system

Abstract

Provide a system. 【Solution means】 Means for acquiring a plurality of data, Means for applying a machine learning algorithm for analyzing the plurality of data, Means for predicting deterioration and abnormalities of a device based on the analysis, Means for generating a maintenance plan based on the prediction, Means for dynamically adjusting the maintenance plan, Means for providing guidance related to categories in real time, Means for sending notifications to users based on the guidance, A system including the above.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the maintenance of conventional wireless base stations, simple preventive maintenance based on scheduled time intervals was performed, resulting in wasteful maintenance work. Also, since signs of failure or deterioration could not be detected early, service interruptions occurred frequently, increasing the operation cost. As a result, there is a problem that efficient resource management is difficult and the reliability of services is also reduced.

Means for Solving the Problems

[0005] This invention provides a system for collecting information in real time at wireless base stations and predicting deterioration and anomalies using an artificial intelligence model. Specifically, it includes means for aggregating multiple pieces of information, applying an artificial intelligence model to analyze this information, and predicting signs of anomalies. Based on the prediction results, it generates an optimal maintenance plan and dynamically adjusts it as needed, enabling efficient and timely maintenance activities. This system also improves the accuracy of the artificial intelligence model by utilizing historical data and automatically provides notifications when anomalies are detected.

[0006] "Means of collecting information" refers to the function of collecting data from devices and the environment using sensors and other data acquisition devices.

[0007] An "artificial intelligence model" refers to an algorithm or learning system built to analyze and predict based on collected data.

[0008] "Means for predicting equipment deterioration or abnormalities" refers to a function that determines whether equipment is functioning normally based on the analysis results of an artificial intelligence model, and detects future problems in advance.

[0009] "Means for generating maintenance plans" refers to functions that formulate optimal maintenance timing and content based on predictive data.

[0010] "Means of adjusting maintenance plans" refers to the ability to update existing maintenance plans in response to newly collected data and changes in circumstances, and to respond flexibly.

[0011] "Using historical data" refers to a method of improving analysis accuracy by referring to previously collected maintenance records and failure reports.

[0012] "Means for automatically sending notifications" refers to a function that automatically sends a warning message to a pre-configured address or device when an anomaly is detected. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. To realize this system, a server plays a central role in acquiring information using multiple sensors and data acquisition devices. The server collects real-time data such as temperature, vibration, and usage history in bulk and uses an artificial intelligence model to analyze this data.

[0035] Based on the collected data, the server accurately predicts the deterioration status and signs of malfunction of the equipment. Based on this, the server automatically generates an appropriate maintenance plan using the prediction results. Furthermore, this plan is periodically reviewed and dynamically adjusted by the server in accordance with the operating environment and new data.

[0036] As a concrete example, if the temperature of a wireless base station exceeds a certain threshold, the server immediately detects this data anomaly and uses an AI model to predict the impact of this condition. Based on the prediction, the server sends a notification to the terminal and provides recommended steps for maintenance personnel to take appropriate action. This process enables users to respond effectively and quickly.

[0037] Furthermore, the server references historical data to improve the prediction accuracy of the AI ​​model, reduce unnecessary maintenance, and support rapid responses as needed. This allows for both reduced operating costs and improved service reliability.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server collects real-time data on temperature, vibration, and usage history from sensors installed at wireless base stations. The sensors transmit the data to the server via IoT protocols, and the server stores this data in a database.

[0041] Step 2:

[0042] The data stored by the server is preprocessed. Specifically, noise is removed from the data and missing values ​​are filled in. In this process, a moving average filter is used to reduce noise.

[0043] Step 3:

[0044] The server inputs pre-processed data into an artificial intelligence model for data analysis. The analysis calculates an anomaly score and identifies signs of equipment deterioration or malfunction.

[0045] Step 4:

[0046] The server automatically generates a maintenance plan based on the analysis results. It determines the optimal timing and content by referring to past maintenance history. This plan is dynamically adjusted as needed.

[0047] Step 5:

[0048] If the server detects an anomaly, it automatically sends a notification to the terminal. The notification includes specific recommended actions, allowing users to take immediate action.

[0049] Step 6:

[0050] Users can check information from the server via their terminals and perform maintenance tasks according to the indicated procedures. This allows for effective maintenance while eliminating unnecessary work.

[0051] Step 7:

[0052] The server continuously learns the AI ​​model while reflecting the latest data, improving prediction accuracy. This will also be effectively utilized in future maintenance planning.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In modern communication infrastructure, the proper maintenance and operation of wireless base stations are crucial for ensuring service quality and reliability. However, conventional systems often lack sufficient early detection of anomalies and predictive maintenance planning, leading to high operating costs and increased risk of service downtime. In addition, unnecessary maintenance and sudden failure responses can occur, potentially reducing work efficiency.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for collecting real-time information from multiple detection devices, means for applying a machine learning model for analyzing the real-time information, and means for predicting device deterioration or abnormalities based on the analysis. This enables the detection of device abnormalities and the generation of predictive maintenance plans, thereby reducing operating costs and improving service reliability.

[0058] A "detection device" refers to sensors or devices used to collect data from the environment or equipment.

[0059] "Real-time information" refers to data that is available almost instantly and shows processes or states that are currently in progress.

