedback. This allows the proposal department to provide users with optimal preventive maintenance measures and improve the operational efficiency of the servers.

[0033] The planning generation unit generates replacement plans based on the preventive maintenance proposed by the proposal unit. Specifically, it checks for spare parts and verifies that all necessary parts are available. If spare parts are insufficient, it places orders with vendors to secure the necessary parts. When coordinating with vendors, it selects the most suitable vendor considering factors such as delivery time, price, and quality. When creating work tickets, it provides the assigned worker with a detailed schedule and procedure for the replacement work. The work tickets include a list of tools and parts required for the replacement work, detailed work procedures, and points to note during the work. Based on this information, the planning generation unit generates an efficient and effective replacement plan to support the smooth execution of the work. Furthermore, the planning generation unit can monitor the progress of the replacement work in real time and revise or adjust the plan as needed. For example, if an unexpected problem occurs during the replacement work, it can quickly take countermeasures to minimize delays. In addition, the planning generation unit creates a work report after the completion of the replacement work, recording the work content and results. This allows the planning generation unit to generate efficient replacement plans based on preventive maintenance proposals and improve the operational efficiency of the servers.

[0034] The evaluation unit evaluates the condition and operational status of the equipment after replacement based on the replacement plan generated by the plan generation unit, and improves the predictive data. Specifically, it evaluates the condition of the equipment after replacement and verifies that it is functioning correctly. For example, it checks whether the replaced parts are installed correctly and whether there are any abnormalities in their operation. When evaluating the operational status of the equipment after replacement, it monitors the server's operational status and performance based on data provided by the data collection unit. If an abnormality is detected, it takes prompt countermeasures to resolve the problem. The evaluation unit improves the accuracy of the predictive data by evaluating the condition and operational status of the equipment after replacement. For example, by retraining the failure prediction model based on the data after replacement, more accurate failure prediction becomes possible. Furthermore, the evaluation unit evaluates the effectiveness of the replacement work and identifies proposed preventive maintenance measures and areas for improvement in the replacement plan. In this way, the evaluation unit can improve the reliability and safety of the entire system by continuously improving the predictive data through evaluating the condition and operational status of the equipment after replacement.

[0035] The data collection unit can collect information from sensors over a wide area, including at least one of the following: server log data, traffic, temperature, and abnormal noise. For example, the data collection unit can collect server log data from sensors. The data collection unit can also collect server traffic data from sensors. For example, the data collection unit can collect server temperature data from sensors. For example, the data collection unit can also collect server abnormal noise data from sensors. This allows for a detailed understanding of the server's status. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the data collected from sensors into an AI and have the AI ​​perform data analysis.

[0036] The analysis unit can calculate failure probabilities based on past failure knowledge. For example, the analysis unit can calculate failure probabilities based on past failure data. For example, the analysis unit can also calculate failure probabilities based on a failure history database. For example, the analysis unit can also calculate failure probabilities based on failure cause analysis reports. This makes it possible to predict the probability of future failures. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past failure data into AI and have AI perform the calculation of failure probabilities.

[0037] The proposal unit can propose preventive maintenance when the probability of failure exceeds a certain level. For example, the proposal unit can propose preventive maintenance when the probability of failure exceeds 50%. For example, the proposal unit can also propose preventive maintenance when the probability of failure exceeds 70%. For example, the proposal unit can also propose preventive maintenance when the probability of failure exceeds 90%. This can reduce the need for sudden replacements. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input failure probability data into AI and have the AI ​​execute preventive maintenance proposals.

[0038] The planning generation unit can perform at least one of the following: checking spare parts, coordinating with vendors, or creating work tickets. For example, the planning generation unit can check spare parts. For example, the planning generation unit can also coordinate with vendors. For example, the planning generation unit can also create work tickets. This optimizes the preparation and timing of replacement work and minimizes human intervention. Some or all of the above processes in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input spare parts inventory data into the AI ​​and have the AI ​​perform the spare parts check.

[0039] The evaluation unit can evaluate the condition and operating status of the equipment after replacement and optimize the predictive data. For example, the evaluation unit evaluates the condition of the equipment after replacement. The evaluation unit can also evaluate the operating status of the equipment after replacement. The evaluation unit can also evaluate the condition and operating status of the equipment after replacement and optimize the predictive data. This allows for continuous improvement of the predictive model and enhances future prediction accuracy. Some or all of the above-described processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input data from the equipment after replacement into the AI ​​and have the AI ​​perform the evaluation and optimization of the predictive data.

[0040] The data collection unit can dynamically change the collection frequency according to the server's operating status. For example, if the server load is high, the collection unit can reduce the collection frequency to maintain system stability. For example, if the server load is low, the collection unit can increase the collection frequency to obtain more detailed data. The collection unit can also adjust the collection frequency in real time according to the server's operating status. This allows the system to maintain stability by adjusting the collection frequency according to the server's operating status. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input server operating status data into the AI ​​and have the AI ​​dynamically change the collection frequency.

[0041] The data collection unit can prioritize the collection of specific sensor data when an anomaly occurs. For example, when an anomaly is detected, the data collection unit can prioritize the collection of data from a temperature sensor. For example, when an anomaly occurs, the data collection unit can also prioritize the collection of data from an abnormal noise sensor. For example, when an anomaly occurs, the data collection unit can also prioritize the collection of traffic data. This allows for a rapid response by prioritizing the collection of important data when an anomaly occurs. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input anomaly detection data into the AI ​​and have the AI ​​perform the priority collection of specific sensor data.

[0042] The data collection unit can collect data according to environmental conditions, taking into account the server's geographical location. For example, if the server is located in a hot and humid region, the data collection unit will prioritize collecting temperature and humidity data. For example, if the server is located in a cold region, the data collection unit can prioritize collecting temperature and power consumption data. For example, if the server is located in an earthquake-prone area, the data collection unit can prioritize collecting vibration sensor data. By considering the server's geographical location when collecting data, it is possible to collect appropriate data according to environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input the server's geographical location data into the AI ​​and have the AI ​​perform data collection according to environmental conditions.

[0043] The data collection unit can analyze social media trends and collect relevant data. For example, if a problem with a particular server is being discussed on social media, the data collection unit will prioritize collecting data from that server. For example, if traffic in a particular region is increasing on social media, the data collection unit can also prioritize collecting data from servers in that region. For example, if a particular anomaly is being reported on social media, the data collection unit can also prioritize collecting data related to that anomaly. This allows for the collection of relevant data and rapid response by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media trend data into an AI and have the AI ​​perform the collection of relevant data.