[0060] A "machine learning model" is an algorithm that learns patterns and rules based on large amounts of data and uses that knowledge to make predictions and perform analyses on new data.

[0061] "Analysis" is the process of breaking down data and identifying useful information and trends based on that analysis.

[0062] "Degradation" refers to a state in which the performance of a device or system declines over time or with use.

[0063] An "anomaly" is an unexpected event or pattern that deviates from normal operation or state.

[0064] A "maintenance plan" is a set of schedules and procedures designed for the maintenance and management of equipment and systems.

[0065] "Dynamic adjustment" refers to the process of automatically modifying and adapting plans and operations in response to changing circumstances.

[0066] A "terminal" refers to an electronic device or computer device used to receive information.

[0067] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. The system is centered around a server and has the function of collecting real-time information using multiple detection devices. The server acquires data using temperature sensors and vibration sensors and applies machine learning models to analyze this data.

[0068] Specifically, the server uses software such as TENSORFLOW® and PyTorch to create machine learning models and analyze data. Based on the analyzed data, the server predicts the deterioration status and signs of anomalies in the equipment. These predictions are useful for automatically generating maintenance plans.

[0069] Based on the prediction results, the server sends notifications to terminals and provides suggested steps for maintenance personnel to take appropriate action. This enables cost reduction and improved service reliability.

[0070] For example, if the temperature of a wireless base station becomes too high, the server detects this anomaly and uses an AI model to predict its impact. Based on this result, the server sends a notification to the terminal such as "Temperature anomaly detected: Please check the cooling system immediately," allowing the user to take prompt action accordingly.

[0071] As an example of a prompt, the AI ​​model might receive a request stating, "Predict the impact if the temperature of a wireless base station exceeds a certain threshold, and propose an appropriate maintenance plan."

[0072] This system allows servers to respond quickly to environmental changes and dynamically adjust maintenance plans, significantly improving operational efficiency and reliability.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects real-time information such as temperature, vibration, and usage history from various detection devices. Input data includes data from temperature and vibration sensors. Based on this, the server constructs a raw dataset and prepares it for analysis.

[0076] Step 2:

[0077] The server feeds the collected data into a machine learning model. The input data consists of various parameters such as temperature and vibration, and the model performs analysis based on this data. Specific data processing includes data normalization and preprocessing to perform anomaly detection and pattern prediction. The output provides anomaly detection results and degradation prediction information.

[0078] Step 3:

[0079] The server automatically generates a maintenance plan based on the analysis results. The input is the results of anomaly detection obtained from the analyzed data. The server uses this to determine what maintenance is required and outputs specific maintenance procedures.

[0080] Step 4:

[0081] The server sends a notification to the terminal based on the generated maintenance plan. This step uses the created maintenance plan as input. The output to the terminal is a procedure designed to allow the maintenance user to respond quickly. It includes specific action instructions such as "Temperature anomaly detected: Check the cooling system immediately."

[0082] Step 5:

[0083] The server references historical data to further improve the prediction accuracy of the machine learning model. The input here consists of past maintenance history and operational data. Based on this, the server performs data calculations and retrains the model to improve accuracy. The output is the improved prediction model.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] Traditional data center equipment maintenance relies on periodic inspections and reactive measures after malfunctions occur, resulting in wasted costs and equipment downtime. Furthermore, the lack of real-time detection of potential problems and rapid response capabilities creates a need for improved equipment reliability.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes a device for acquiring multiple data sets, a device for applying a machine learning algorithm for analyzing the multiple data sets, a function for predicting equipment deterioration or abnormalities based on the analysis, a function for generating a maintenance plan based on the prediction, a function for dynamically adjusting the maintenance plan, a device for providing category-related guidance in real time, and a function for sending notifications to users based on the guidance. This makes it possible to predict equipment abnormalities in the data center in advance and provide guidance for immediate response, thereby reducing unnecessary maintenance and improving equipment reliability.

[0089] "Data" refers to information acquired by the system, such as temperature, vibration, and usage history.

[0090] A "machine learning algorithm" is a mathematical and statistical method used to analyze collected data.

[0091] The "function to predict deterioration or abnormalities" refers to the ability to predict changes in the device's state in advance based on analysis results.

[0092] The "maintenance plan generation function" is the process of creating an optimal maintenance schedule based on predicted deterioration and anomalies.

[0093] The "dynamic adjustment function" refers to the ability to automatically optimize existing plans and schedules in response to environmental changes or new data.

[0094] A "device that provides guidance" is a device that, based on the analytical information obtained, presents appropriate countermeasures to the user.

[0095] The "notification sending function" is a means of communicating important information from the system to the user.

[0096] The system implementing this invention is designed for the efficient operation and maintenance of equipment within a data center. A server plays a central role in the system, collecting and processing data from various sensors in real time. Specific hardware used includes temperature and vibration sensors installed in each server rack. Data from these sensors is transmitted via a network to a server in the cloud.

[0097] The server uses machine learning algorithms to analyze the collected data and predict the deterioration status and signs of abnormalities in the equipment. This analysis utilizes TensorFlow, enabling advanced pattern recognition. Based on the analysis results, the server automatically generates a maintenance plan based on the predictions and has the capability to dynamically adjust it according to the current operating environment.