[0044] The analysis unit can optimize analysis methods for detecting specific patterns based on past failure data. For example, the analysis unit can optimize analysis methods for detecting specific abnormal patterns based on past failure data. The analysis unit can also optimize analysis methods for detecting patterns that are precursors to failures based on past failure data. The analysis unit can also optimize analysis methods for identifying the cause of failures based on past failure data. By optimizing analysis methods based on past failure data, precursors to failures can be detected early. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past failure data into AI and have AI perform the detection of specific patterns.

[0045] The analysis unit can update analysis results in real time when an anomaly is detected. For example, when an anomaly is detected, the analysis unit updates the analysis results in real time to provide the latest information. The analysis unit can also update analysis results in real time when an anomaly occurs, enabling a rapid response. For example, when an anomaly occurs, the analysis unit can update analysis results in real time to identify the cause of the anomaly. This enables a rapid response by updating analysis results in real time when an anomaly is detected. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input anomaly detection data into the AI ​​and have the AI ​​perform real-time updates of the analysis results.

[0046] The analysis unit can analyze region-specific failure patterns by considering the server's geographical location. For example, if the server is located in a hot and humid region, the analysis unit can analyze region-specific failure patterns. For example, if the server is located in a cold region, the analysis unit can also analyze region-specific failure patterns. For example, if the server is located in an earthquake-prone area, the analysis unit can also analyze region-specific failure patterns. By analyzing failure patterns while considering the server's geographical location, region-specific failures can be detected early. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the server's geographical location data into the AI ​​and have the AI ​​perform the analysis of region-specific failure patterns.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. For example, the analysis unit can improve its analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis by referring to relevant databases. The analysis unit can also optimize its analysis method by referring to relevant research results. In this way, the accuracy of the analysis can be improved by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from relevant literature and databases into the AI ​​and have the AI ​​perform the improvement of the analysis accuracy.

[0048] The proposal unit can propose specific preventive maintenance procedures when the probability of failure is high. For example, the proposal unit can propose specific preventive maintenance procedures when the probability of failure is high. For example, the proposal unit can also propose a list of necessary spare parts when the probability of failure is high. For example, the proposal unit can also propose a procedure for coordinating with vendors when the probability of failure is high. In this way, by proposing specific preventive maintenance procedures when the probability of failure is high, sudden failures can be prevented. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input failure probability data into AI and have the AI ​​execute the proposal of specific preventive maintenance procedures.

[0049] The proposal unit can automatically allocate the necessary resources based on the proposed content. For example, the proposal unit can automatically allocate the necessary spare parts based on the proposed content. The proposal unit can also automatically allocate the necessary personnel based on the proposed content. The proposal unit can also automatically allocate the necessary equipment based on the proposed content. This enables efficient maintenance response by automatically allocating the necessary resources based on the proposed content. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the data of the proposed content into the AI ​​and have the AI ​​perform the allocation of the necessary resources.

[0050] The proposal unit can make region-specific conservation suggestions by considering the server's geographical location. For example, if the server is located in a hot and humid region, the proposal unit can make region-specific conservation suggestions. For example, if the server is located in a cold region, the proposal unit can also make region-specific conservation suggestions. For example, if the server is located in an earthquake-prone area, the proposal unit can also make region-specific conservation suggestions. In this way, by making conservation suggestions while considering the server's geographical location, region-specific problems can be addressed. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the server's geographical location data into AI and have the AI ​​execute region-specific conservation suggestions.

[0051] The proposal unit can analyze social media trends and make relevant maintenance suggestions. For example, if a problem with a particular server is being discussed on social media, the proposal unit can make a maintenance suggestion for that server. For example, if traffic in a particular region is increasing on social media, the proposal unit can also make a maintenance suggestion for servers in that region. For example, if a particular anomaly is being reported on social media, the proposal unit can also make a maintenance suggestion for that anomaly. This allows for quick responses by making relevant maintenance suggestions through the analysis of social media trends. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input social media trend data into AI and have the AI ​​execute relevant maintenance suggestions.

[0052] The planning generation unit can check the inventory status of spare parts in real time and generate an optimal replacement plan. For example, the planning generation unit can check the inventory status of spare parts in real time and generate an optimal replacement plan. For example, if the inventory of spare parts is low, the planning generation unit can adjust the replacement plan and order the necessary spare parts. For example, the planning generation unit can dynamically change the replacement plan according to the inventory status of spare parts. This makes it possible to generate an optimal replacement plan by checking the inventory status of spare parts in real time. Some or all of the above processes in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input spare parts inventory data into AI and have the AI ​​generate an optimal replacement plan.

[0053] The planning generation unit can automate coordination with vendors and optimize the replacement work schedule. For example, the planning generation unit can automate coordination with vendors and optimize the replacement work schedule. The planning generation unit can also determine the optimal timing for replacement work, taking into account the vendor's schedule. The planning generation unit can also perform coordination with vendors in real time and dynamically change the replacement work schedule. By automating coordination with vendors, the replacement work schedule can be optimized, enabling efficient maintenance response. Some or all of the above processes in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input vendor schedule data into AI and have the AI ​​perform coordination and schedule optimization.

[0054] The plan generation unit can generate region-specific exchange plans by considering the geographical location information of the server. For example, if the server is located in a hot and humid region, the plan generation unit will generate a region-specific exchange plan. For example, if the server is located in a cold region, the plan generation unit can also generate a region-specific exchange plan. For example, if the server is located in an earthquake-prone area, the plan generation unit can also generate a region-specific exchange plan. In this way, by generating exchange plans while considering the geographical location information of the server, it is possible to address region-specific problems. Some or all of the above processing in the plan generation unit may be performed using AI or not. For example, the plan generation unit can input the server's geographical location data into the AI ​​and have the AI ​​perform the generation of region-specific exchange plans.

[0055] The planning generation unit can improve planning accuracy by referring to relevant literature and databases. For example, the planning generation unit can improve planning accuracy by referring to relevant literature. The planning generation unit can also improve planning accuracy by referring to relevant databases. The planning generation unit can also improve planning accuracy by referring to relevant research results. In this way, planning accuracy can be improved by referring to relevant literature and databases. Some or all of the above processing in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input data from relevant literature and databases into the AI ​​and have the AI ​​perform the improvement of planning accuracy.

[0056] The evaluation unit can monitor the operating status of the replaced equipment in real time and update the evaluation results. For example, the evaluation unit can monitor the operating status of the replaced equipment in real time and update the evaluation results. For example, if an abnormality occurs in the replaced equipment, the evaluation unit can collect the data and update the evaluation results. For example, the evaluation unit can continuously monitor the operating status of the replaced equipment and update the evaluation results. This enables a rapid response by monitoring the operating status of the replaced equipment in real time. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the operating status data of the replaced equipment into the AI ​​and have the AI ​​update the evaluation results.

[0057] The evaluation unit can continuously improve the predictive model based on the evaluation results. For example, the evaluation unit can continuously improve the predictive model based on the evaluation results. The evaluation unit can also use the evaluation results as feedback to improve the accuracy of the predictive model. For example, the evaluation unit can adjust the parameters of the predictive model based on the evaluation results. This allows for improved prediction accuracy by continuously improving the predictive model based on the evaluation results. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the evaluation result data into AI and have AI perform improvements to the predictive model.

[0058] The evaluation unit can set region-specific evaluation criteria, taking into account the server's geographical location. For example, if the server is located in a hot and humid region, the evaluation unit can set region-specific evaluation criteria. For example, if the server is located in a cold region, the evaluation unit can also set region-specific evaluation criteria. For example, if the server is located in an earthquake-prone area, the evaluation unit can also set region-specific evaluation criteria. This allows for addressing region-specific issues by setting evaluation criteria while considering the server's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input the server's geographical location data into the AI ​​and have the AI ​​perform the setting of region-specific evaluation criteria.

[0059] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature and databases. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant databases. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant research results. In this way, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature and databases. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input data from relevant literature and databases into the AI ​​and have the AI ​​perform the improvement of evaluation accuracy.

[0060] The evaluation unit can optimize the next maintenance plan based on the evaluation results. For example, the evaluation unit can optimize the next maintenance plan based on the evaluation results. The evaluation unit can also use the evaluation results as feedback to improve the accuracy of the next maintenance plan. For example, the evaluation unit can adjust the parameters of the next maintenance plan based on the evaluation results. By optimizing the next maintenance plan based on the evaluation results, the accuracy of maintenance responses can be improved. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the evaluation result data into AI and have AI perform the optimization of the next maintenance plan.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can dynamically change the type of data collected according to the server's operating status. For example, when the server load is high, it can collect only essential data to maintain system stability. When the server load is low, it can collect detailed data to perform more accurate analysis. Furthermore, it can adjust the type of data collected in real time according to the server's operating status. This allows the system to maintain stability by adjusting the type of data collected according to the server's operating status.

[0063] The planning generation unit can check the inventory status of spare parts in real time and generate the optimal replacement plan. For example, it can check the inventory status of spare parts in real time and generate the optimal replacement plan. If the inventory of spare parts is low, it can adjust the replacement plan and order the necessary spare parts. It can also dynamically change the replacement plan according to the inventory status of spare parts. In this way, by checking the inventory status of spare parts in real time, the optimal replacement plan can be generated.

[0064] The evaluation unit can monitor the operational status of the replaced equipment in real time and update the evaluation results. For example, it can monitor the operational status of the replaced equipment in real time and update the evaluation results. If an abnormality occurs in the replaced equipment, it can also collect that data and update the evaluation results. It can also continuously monitor the operational status of the replaced equipment and update the evaluation results. This enables a rapid response by monitoring the operational status of the replaced equipment in real time.

[0065] The analysis unit can optimize analysis methods to detect specific patterns based on past failure data. For example, it can optimize analysis methods to detect specific abnormal patterns based on past failure data. It can also optimize analysis methods to detect patterns that indicate impending failures based on past failure data. It can also optimize analysis methods to identify the cause of failures based on past failure data. As a result, by optimizing analysis methods based on past failure data, it is possible to detect impending failures at an early stage.

[0066] The proposal function can consider the server's geographical location to provide region-specific maintenance suggestions. For example, if the server is located in a hot and humid region, it can provide region-specific maintenance suggestions. If the server is located in a cold region, it can also provide region-specific maintenance suggestions. If the server is located in an earthquake-prone area, it can also provide region-specific maintenance suggestions. In this way, by providing maintenance suggestions that take the server's geographical location into consideration, it is possible to address region-specific problems.

[0067] The evaluation unit can optimize the next maintenance plan based on the evaluation results. For example, it can optimize the next maintenance plan based on the evaluation results. It can also use the evaluation results as feedback to improve the accuracy of the next maintenance plan. It can also adjust the parameters of the next maintenance plan based on the evaluation results. In this way, the accuracy of maintenance responses can be improved by optimizing the next maintenance plan based on the evaluation results.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The collection unit collects server log data and at least one of the following information: traffic, temperature, or unusual noises. For example, the collection unit can collect server log data, traffic data, temperature data, and unusual noise data. Step 2: The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. For example, it can analyze collected log data, traffic data, temperature data, and abnormal noise data to calculate the probability of failure. Step 3: The proposal unit proposes preventive maintenance based on the failure probability calculated by the analysis unit. For example, preventive maintenance can be proposed when the failure probability exceeds a certain level, is high, or is moderate. Step 4: The planning generation unit generates a replacement plan based on the preventive maintenance proposed by the proposal unit. For example, it can check spare parts, coordinate with vendors, and create work tickets. Step 5: The evaluation unit evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit, and improves the predictive data. For example, it can evaluate the condition and operating status of the equipment after replacement and improve the predictive data.

[0070] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to streamline server failure prediction and maintenance response. This system collects information such as server log data, traffic, temperature, and unusual noises from a wide range of sensors. Next, the AI ​​agent analyzes the collected data and calculates the probability of failure based on past failure knowledge. If the probability of failure exceeds a certain level, the AI ​​agent proposes preventive maintenance and generates a replacement plan. This replacement plan includes checking spare parts, coordinating with vendors, and creating work tickets. Furthermore, the system evaluates the condition and operating status of the equipment after replacement as feedback and optimizes the prediction data to improve the accuracy of future predictions. This mechanism can reduce sudden replacement responses, streamline maintenance response, and reduce the risk of service impact. For example, information such as server log data, traffic, temperature, and unusual noises is collected from a wide range of sensors. At this time, a long-term dataset is constructed that includes data fluctuations over time. For example, data is collected when the server temperature exceeds a certain range or when unusual noises occur. This allows for a detailed understanding of the server's condition. Next, the AI ​​agent analyzes the collected data. The AI ​​agent calculates the probability of failure based on past failure knowledge. For example, if a specific pattern is detected, the probability of an anomaly occurring is calculated based on that pattern. This allows for the prediction of future failures. If the failure probability exceeds a certain level, the AI ​​agent proposes preventive maintenance. Specifically, it automatically performs tasks such as checking spare parts, coordinating with vendors, and creating work tickets. This optimizes the preparation and timing of replacement work, minimizing human intervention. For example, for a server predicted to fail, the system checks the inventory of spare parts and places orders with vendors if necessary. It also creates a replacement work schedule and issues work tickets. This reduces the need for sudden replacements and improves the efficiency of maintenance. Furthermore, the system evaluates the status and operational performance of equipment after replacement as feedback to optimize predictive data. For example, it monitors the operational status of a server after replacement and collects data if an anomaly occurs.This allows for continuous improvement of the predictive model, thereby enhancing future prediction accuracy. This mechanism reduces the need for unexpected replacements, streamlines maintenance, and mitigates the risk of service disruption. For example, preventive maintenance reduces the number of alarms triggered, lessening the burden on monitoring teams. It also reduces the burden on maintenance teams in emergency response and replacement planning. This enables stable service delivery, providing users with peace of mind. As a result, systems using AI agents can streamline server failure prediction and maintenance, reducing the risk of service disruption.