[0098] Users receive notifications from the cloud server on their smartphones and tablets. These notifications include information on abnormal equipment conditions and recommended maintenance procedures, enabling rapid, real-time responses. For example, if the temperature of a specific server rack in the data center rises, this is immediately detected, and a notification suggesting cooling measures is sent to the user's device. This process reduces unnecessary maintenance and improves operational efficiency.

[0099] Because it is a generative AI model, it can combine historical data with real-time data to enable more accurate predictions, and based on those predictions, an appropriate maintenance plan can be provided. An example of a prompt message is, "Enter the current temperature data for the server rack and predict the next maintenance date."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server collects data in real time from temperature and vibration sensors within the data center. The input consists of numerical temperature and vibration data sent from each sensor, which is stored in an initial database. The output is a dataset of sensor information that is updated periodically.

[0103] Step 2:

[0104] The server applies machine learning algorithms to the collected data and analyzes the dataset. Specifically, it uses an artificial intelligence model based on TensorFlow to identify patterns in the data and evaluate the possibility of equipment degradation or anomalies. Its input is the dataset obtained in step 1, and its output is an evaluation report that includes anomaly predictions.

[0105] Step 3:

[0106] Based on the analysis results, the server automatically generates a maintenance plan based on predicted degradation and anomalies. The input here is the evaluation report from step 2, and the output is the proposed maintenance schedule. Specifically, the server refers to historical data and incorporates the optimal maintenance timing and procedures into the plan.

[0107] Step 4:

[0108] The server dynamically adjusts the generated maintenance plan based on real-time data and operational status. Inputs include the latest sensor data and environmental information, and output is the adjusted, up-to-date maintenance schedule.

[0109] Step 5:

[0110] The terminal receives information from the server and sends notifications to the user. These notifications provide the user with information regarding anomalies, recommended actions, and maintenance schedules. The input is maintenance information sent from the server, and the output is operation guides and alert messages displayed on the user terminal.

[0111] These steps enable data center equipment to operate more efficiently and allow users to detect anomalies early and respond appropriately. By employing generative AI models, the system continuously learns from past data and better predicts future maintenance. An example of a prompt message is, "Analyze data from the anomaly sensor and adjust the maintenance plan as needed."

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention provides a system that combines an emotion engine to improve the operational efficiency of wireless base stations and support the psychological stability of users. In this system, a server plays a central role. The server acquires data such as temperature, vibration, and usage history in real time from multiple sensors installed at the base station. The collected data is centrally managed and input into an artificial intelligence model for analysis.

[0114] Subsequently, the server uses an artificial intelligence model to accurately predict equipment degradation and anomalies, and based on this, creates an optimal maintenance plan. This includes a process of improving the model's accuracy by utilizing historical data. The maintenance plan is dynamically adjusted in response to real-time data.

[0115] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The server acquires the user's emotional data through emotion sensors and analyzes it to determine the user's state. This emotional data influences the content and method of notifications provided to the user. For example, if the user is feeling anxious, the server sends more detailed information or reassuring messages to the device.

[0116] To give a specific example, if the temperature at a base station suddenly rises, and the server detects the anomaly, and the emotion engine determines that the user of the assigned technician is feeling anxious, the server will not only take swift action but also issue an alert confirming that the situation is under control. This allows the user to respond to the situation calmly and effectively.

[0117] This integrated system aims to improve the user experience while ensuring the safety and efficient operation and maintenance of base stations.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The server collects data such as temperature, vibration, and usage history in real time from sensors installed at wireless base stations. The sensor data is periodically updated and stored in the server's database.

[0121] Step 2:

[0122] The server preprocesses the collected data, performing tasks such as noise reduction and missing value imputation. Algorithms such as moving averages are used to maintain data accuracy.

[0123] Step 3:

[0124] The server inputs pre-processed data into an artificial intelligence model to predict equipment degradation and detect anomalies. The AI ​​model utilizes historical data and employs a feedback loop to improve analysis accuracy.

[0125] Step 4:

[0126] If a server malfunction is predicted, an optimal maintenance plan is generated. The generated plan includes countermeasures and execution timing based on the predicted problem.

[0127] Step 5:

[0128] The server acquires user emotion data from emotion sensors and analyzes it using an emotion engine. Based on the user's emotional state, it generates specific response messages.

[0129] Step 6:

[0130] When the server sends an anomaly notification to the terminal, it considers the results of the emotion engine and sends the notification in an appropriate tone and content based on the user's emotional state. This allows for measures such as including additional information to alleviate anxiety.

[0131] Step 7:

[0132] The user receives a notification from their device and performs maintenance work based on the provided instructions. The device provides detailed information to support the user's actions.

[0133] Step 8:

[0134] The server performs the next analysis based on the latest sensor and sentiment data, continuously improving the model to enhance the overall predictive accuracy of the system. This increases system reliability and user satisfaction.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0137] In wireless base stations and other equipment, there is a need to ensure efficient operation and maintenance, as well as security, while simultaneously providing information that takes into account the user's psychological state. However, conventional systems have struggled to accurately predict equipment deterioration or malfunctions, or to provide real-time feedback that appropriately reflects the user's emotions. Furthermore, there is a lack of dynamic adjustments to generated maintenance plans and notification methods that respond to the user's condition, resulting in challenges in responding quickly and appropriately.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes means for acquiring multiple observation data, means for managing and pre-processing the observation data, and means for applying a generated AI model using the pre-processed observation data. This makes it possible to predict equipment deterioration and abnormalities with high accuracy. Furthermore, it enables dynamic adjustment of maintenance plans and the provision of feedback that takes into account the user's psychological state, thereby realizing efficient and safe operation.

[0140] "Observation data" refers to information acquired in real time from various sensors at wireless base stations, including temperature, vibration, and usage history.

[0141] A "generative AI model" is an artificial intelligence algorithm trained using a machine learning framework, and it is a model that analyzes observational data to predict equipment degradation and anomalies.

[0142] A "maintenance plan" is a work plan created based on predictions of equipment deterioration and malfunctions, aimed at maintaining the stable and efficient operation of the equipment.

[0143] "User's psychological state" refers to information about the user's emotions, such as anxiety and feelings of security, obtained through emotion sensors.

[0144] "Notifications" are a means of conveying information, warnings, and reassuring messages generated by a server to the user.

[0145] "Data preprocessing" refers to a series of procedures that remove outliers from acquired observational data and prepare it for analysis.

[0146] "Dynamic adjustment" is a process that modifies maintenance plans and notifications in real time in response to changes in the status of the equipment and users.

[0147] This invention is constructed as a system integrating multiple technologies to improve the operational efficiency of wireless base stations and support the psychological well-being of users. The server plays the primary role, and its details are described below.

[0148] The server acquires real-time observation data using temperature and vibration sensors installed at wireless base stations. These sensors provide data on the environmental conditions and physical operation of the system at the base stations. The server acquires data every five minutes and stores it in a central database. This data is managed and preprocessed to create an analyzable format with outliers removed.

[0149] Next, the server inputs the pre-processed observation data into a generative AI model using TensorFlow or PyTorch. This generative AI model is trained using historical data and can accurately predict equipment degradation and anomalies. Based on these predictions, the server dynamically generates an effective maintenance plan, constantly adjusting it according to the latest data.

[0150] Furthermore, the server analyzes the user's psychological state obtained from emotion sensors and provides appropriate feedback. If the user is feeling anxious, the server generates a reassuring message and sends it to the device to promote the user's psychological stability. Notifications are sent in real time, supporting the user's situation and the implementation of appropriate measures.

[0151] For example, if vibration data at a base station shows an abnormal pattern, the server detects the anomaly and sends a notification to the technician. If the emotional data indicates that the technician is feeling anxious, the server prompts them with a message such as, "The situation is under control. Necessary measures are being taken."

[0152] An example of a prompt message for a generated AI model would be: "Based on the base station's temperature data, predict the likelihood of an anomaly occurring and suggest the necessary actions."

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The server acquires observation data in real time from temperature and vibration sensors installed at wireless base stations. Specifically, data from each sensor is automatically sent to the server every 5 minutes and stored in a central database. It receives observation data from sensors as input and generates data stored in the central database as output.

[0156] Step 2:

[0157] The server preprocesses the acquired observational data. Specifically, it detects outliers and normalizes the data. Data processing involves removing abnormal temperature and vibration values ​​and converting the data into a format suitable for analysis. The input is the unprocessed observational data accumulated in step 1, and the output is a clean dataset.

[0158] Step 3:

[0159] The server inputs pre-processed data into a generating AI model to predict equipment degradation and anomalies. Because the model is trained using historical data, it can make highly accurate predictions. The data calculations include anomaly detection and degradation prediction by the model. The input is pre-processed data, and the output is the prediction result.

[0160] Step 4:

[0161] The server dynamically generates an optimal maintenance plan based on the prediction results from the generated AI model. Specifically, it plans the work required to address the predicted anomalies and assigns them to technicians as prioritized tasks. The input is the prediction results obtained in step 3, and the output is a maintenance work plan.

[0162] Step 5:

[0163] The server acquires and analyzes data on the user's psychological state from their emotion sensor. Specifically, it determines whether the user is feeling anxious or stressed and adjusts the feedback accordingly. The input is emotional data, and the output is a notification tailored to the user's psychological state.

[0164] Step 6:

[0165] The server sends a notification to the terminal based on the analysis results. Specifically, it provides the user with a message that reassures them that "the situation is under control," as well as specific instructions for action. The input is the results of steps 4 and 5, and the output is the notification message displayed on the terminal.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In recent years, the operation of wireless communication equipment has required efficient maintenance and management, as well as psychological stability for users. However, current systems are insufficient for detecting equipment anomalies and developing efficient maintenance plans, and they also have difficulty appropriately addressing users' emotions. This can lead to increased user anxiety and disrupt operations. To address this, a system is needed that integrates early detection of anomalies with notifications that provide reassurance.

[0169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0170] In this invention, the server includes means for collecting multiple pieces of information, means for applying an artificial intelligence algorithm for analyzing the information, and means for recognizing the user's emotional state and dynamically adjusting the notification content. This enables efficient device management and improved user psychological stability.

[0171] "Multiple pieces of information" refers to various types of data acquired from sensors, such as vibration, temperature, and usage history.

[0172] An "artificial intelligence algorithm" is a computational method used to analyze collected information and perform anomaly detection and predictive analysis.