[0071] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a plan generation unit, and an evaluation unit. The collection unit collects server log data and at least one of the following information: traffic, temperature, and abnormal noise. For example, the collection unit collects server log data. The collection unit may also collect server traffic data. For example, the collection unit may collect server temperature data. For example, the collection unit may collect server abnormal noise data. The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. For example, the analysis unit analyzes the collected log data and calculates the probability of failure. For example, the analysis unit may also analyze the collected traffic data and calculate the probability of failure. For example, the analysis unit may also analyze the collected temperature data and calculate the probability of failure. For example, the analysis unit may also analyze the collected abnormal noise data and calculate the probability of failure. The proposal unit proposes preventive maintenance based on the probability of failure calculated by the analysis unit. For example, the proposal unit proposes preventive maintenance when the probability of failure exceeds a certain level. The proposal unit can, for example, propose preventive maintenance when the probability of failure is high. The proposal unit can, for example, propose preventive maintenance when the probability of failure is moderate. The plan generation unit generates a replacement plan based on the preventive maintenance proposed by the proposal unit. The plan generation unit can, for example, verify spare parts. The plan generation unit can, for example, coordinate with vendors. The plan generation unit can, for example, create work tickets. The evaluation unit evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit and improves the predictive data. The evaluation unit can, for example, evaluate the condition of the equipment after replacement. The evaluation unit can, for example, evaluate the operating status of the equipment after replacement. The evaluation unit can, for example, evaluate the condition and operating status of the equipment after replacement and improve the predictive data. As a result, the system according to the embodiment can streamline server failure prediction and maintenance response and reduce the risk of service impact.

[0072] The data collection unit collects server log data and at least one of the following information: traffic, temperature, and abnormal noise. Specifically, server log data includes system operation history, error messages, and user access history. This allows for a detailed understanding of the server's operating status and the occurrence of abnormalities. Traffic data shows the amount of network traffic and the status of packet transmission and reception, and is used to monitor the server's load status and network congestion. Temperature data is obtained from temperature sensors inside the server and is important for evaluating the server's cooling status and the risk of overheating. Abnormal noise data detects abnormal noises generated from mechanical parts such as fans and hard drives inside the server and is used to detect signs of mechanical failure early. The data collection unit collects this data in real time and transmits it to a central database. Data collection is performed at regular intervals, and also immediately when an abnormality is detected. This allows the data collection unit to always have up-to-date information on the server's operating status and to respond quickly. Furthermore, the data collection unit centrally manages the data and can share information in cooperation with other systems and departments. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. Specifically, it analyzes the collected log data and analyzes the system's operation history and error message patterns to detect signs of failure. For example, it can find precursors to failure from past data, such as when a specific error message occurs frequently or when an anomaly occurs after a specific operation is performed. In the analysis of traffic data, it analyzes the network traffic volume and packet transmission and reception status to evaluate the server load state and network congestion. This makes it possible to predict the risk of failure due to overload. In the analysis of temperature data, it monitors temperature fluctuations inside the server and evaluates the risk of overheating. If an abnormal temperature rise is detected, it may be due to a cooling system failure or overload, allowing for early countermeasures to be taken. In the analysis of abnormal noise data, it detects abnormal noises generated from mechanical parts inside the server to detect signs of mechanical failure early. For example, if abnormal noises from a fan or hard disk are detected, failure of these parts can be predicted. The analysis unit comprehensively analyzes this data and calculates the probability of failure. By utilizing AI-powered machine learning algorithms, it is possible to learn failure patterns from past data and make more accurate failure predictions. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the server failure risk in real time.

[0074] The proposal department proposes preventive maintenance based on the failure probability calculated by the analysis department. Specifically, it proposes preventive maintenance when the failure probability exceeds a certain level. For example, if the failure probability is high, it can propose immediate inspection or replacement of parts. If the failure probability is moderate, it can propose regular inspections or enhanced monitoring. Based on the failure probability data provided by the analysis department, the proposal department selects the optimal preventive maintenance measures and presents a concrete action plan. For example, if replacement of a specific part is necessary, it provides the timing of the replacement and a list of necessary parts. Also, if regular inspections are necessary, it proposes an inspection schedule and inspection items. When proposing preventive maintenance, the proposal department also considers past maintenance history and recommendations from vendors. This allows the proposal department to propose optimal preventive maintenance measures and reduce the risk of server failure. Furthermore, the proposal department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. For example, after implementing preventive maintenance, it reviews and improves the proposals based on user feedback. This allows the proposal department to provide users with optimal preventive maintenance measures and improve the operational efficiency of the servers.

[0075] The planning generation unit generates replacement plans based on the preventive maintenance proposed by the proposal unit. Specifically, it checks for spare parts and verifies that all necessary parts are available. If spare parts are insufficient, it places orders with vendors to secure the necessary parts. When coordinating with vendors, it selects the most suitable vendor considering factors such as delivery time, price, and quality. When creating work tickets, it provides the assigned worker with a detailed schedule and procedure for the replacement work. The work tickets include a list of tools and parts required for the replacement work, detailed work procedures, and points to note during the work. Based on this information, the planning generation unit generates an efficient and effective replacement plan to support the smooth execution of the work. Furthermore, the planning generation unit can monitor the progress of the replacement work in real time and revise or adjust the plan as needed. For example, if an unexpected problem occurs during the replacement work, it can quickly take countermeasures to minimize delays. In addition, the planning generation unit creates a work report after the completion of the replacement work, recording the work content and results. This allows the planning generation unit to generate efficient replacement plans based on preventive maintenance proposals and improve the operational efficiency of the servers.