[0173] "Means for predicting equipment deterioration or abnormalities" refers to a function that uses artificial intelligence algorithms to evaluate the operating status of equipment and identify potential failures or problems in advance.

[0174] "Means of generating maintenance plans" refers to the process of creating an efficient and effective maintenance schedule based on predicted anomalies.

[0175] "Means for adjusting maintenance plans" refers to a function that optimizes existing maintenance plans in response to real-time data and new information.

[0176] "Means of recognizing the user's emotional state" refers to a function that detects the user's psychological response via an emotion engine and determines that state.

[0177] "Means for dynamically adjusting notification content" refers to a mechanism that changes the content of information and warning messages provided according to the user's emotional state.

[0178] A "means of supporting psychological stability" refers to a system that provides information and messages to reassure users when they are feeling anxious or stressed.

[0179] This invention provides a system that supports the efficient operation of wireless communication devices while also supporting the psychological well-being of users. The server aggregates data acquired in real time from multiple sensors and uses artificial intelligence algorithms to predict equipment degradation and abnormalities with high accuracy. This analysis process utilizes programming languages ​​such as Python and incorporates machine learning libraries such as TensorFlow and Scikit-learn.

[0180] The server uses historical data to improve the accuracy of its artificial intelligence algorithms and dynamically adjusts maintenance plans when anomalies are detected. It also utilizes an emotion recognition API to recognize the user's emotional state. This allows for analysis of the user's psychological condition and dynamic adjustment of notification content based on the results.

[0181] Furthermore, if the device detects that the user is feeling anxious, a reassuring message is immediately delivered. This establishes a system that allows users to respond calmly and effectively.

[0182] For example, if suspicious vibrations are detected within a facility and the AI ​​classifies them as an anomaly, the user will receive instructions for immediate action along with a message assuring them that safety is ensured. This system reduces user anxiety and enables reliable equipment management.

[0183] An example of an input prompt for the generating AI model might be something like, "Please have the AI ​​analyze the recent vibration patterns within the facility and clarify what this anomaly indicates."

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server collects data such as temperature, vibration, and usage history in real time from multiple sensors. This input data is aggregated as raw data on the server and centrally managed.

[0187] Step 2:

[0188] The server preprocesses the collected raw data and converts it into a format suitable for machine learning. This process includes denoising and normalizing the data. The preprocessed data is then prepared as a dataset that can be easily analyzed by artificial intelligence algorithms.

[0189] Step 3:

[0190] The server uses pre-processed data as input to run an artificial intelligence algorithm and predict equipment degradation and anomalies. In this step, historical data is also incorporated to improve the model's accuracy. An anomaly prediction report is generated as output.

[0191] Step 4:

[0192] The server creates an efficient maintenance plan based on anomaly prediction reports. It also has the capability to dynamically adjust the maintenance plan in response to real-time data changes. The output at this stage is the updated maintenance schedule.

[0193] Step 5:

[0194] The server analyzes emotional data collected through an emotion recognition API to recognize the user's emotional state. Based on the analysis results, it determines whether the user is feeling anxious.

[0195] Step 6:

[0196] The device generates and delivers notifications that are dynamically adjusted based on the user's emotional state. These notifications include necessary actions and reassuring content.

[0197] Step 7:

[0198] Users can check notifications received from their devices and respond to the situation calmly and effectively. This allows for both improved operational efficiency and psychological stability.

[0199] 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.

[0200] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0206] 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.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0208] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] 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.

[0210] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The 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.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. To realize this system, a server plays a central role in acquiring information using multiple sensors and data acquisition devices. The server collects real-time data such as temperature, vibration, and usage history in bulk and uses an artificial intelligence model to analyze this data.

[0216] Based on the collected data, the server accurately predicts the deterioration status and signs of malfunction of the equipment. Based on this, the server automatically generates an appropriate maintenance plan using the prediction results. Furthermore, this plan is periodically reviewed and dynamically adjusted by the server in accordance with the operating environment and new data.

[0217] As a concrete example, if the temperature of a wireless base station exceeds a certain threshold, the server immediately detects this data anomaly and uses an AI model to predict the impact of this condition. Based on the prediction, the server sends a notification to the terminal and provides recommended steps for maintenance personnel to take appropriate action. This process enables users to respond effectively and quickly.

[0218] Furthermore, the server references historical data to improve the prediction accuracy of the AI ​​model, reduce unnecessary maintenance, and support rapid responses as needed. This allows for both reduced operating costs and improved service reliability.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server collects real-time data on temperature, vibration, and usage history from sensors installed at wireless base stations. The sensors transmit the data to the server via IoT protocols, and the server stores this data in a database.

[0222] Step 2:

[0223] The data stored by the server is preprocessed. Specifically, noise is removed from the data and missing values ​​are filled in. In this process, a moving average filter is used to reduce noise.

[0224] Step 3:

[0225] The server inputs pre-processed data into an artificial intelligence model for data analysis. The analysis calculates an anomaly score and identifies signs of equipment deterioration or malfunction.

[0226] Step 4:

[0227] The server automatically generates a maintenance plan based on the analysis results. It determines the optimal timing and content by referring to past maintenance history. This plan is dynamically adjusted as needed.

[0228] Step 5:

[0229] If the server detects an anomaly, it automatically sends a notification to the terminal. The notification includes specific recommended actions, allowing users to take immediate action.

[0230] Step 6:

[0231] Users can check information from the server via their terminals and perform maintenance tasks according to the indicated procedures. This allows for effective maintenance while eliminating unnecessary work.

[0232] Step 7:

[0233] The server continuously learns the AI ​​model while reflecting the latest data, improving prediction accuracy. This will also be effectively utilized in future maintenance planning.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] In modern communication infrastructure, the proper maintenance and operation of wireless base stations are crucial for ensuring service quality and reliability. However, conventional systems often lack sufficient early detection of anomalies and predictive maintenance planning, leading to high operating costs and increased risk of service downtime. In addition, unnecessary maintenance and sudden failure responses can occur, potentially reducing work efficiency.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes means for collecting real-time information from multiple detection devices, means for applying a machine learning model for analyzing the real-time information, and means for predicting device deterioration or abnormalities based on the analysis. This enables the detection of device abnormalities and the generation of predictive maintenance plans, thereby reducing operating costs and improving service reliability.

[0239] A "detection device" refers to sensors or devices used to collect data from the environment or equipment.

[0240] "Real-time information" refers to data that is available almost instantly and shows processes or states that are currently in progress.

[0241] A "machine learning model" is an algorithm that learns patterns and rules based on large amounts of data and uses that knowledge to make predictions and perform analyses on new data.

[0242] "Analysis" is the process of breaking down data and identifying useful information and trends based on that analysis.

[0243] "Degradation" refers to a state in which the performance of a device or system declines over time or with use.

[0244] An "anomaly" is an unexpected event or pattern that deviates from normal operation or state.

[0245] A "maintenance plan" is a set of schedules and procedures designed for the maintenance and management of equipment and systems.

[0246] "Dynamic adjustment" refers to the process of automatically modifying and adapting plans and operations in response to changing circumstances.

[0247] A "terminal" refers to an electronic device or computer device used to receive information.

[0248] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. The system is centered around a server and has the function of collecting real-time information using multiple detection devices. The server acquires data using temperature sensors and vibration sensors and applies machine learning models to analyze this data.

[0249] Specifically, the server uses software such as TensorFlow and PyTorch to create machine learning models and analyze data. Based on the analyzed data, the server predicts the deterioration status and signs of anomalies in the equipment. These predictions are useful for automatically generating maintenance plans.

[0250] Based on the prediction results, the server sends notifications to terminals and provides suggested steps for maintenance personnel to take appropriate action. This enables cost reduction and improved service reliability.

[0251] For example, if the temperature of a wireless base station becomes too high, the server detects this anomaly and uses an AI model to predict its impact. Based on this result, the server sends a notification to the terminal such as "Temperature anomaly detected: Please check the cooling system immediately," allowing the user to take prompt action accordingly.

[0252] As an example of a prompt, the AI ​​model might receive a request stating, "Predict the impact if the temperature of a wireless base station exceeds a certain threshold, and propose an appropriate maintenance plan."

[0253] This system allows servers to respond quickly to environmental changes and dynamically adjust maintenance plans, significantly improving operational efficiency and reliability.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The server collects real-time information such as temperature, vibration, and usage history from various detection devices. Input data includes data from temperature and vibration sensors. Based on this, the server constructs a raw dataset and prepares it for analysis.

[0257] Step 2:

[0258] The server feeds the collected data into a machine learning model. The input data consists of various parameters such as temperature and vibration, and the model performs analysis based on this data. Specific data processing includes data normalization and preprocessing to perform anomaly detection and pattern prediction. The output provides anomaly detection results and degradation prediction information.

[0259] Step 3:

[0260] The server automatically generates a maintenance plan based on the analysis results. The input is the results of anomaly detection obtained from the analyzed data. The server uses this to determine what maintenance is required and outputs specific maintenance procedures.

[0261] Step 4:

[0262] The server sends a notification to the terminal based on the generated maintenance plan. This step uses the created maintenance plan as input. The output to the terminal is a procedure designed to allow the maintenance user to respond quickly. It includes specific action instructions such as "Temperature anomaly detected: Check the cooling system immediately."

[0263] Step 5:

[0264] The server references historical data to further improve the prediction accuracy of the machine learning model. The input here consists of past maintenance history and operational data. Based on this, the server performs data calculations and retrains the model to improve accuracy. The output is the improved prediction model.

[0265] (Application Example 1)

[0266] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] Traditional data center equipment maintenance relies on periodic inspections and reactive measures after malfunctions occur, resulting in wasted costs and equipment downtime. Furthermore, the lack of real-time detection of potential problems and rapid response capabilities creates a need for improved equipment reliability.

[0268] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0269] In this invention, the server includes a device for acquiring multiple data sets, a device for applying a machine learning algorithm for analyzing the multiple data sets, a function for predicting equipment deterioration or abnormalities based on the analysis, a function for generating a maintenance plan based on the prediction, a function for dynamically adjusting the maintenance plan, a device for providing category-related guidance in real time, and a function for sending notifications to users based on the guidance. This makes it possible to predict equipment abnormalities in the data center in advance and provide guidance for immediate response, thereby reducing unnecessary maintenance and improving equipment reliability.

[0270] "Data" refers to information acquired by the system, such as temperature, vibration, and usage history.