[0076] The evaluation unit evaluates the condition and operational status of the equipment after replacement based on the replacement plan generated by the plan generation unit, and improves the predictive data. Specifically, it evaluates the condition of the equipment after replacement and verifies that it is functioning correctly. For example, it checks whether the replaced parts are installed correctly and whether there are any abnormalities in their operation. When evaluating the operational status of the equipment after replacement, it monitors the server's operational status and performance based on data provided by the data collection unit. If an abnormality is detected, it takes prompt countermeasures to resolve the problem. The evaluation unit improves the accuracy of the predictive data by evaluating the condition and operational status of the equipment after replacement. For example, by retraining the failure prediction model based on the data after replacement, more accurate failure prediction becomes possible. Furthermore, the evaluation unit evaluates the effectiveness of the replacement work and identifies proposed preventive maintenance measures and areas for improvement in the replacement plan. In this way, the evaluation unit can improve the reliability and safety of the entire system by continuously improving the predictive data through evaluating the condition and operational status of the equipment after replacement.

[0077] The data collection unit can collect information from sensors over a wide area, including at least one of the following: server log data, traffic, temperature, and abnormal noise. For example, the data collection unit can collect server log data from sensors. The data collection unit can also collect server traffic data from sensors. For example, the data collection unit can collect server temperature data from sensors. For example, the data collection unit can also collect server abnormal noise data from sensors. This allows for a detailed understanding of the server's status. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the data collected from sensors into an AI and have the AI ​​perform data analysis.

[0078] The analysis unit can calculate failure probabilities based on past failure knowledge. For example, the analysis unit can calculate failure probabilities based on past failure data. For example, the analysis unit can also calculate failure probabilities based on a failure history database. For example, the analysis unit can also calculate failure probabilities based on failure cause analysis reports. This makes it possible to predict the probability of future failures. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past failure data into AI and have AI perform the calculation of failure probabilities.

[0079] The proposal unit can propose preventive maintenance when the probability of failure exceeds a certain level. For example, the proposal unit can propose preventive maintenance when the probability of failure exceeds 50%. For example, the proposal unit can also propose preventive maintenance when the probability of failure exceeds 70%. For example, the proposal unit can also propose preventive maintenance when the probability of failure exceeds 90%. This can reduce the need for sudden replacements. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input failure probability data into AI and have the AI ​​execute preventive maintenance proposals.

[0080] The planning generation unit can perform at least one of the following: checking spare parts, coordinating with vendors, or creating work tickets. For example, the planning generation unit can check spare parts. For example, the planning generation unit can also coordinate with vendors. For example, the planning generation unit can also create work tickets. This optimizes the preparation and timing of replacement work and minimizes human intervention. Some or all of the above processes in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input spare parts inventory data into the AI ​​and have the AI ​​perform the spare parts check.

[0081] The evaluation unit can evaluate the condition and operating status of the equipment after replacement and optimize the predictive data. For example, the evaluation unit evaluates the condition of the equipment after replacement. The evaluation unit can also evaluate the operating status of the equipment after replacement. The evaluation unit can also evaluate the condition and operating status of the equipment after replacement and optimize the predictive data. This allows for continuous improvement of the predictive model and enhances future prediction accuracy. Some or all of the above-described processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input data from the equipment after replacement into the AI ​​and have the AI ​​perform the evaluation and optimization of the predictive data.

[0082] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect only essential data to reduce the burden. For example, if the user is relaxed, the data collection unit can collect detailed data for more accurate analysis. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This reduces the burden on the user by adjusting the types of data collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the types of data to be collected.

[0083] The data collection unit can dynamically change the collection frequency according to the server's operating status. For example, if the server load is high, the collection unit can reduce the collection frequency to maintain system stability. For example, if the server load is low, the collection unit can increase the collection frequency to obtain more detailed data. The collection unit can also adjust the collection frequency in real time according to the server's operating status. This allows the system to maintain stability by adjusting the collection frequency according to the server's operating status. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input server operating status data into the AI ​​and have the AI ​​dynamically change the collection frequency.

[0084] The data collection unit can prioritize the collection of specific sensor data when an anomaly occurs. For example, when an anomaly is detected, the data collection unit can prioritize the collection of data from a temperature sensor. For example, when an anomaly occurs, the data collection unit can also prioritize the collection of data from an abnormal noise sensor. For example, when an anomaly occurs, the data collection unit can also prioritize the collection of traffic data. This allows for a rapid response by prioritizing the collection of important data when an anomaly occurs. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input anomaly detection data into the AI ​​and have the AI ​​perform the priority collection of specific sensor data.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit may prioritize data that can be collected quickly. This reduces the user's burden by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of data to collect.

[0086] The data collection unit can collect data according to environmental conditions, taking into account the server's geographical location. For example, if the server is located in a hot and humid region, the data collection unit will prioritize collecting temperature and humidity data. For example, if the server is located in a cold region, the data collection unit can prioritize collecting temperature and power consumption data. For example, if the server is located in an earthquake-prone area, the data collection unit can prioritize collecting vibration sensor data. By considering the server's geographical location when collecting data, it is possible to collect appropriate data according to environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input the server's geographical location data into the AI ​​and have the AI ​​perform data collection according to environmental conditions.

[0087] The data collection unit can analyze social media trends and collect relevant data. For example, if a problem with a particular server is being discussed on social media, the data collection unit will prioritize collecting data from that server. For example, if traffic in a particular region is increasing on social media, the data collection unit can also prioritize collecting data from servers in that region. For example, if a particular anomaly is being reported on social media, the data collection unit can also prioritize collecting data related to that anomaly. This allows for the collection of relevant data and rapid response by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media trend data into an AI and have the AI ​​perform the collection of relevant data.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis algorithm to provide results quickly. For example, if the user is relaxed, the analysis unit can also use a detailed analysis algorithm to provide highly accurate results. For example, if the user is in a hurry, the analysis unit can adjust the algorithm parameters to perform a rapid analysis. This allows for the provision of rapid and highly accurate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the analysis algorithm.

[0089] The analysis unit can optimize analysis methods for detecting specific patterns based on past failure data. For example, the analysis unit can optimize analysis methods for detecting specific abnormal patterns based on past failure data. The analysis unit can also optimize analysis methods for detecting patterns that are precursors to failures based on past failure data. The analysis unit can also optimize analysis methods for identifying the cause of failures based on past failure data. By optimizing analysis methods based on past failure data, precursors to failures can be detected early. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past failure data into AI and have AI perform the detection of specific patterns.

[0090] The analysis unit can update analysis results in real time when an anomaly is detected. For example, when an anomaly is detected, the analysis unit updates the analysis results in real time to provide the latest information. The analysis unit can also update analysis results in real time when an anomaly occurs, enabling a rapid response. For example, when an anomaly occurs, the analysis unit can update analysis results in real time to identify the cause of the anomaly. This enables a rapid response by updating analysis results in real time when an anomaly is detected. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input anomaly detection data into the AI ​​and have the AI ​​perform real-time updates of the analysis results.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0092] The analysis unit can analyze region-specific failure patterns by considering the server's geographical location. For example, if the server is located in a hot and humid region, the analysis unit can analyze region-specific failure patterns. For example, if the server is located in a cold region, the analysis unit can also analyze region-specific failure patterns. For example, if the server is located in an earthquake-prone area, the analysis unit can also analyze region-specific failure patterns. By analyzing failure patterns while considering the server's geographical location, region-specific failures can be detected early. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the server's geographical location data into the AI ​​and have the AI ​​perform the analysis of region-specific failure patterns.

[0093] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. For example, the analysis unit can improve its analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis by referring to relevant databases. The analysis unit can also optimize its analysis method by referring to relevant research results. In this way, the accuracy of the analysis can be improved by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from relevant literature and databases into the AI ​​and have the AI ​​perform the improvement of the analysis accuracy.

[0094] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can make concise and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can make suggestions that can be implemented quickly. By adjusting the suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the suggestions.

[0095] The proposal unit can propose specific preventive maintenance procedures when the probability of failure is high. For example, the proposal unit can propose specific preventive maintenance procedures when the probability of failure is high. For example, the proposal unit can also propose a list of necessary spare parts when the probability of failure is high. For example, the proposal unit can also propose a procedure for coordinating with vendors when the probability of failure is high. In this way, by proposing specific preventive maintenance procedures when the probability of failure is high, sudden failures can be prevented. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input failure probability data into AI and have the AI ​​execute the proposal of specific preventive maintenance procedures.

[0096] The proposal unit can automatically allocate the necessary resources based on the proposed content. For example, the proposal unit can automatically allocate the necessary spare parts based on the proposed content. The proposal unit can also automatically allocate the necessary personnel based on the proposed content. The proposal unit can also automatically allocate the necessary equipment based on the proposed content. This enables efficient maintenance response by automatically allocating the necessary resources based on the proposed content. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the data of the proposed content into the AI ​​and have the AI ​​perform the allocation of the necessary resources.

[0097] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit may also prioritize detailed suggestions. If the user is in a hurry, the suggestion unit may also prioritize suggestions that can be acted upon quickly. This allows the suggestion unit to provide the user with the most suitable suggestions by prioritizing suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​determine the priority of suggestions.

[0098] The proposal unit can make region-specific conservation suggestions by considering the server's geographical location. For example, if the server is located in a hot and humid region, the proposal unit can make region-specific conservation suggestions. For example, if the server is located in a cold region, the proposal unit can also make region-specific conservation suggestions. For example, if the server is located in an earthquake-prone area, the proposal unit can also make region-specific conservation suggestions. In this way, by making conservation suggestions while considering the server's geographical location, region-specific problems can be addressed. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the server's geographical location data into AI and have the AI ​​execute region-specific conservation suggestions.

[0099] The proposal unit can analyze social media trends and make relevant maintenance suggestions. For example, if a problem with a particular server is being discussed on social media, the proposal unit can make a maintenance suggestion for that server. For example, if traffic in a particular region is increasing on social media, the proposal unit can also make a maintenance suggestion for servers in that region. For example, if a particular anomaly is being reported on social media, the proposal unit can also make a maintenance suggestion for that anomaly. This allows for quick responses by making relevant maintenance suggestions through the analysis of social media trends. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input social media trend data into AI and have the AI ​​execute relevant maintenance suggestions.

[0100] The plan generation unit can estimate the user's emotions and adjust the plan content based on the estimated emotions. For example, if the user is stressed, the plan generation unit can generate a concise and easy-to-understand plan. For example, if the user is relaxed, the plan generation unit can also generate a detailed plan. For example, if the user is in a hurry, the plan generation unit can also generate a plan that can be executed quickly. In this way, by adjusting the plan content according to the user's emotions, the optimal plan for the user can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the plan generation unit may be performed using AI or not using AI. For example, the plan generation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the plan content.

[0101] The planning generation unit can check the inventory status of spare parts in real time and generate an optimal replacement plan. For example, the planning generation unit can check the inventory status of spare parts in real time and generate an optimal replacement plan. For example, if the inventory of spare parts is low, the planning generation unit can adjust the replacement plan and order the necessary spare parts. For example, the planning generation unit can dynamically change the replacement plan according to the inventory status of spare parts. This makes it possible to generate an optimal replacement plan by checking the inventory status of spare parts in real time. Some or all of the above processes in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input spare parts inventory data into AI and have the AI ​​generate an optimal replacement plan.

[0102] The planning generation unit can automate coordination with vendors and optimize the replacement work schedule. For example, the planning generation unit can automate coordination with vendors and optimize the replacement work schedule. The planning generation unit can also determine the optimal timing for replacement work, taking into account the vendor's schedule. The planning generation unit can also perform coordination with vendors in real time and dynamically change the replacement work schedule. By automating coordination with vendors, the replacement work schedule can be optimized, enabling efficient maintenance response. Some or all of the above processes in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input vendor schedule data into AI and have the AI ​​perform coordination and schedule optimization.

[0103] The planning generation unit can estimate the user's emotions and determine the priority of plans based on the estimated emotions. For example, if the user is stressed, the planning generation unit will prioritize important plans. For example, if the user is relaxed, the planning generation unit may also prioritize detailed plans. For example, if the user is in a hurry, the planning generation unit may also prioritize plans that can be executed quickly. In this way, by determining the priority of plans according to the user's emotions, the optimal plan for the user can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input user emotion data into an AI and have the AI ​​perform the determination of plan priorities.

[0104] The plan generation unit can generate region-specific exchange plans by considering the geographical location information of the server. For example, if the server is located in a hot and humid region, the plan generation unit will generate a region-specific exchange plan. For example, if the server is located in a cold region, the plan generation unit can also generate a region-specific exchange plan. For example, if the server is located in an earthquake-prone area, the plan generation unit can also generate a region-specific exchange plan. In this way, by generating exchange plans while considering the geographical location information of the server, it is possible to address region-specific problems. Some or all of the above processing in the plan generation unit may be performed using AI or not. For example, the plan generation unit can input the server's geographical location data into the AI ​​and have the AI ​​perform the generation of region-specific exchange plans.