[0271] A "machine learning algorithm" is a mathematical and statistical method used to analyze collected data.

[0272] The "function to predict deterioration or abnormalities" refers to the ability to predict changes in the device's state in advance based on analysis results.

[0273] The "maintenance plan generation function" is the process of creating an optimal maintenance schedule based on predicted deterioration and anomalies.

[0274] The "dynamic adjustment function" refers to the ability to automatically optimize existing plans and schedules in response to environmental changes or new data.

[0275] A "device that provides guidance" is a device that, based on the analytical information obtained, presents appropriate countermeasures to the user.

[0276] The "notification sending function" is a means of communicating important information from the system to the user.

[0277] The system implementing this invention is designed for the efficient operation and maintenance of equipment within a data center. A server plays a central role in the system, collecting and processing data from various sensors in real time. Specific hardware used includes temperature and vibration sensors installed in each server rack. Data from these sensors is transmitted via a network to a server in the cloud.

[0278] The server uses machine learning algorithms to analyze the collected data and predict the deterioration status and signs of abnormalities in the equipment. This analysis utilizes TensorFlow, enabling advanced pattern recognition. Based on the analysis results, the server automatically generates a maintenance plan based on the predictions and has the capability to dynamically adjust it according to the current operating environment.

[0279] Users receive notifications from the cloud server on their smartphones and tablets. These notifications include information on abnormal equipment conditions and recommended maintenance procedures, enabling rapid, real-time responses. For example, if the temperature of a specific server rack in the data center rises, this is immediately detected, and a notification suggesting cooling measures is sent to the user's device. This process reduces unnecessary maintenance and improves operational efficiency.

[0280] Because it is a generative AI model, it can combine historical data with real-time data to enable more accurate predictions, and based on those predictions, an appropriate maintenance plan can be provided. An example of a prompt message is, "Enter the current temperature data for the server rack and predict the next maintenance date."

[0281] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0282] Step 1:

[0283] The server collects data in real time from temperature sensors and vibration sensors within the data center. The input is the numerical data of temperature and vibration sent from each sensor, and this data is stored in an initial database. The output is a dataset of sensor information that is updated periodically.

[0284] Step 2:

[0285] The server applies a machine learning algorithm using the collected data to analyze the dataset. Specifically, an artificial intelligence model using TensorFlow is used to identify patterns in the data and evaluate the likelihood of device degradation and anomalies. The input is the dataset obtained in Step 1, and the output is an evaluation report containing signs of anomalies.

[0286] Step 3:

[0287] Based on the analysis results, the server automatically generates a maintenance plan based on the predicted degradation and anomalies. The input here is the evaluation report from Step 2, and the output is the proposed maintenance schedule. As a specific operation, the server refers to past historical data and incorporates the optimal maintenance time and procedures into the plan.

[0288] Step 4:

[0289] The server dynamically adjusts the generated maintenance plan based on real-time data and operating conditions. The inputs include the latest sensor data and environmental information, and the output provides the adjusted and latest maintenance schedule.

[0290] Step 5:

[0291] The terminal receives information from the server and sends notifications to the user. This notification provides the user with information about anomaly warnings, recommended countermeasures, and maintenance schedules. The input is the maintenance information sent from the server, and the output is the operation guide and alert message displayed on the user terminal.

[0292] These steps enable data center equipment to operate more efficiently and allow users to detect anomalies early and respond appropriately. By employing generative AI models, the system continuously learns from past data and better predicts future maintenance. An example of a prompt message is, "Analyze data from the anomaly sensor and adjust the maintenance plan as needed."

[0293] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0294] This invention provides a system that combines an emotion engine to improve the operational efficiency of wireless base stations and support the psychological stability of users. In this system, a server plays a central role. The server acquires data such as temperature, vibration, and usage history in real time from multiple sensors installed at the base station. The collected data is centrally managed and input into an artificial intelligence model for analysis.

[0295] Subsequently, the server uses an artificial intelligence model to accurately predict equipment degradation and anomalies, and based on this, creates an optimal maintenance plan. This includes a process of improving the model's accuracy by utilizing historical data. The maintenance plan is dynamically adjusted in response to real-time data.

[0296] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The server acquires the user's emotional data through emotion sensors and analyzes it to determine the user's state. This emotional data influences the content and method of notifications provided to the user. For example, if the user is feeling anxious, the server sends more detailed information or reassuring messages to the device.

[0297] For example, when the temperature of a certain base station rises rapidly and the server detects an abnormality, and the emotion engine determines that the user, a technical staff member in charge, is feeling anxious, in addition to taking prompt measures, the server issues an alert to confirm that the situation is under control. As a result, the user can respond to the situation calmly and effectively.

[0298] This integrated system aims to improve the user experience while ensuring the safety and efficient operation and maintenance of the base station.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server collects data such as temperature, vibration, and usage history in real time from sensors installed in the wireless base station. The sensor data is updated periodically and stored in the server's database.

[0302] Step 2:

[0303] The server preprocesses the collected data, performing operations such as noise removal and missing value completion. By using algorithms such as moving average, operations are carried out to maintain the accuracy of the data.

[0304] Step 3:

[0305] The server inputs the preprocessed data into an artificial intelligence model to predict equipment degradation and detect abnormalities. In the AI model, past history data is utilized, and a feedback loop is used to improve the analysis accuracy.