[0105] The planning generation unit can improve planning accuracy by referring to relevant literature and databases. For example, the planning generation unit can improve planning accuracy by referring to relevant literature. The planning generation unit can also improve planning accuracy by referring to relevant databases. The planning generation unit can also improve planning accuracy by referring to relevant research results. In this way, planning accuracy can be improved by referring to relevant literature and databases. Some or all of the above processing in the planning generation unit may be performed using AI or not. For example, the planning generation unit can input data from relevant literature and databases into the AI ​​and have the AI ​​perform the improvement of planning accuracy.

[0106] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit can provide concise and easy-to-understand evaluation criteria. For example, if the user is relaxed, the evaluation unit can also provide detailed evaluation criteria. For example, if the user is in a hurry, the evaluation unit can also provide criteria that allow for quick evaluation. By adjusting the evaluation criteria according to the user's emotions, an evaluation that is easy for the user to understand becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into AI and have the AI ​​perform the adjustment of evaluation criteria.

[0107] The evaluation unit can monitor the operating status of the replaced equipment in real time and update the evaluation results. For example, the evaluation unit can monitor the operating status of the replaced equipment in real time and update the evaluation results. For example, if an abnormality occurs in the replaced equipment, the evaluation unit can collect the data and update the evaluation results. For example, the evaluation unit can continuously monitor the operating status of the replaced equipment and update the evaluation results. This enables a rapid response by monitoring the operating status of the replaced equipment in real time. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the operating status data of the replaced equipment into the AI ​​and have the AI ​​update the evaluation results.

[0108] The evaluation unit can continuously improve the predictive model based on the evaluation results. For example, the evaluation unit can continuously improve the predictive model based on the evaluation results. The evaluation unit can also use the evaluation results as feedback to improve the accuracy of the predictive model. For example, the evaluation unit can adjust the parameters of the predictive model based on the evaluation results. This allows for improved prediction accuracy by continuously improving the predictive model based on the evaluation results. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the evaluation result data into AI and have AI perform improvements to the predictive model.

[0109] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the evaluation unit can also provide a display method that gets straight to the point. By adjusting the display method of the evaluation results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into AI and have the AI ​​adjust the display method of the evaluation results.

[0110] The evaluation unit can set region-specific evaluation criteria, taking into account the server's geographical location. For example, if the server is located in a hot and humid region, the evaluation unit can set region-specific evaluation criteria. For example, if the server is located in a cold region, the evaluation unit can also set region-specific evaluation criteria. For example, if the server is located in an earthquake-prone area, the evaluation unit can also set region-specific evaluation criteria. This allows for addressing region-specific issues by setting evaluation criteria while considering the server's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input the server's geographical location data into the AI ​​and have the AI ​​perform the setting of region-specific evaluation criteria.

[0111] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature and databases. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant databases. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant research results. In this way, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature and databases. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input data from relevant literature and databases into the AI ​​and have the AI ​​perform the improvement of evaluation accuracy.

[0112] The evaluation unit can optimize the next maintenance plan based on the evaluation results. For example, the evaluation unit can optimize the next maintenance plan based on the evaluation results. The evaluation unit can also use the evaluation results as feedback to improve the accuracy of the next maintenance plan. For example, the evaluation unit can adjust the parameters of the next maintenance plan based on the evaluation results. By optimizing the next maintenance plan based on the evaluation results, the accuracy of maintenance responses can be improved. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the evaluation result data into AI and have AI perform the optimization of the next maintenance plan.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit can dynamically change the type of data collected according to the server's operating status. For example, when the server load is high, it can collect only essential data to maintain system stability. When the server load is low, it can collect detailed data to perform more accurate analysis. Furthermore, it can adjust the type of data collected in real time according to the server's operating status. This allows the system to maintain stability by adjusting the type of data collected according to the server's operating status.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0116] The suggestion function can estimate the user's emotions and adjust the suggestion content based on those emotions. For example, if the user is stressed, it can provide a concise and easy-to-understand suggestion. If the user is relaxed, it can provide a more detailed suggestion. If the user is in a hurry, it can provide a suggestion that can be implemented quickly. In this way, by adjusting the suggestion content according to the user's emotions, it can provide the most suitable suggestion for the user.

[0117] The planning generation unit can check the inventory status of spare parts in real time and generate the optimal replacement plan. For example, it can check the inventory status of spare parts in real time and generate the optimal replacement plan. If the inventory of spare parts is low, it can adjust the replacement plan and order the necessary spare parts. It can also dynamically change the replacement plan according to the inventory status of spare parts. In this way, by checking the inventory status of spare parts in real time, the optimal replacement plan can be generated.

[0118] The evaluation unit can monitor the operational status of the replaced equipment in real time and update the evaluation results. For example, it can monitor the operational status of the replaced equipment in real time and update the evaluation results. If an abnormality occurs in the replaced equipment, it can also collect that data and update the evaluation results. It can also continuously monitor the operational status of the replaced equipment and update the evaluation results. This enables a rapid response by monitoring the operational status of the replaced equipment in real time.

[0119] The data collection unit can estimate the user's emotions and adjust the type of data collected based on those emotions. For example, if the user is stressed, only essential data can be collected to reduce the burden. If the user is relaxed, more detailed data can be collected for more accurate analysis. If the user is in a hurry, data that can be collected quickly can be prioritized. In this way, the burden on the user can be reduced by adjusting the type of data collected according to their emotions.

[0120] The analysis unit can optimize analysis methods to detect specific patterns based on past failure data. For example, it can optimize analysis methods to detect specific abnormal patterns based on past failure data. It can also optimize analysis methods to detect patterns that indicate impending failures based on past failure data. It can also optimize analysis methods to identify the cause of failures based on past failure data. As a result, by optimizing analysis methods based on past failure data, it is possible to detect impending failures at an early stage.

[0121] The proposal function can consider the server's geographical location to provide region-specific maintenance suggestions. For example, if the server is located in a hot and humid region, it can provide region-specific maintenance suggestions. If the server is located in a cold region, it can also provide region-specific maintenance suggestions. If the server is located in an earthquake-prone area, it can also provide region-specific maintenance suggestions. In this way, by providing maintenance suggestions that take the server's geographical location into consideration, it is possible to address region-specific problems.

[0122] The plan generation unit can estimate the user's emotions and adjust the plan content based on those emotions. For example, if the user is stressed, it can generate a concise and easy-to-understand plan. If the user is relaxed, it can generate a detailed plan. If the user is in a hurry, it can generate a plan that can be executed quickly. In this way, by adjusting the plan content according to the user's emotions, it can generate the optimal plan for the user.

[0123] The evaluation unit can optimize the next maintenance plan based on the evaluation results. For example, it can optimize the next maintenance plan based on the evaluation results. It can also use the evaluation results as feedback to improve the accuracy of the next maintenance plan. It can also adjust the parameters of the next maintenance plan based on the evaluation results. In this way, the accuracy of maintenance responses can be improved by optimizing the next maintenance plan based on the evaluation results.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects server log data and at least one of the following information: traffic, temperature, or unusual noises. For example, the collection unit can collect server log data, traffic data, temperature data, and unusual noise data. Step 2: The analysis unit analyzes the information collected by the collection unit and calculates the probability of failure. For example, it can analyze collected log data, traffic data, temperature data, and abnormal noise data to calculate the probability of failure. Step 3: The proposal unit proposes preventive maintenance based on the failure probability calculated by the analysis unit. For example, preventive maintenance can be proposed when the failure probability exceeds a certain level, is high, or is moderate. Step 4: The planning generation unit generates a replacement plan based on the preventive maintenance proposed by the proposal unit. For example, it can check spare parts, coordinate with vendors, and create work tickets. Step 5: The evaluation unit evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit, and improves the predictive data. For example, it can evaluate the condition and operating status of the equipment after replacement and improve the predictive data.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, plan generation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the sensors and camera 42 of the smart device 14 to collect information such as server log data, traffic, temperature, and abnormal noises. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and calculate the failure probability. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose preventive maintenance based on the failure probability. The plan generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to generate a replacement plan based on the preventive maintenance. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to evaluate the condition and operating status of the equipment after replacement and improve the predictive data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, plan generation unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the sensors and camera 42 of the smart glasses 214 to collect information such as server log data, traffic, temperature, and abnormal noises. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and calculates the probability of failure. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which proposes preventive maintenance based on the probability of failure. The plan generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which generates a replacement plan based on preventive maintenance. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which evaluates the state and operating status of the equipment after replacement and improves the predictive data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, plan generation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the sensors and camera 42 of the headset terminal 314 to collect information such as server log data, traffic, temperature, and abnormal noises. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and calculate the probability of failure. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose preventive maintenance based on the probability of failure. The plan generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to generate a replacement plan based on preventive maintenance. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to evaluate the state and operating status of the equipment after replacement and improve the predictive data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, plan generation unit, and evaluation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the sensors and camera 42 of the robot 414 to collect information such as server log data, traffic, temperature, and abnormal noises. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and calculate the probability of failure. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose preventive maintenance based on the probability of failure. The plan generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to generate a replacement plan based on preventive maintenance. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to evaluate the condition and operating status of the equipment after replacement and improve the predictive data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0179] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) A collection unit that collects server log data and information of at least one of the following: traffic, temperature, and unusual noises. An analysis unit analyzes the information collected by the aforementioned collection unit and calculates the probability of failure, A proposal unit that proposes preventive maintenance based on the failure probability calculated by the aforementioned analysis unit, A planning generation unit that generates a replacement plan based on the preventive maintenance proposed by the proposal unit, The system includes an evaluation unit that evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit, and improves the predictive data. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information from multiple sensors, including at least one of the following: server log data, traffic, temperature, or unusual noises. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The probability of failure is calculated based on past failure knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose preventive maintenance when the probability of failure exceeds a certain level. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned plan generation unit, Perform at least one of the following: check spare parts, coordinate with vendors, or create a work ticket. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Evaluate the condition and operational status of the equipment after replacement and optimize predictive data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The collection frequency is dynamically changed according to the server's operating status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When an anomaly occurs, prioritize the collection of specific sensor data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Data collection is performed according to environmental conditions, taking into account the server's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze social media trends and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Optimize analysis methods to detect specific patterns based on past failure data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When an anomaly is detected, the analysis results are updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Analyze region-specific failure patterns by considering the geographical location of the servers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Referencing relevant literature and databases will improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When the probability of failure is high, we propose specific preventive maintenance procedures. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Based on the proposed content, the necessary resources will be automatically allocated. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Considering the geographical location of the server, we will make region-specific conservation proposals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Analyze social media trends and make relevant conservation proposals. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned plan generation unit, The system estimates the user's emotions and adjusts the plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned plan generation unit, Check spare parts inventory in real time and generate the optimal replacement plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned plan generation unit, Automate vendor coordination and optimize replacement schedules. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned plan generation unit, It estimates user sentiment and prioritizes plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned plan generation unit, Considering the server's geographical location, generate region-specific exchange plans. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned plan generation unit, Referencing relevant literature and databases will improve the accuracy of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 31) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The evaluation unit, The operational status of the replaced equipment is monitored in real time, and the evaluation results are updated. The system described in Appendix 1, characterized by the features described herein. (Note 33) The evaluation unit, Based on the evaluation results, we will continuously improve the predictive model. The system described in Appendix 1, characterized by the features described herein. (Note 34) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The evaluation unit, Considering the geographical location of the server, set region-specific evaluation criteria. The system described in Appendix 1, characterized by the features described herein. (Note 36) The evaluation unit, Referencing relevant literature and databases will improve evaluation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 37) The evaluation unit, Based on the evaluation results, optimize the next maintenance plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects server log data and information of at least one of the following: traffic, temperature, and unusual noises. An analysis unit analyzes the information collected by the aforementioned collection unit and calculates the probability of failure, A proposal unit that proposes preventive maintenance based on the failure probability calculated by the aforementioned analysis unit, A planning generation unit that generates a replacement plan based on the preventive maintenance proposed by the proposal unit, The system includes an evaluation unit that evaluates the condition and operating status of the equipment after replacement based on the replacement plan generated by the plan generation unit, and improves the predictive data. A system characterized by the following features.

2. The aforementioned collection unit is Collect information from multiple sensors, including at least one of the following: server log data, traffic, temperature, or unusual noises. The system according to feature 1.

3. The aforementioned analysis unit, The probability of failure is calculated based on past failure knowledge. The system according to feature 1.

4. The aforementioned proposal section is, We propose preventive maintenance when the probability of failure exceeds a certain level. The system according to feature 1.

5. The aforementioned plan generation unit, Perform at least one of the following: check spare parts, coordinate with vendors, or create a work ticket. The system according to feature 1.

6. The evaluation unit, Evaluate the condition and operational status of the equipment after replacement and optimize predictive data. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The collection frequency is dynamically changed according to the server's operating status. The system according to feature 1.

Images