[0306] Step 4:

[0307] If the server predicts an abnormality, it generates an optimal maintenance plan. The generated plan includes countermeasures based on the predicted problems and the execution timing. [[ID=​Step 5:

[0309] The server acquires user emotion data from emotion sensors and analyzes it using an emotion engine. Based on the user's emotional state, it generates specific response messages.

[0310] Step 6:

[0311] When the server sends an anomaly notification to the terminal, it considers the results of the emotion engine and sends the notification in an appropriate tone and content based on the user's emotional state. This allows for measures such as including additional information to alleviate anxiety.

[0312] Step 7:

[0313] The user receives a notification from their device and performs maintenance work based on the provided instructions. The device provides detailed information to support the user's actions.

[0314] Step 8:

[0315] The server performs the next analysis based on the latest sensor and sentiment data, continuously improving the model to enhance the overall predictive accuracy of the system. This increases system reliability and user satisfaction.

[0316] (Example 2)

[0317] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0318] In wireless base stations and other equipment, there is a need to ensure efficient operation and maintenance, as well as security, while simultaneously providing information that takes into account the user's psychological state. However, conventional systems have struggled to accurately predict equipment deterioration or malfunctions, or to provide real-time feedback that appropriately reflects the user's emotions. Furthermore, there is a lack of dynamic adjustments to generated maintenance plans and notification methods that respond to the user's condition, resulting in challenges in responding quickly and appropriately.

[0319] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0320] In this invention, the server includes means for acquiring multiple observation data, means for managing and pre-processing the observation data, and means for applying a generated AI model using the pre-processed observation data. This makes it possible to predict equipment deterioration and abnormalities with high accuracy. Furthermore, it enables dynamic adjustment of maintenance plans and the provision of feedback that takes into account the user's psychological state, thereby realizing efficient and safe operation.

[0321] "Observation data" refers to information acquired in real time from various sensors at wireless base stations, including temperature, vibration, and usage history.

[0322] A "generative AI model" is an artificial intelligence algorithm trained using a machine learning framework, and it is a model that analyzes observational data to predict equipment degradation and anomalies.

[0323] A "maintenance plan" is a work plan created based on predictions of equipment deterioration and malfunctions, aimed at maintaining the stable and efficient operation of the equipment.

[0324] "User's psychological state" refers to information about the user's emotions, such as anxiety and feelings of security, obtained through emotion sensors.

[0325] "Notifications" are a means of conveying information, warnings, and reassuring messages generated by a server to the user.

[0326] "Data preprocessing" refers to a series of procedures that remove outliers from acquired observational data and prepare it for analysis.

[0327] "Dynamic adjustment" is a process that modifies maintenance plans and notifications in real time in response to changes in the status of the equipment and users.

[0328] This invention is constructed as a system integrating multiple technologies to improve the operational efficiency of wireless base stations and support the psychological well-being of users. The server plays the primary role, and its details are described below.

[0329] The server acquires real-time observation data using temperature and vibration sensors installed at wireless base stations. These sensors provide data on the environmental conditions and physical operation of the system at the base stations. The server acquires data every five minutes and stores it in a central database. This data is managed and preprocessed to create an analyzable format with outliers removed.

[0330] Next, the server inputs the pre-processed observation data into a generative AI model using TensorFlow or PyTorch. This generative AI model is trained using historical data and can accurately predict equipment degradation and anomalies. Based on these predictions, the server dynamically generates an effective maintenance plan, constantly adjusting it according to the latest data.

[0331] Furthermore, the server analyzes the user's psychological state obtained from emotion sensors and provides appropriate feedback. If the user is feeling anxious, the server generates a reassuring message and sends it to the device to promote the user's psychological stability. Notifications are sent in real time, supporting the user's situation and the implementation of appropriate measures.

[0332] For example, if vibration data at a base station shows an abnormal pattern, the server detects the anomaly and sends a notification to the technician. If the emotional data indicates that the technician is feeling anxious, the server prompts them with a message such as, "The situation is under control. Necessary measures are being taken."

[0333] An example of a prompt message for a generated AI model would be: "Based on the base station's temperature data, predict the likelihood of an anomaly occurring and suggest the necessary actions."

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] The server acquires observation data in real time from temperature and vibration sensors installed at wireless base stations. Specifically, data from each sensor is automatically sent to the server every 5 minutes and stored in a central database. It receives observation data from sensors as input and generates data stored in the central database as output.

[0337] Step 2:

[0338] The server preprocesses the acquired observational data. Specifically, it detects outliers and normalizes the data. Data processing involves removing abnormal temperature and vibration values ​​and converting the data into a format suitable for analysis. The input is the unprocessed observational data accumulated in step 1, and the output is a clean dataset.

[0339] Step 3:

[0340] The server inputs pre-processed data into a generating AI model to predict equipment degradation and anomalies. Because the model is trained using historical data, it can make highly accurate predictions. The data calculations include anomaly detection and degradation prediction by the model. The input is pre-processed data, and the output is the prediction result.

[0341] Step 4:

[0342] The server dynamically generates an optimal maintenance plan based on the prediction results from the generated AI model. Specifically, it plans the work required to address the predicted anomalies and assigns them to technicians as prioritized tasks. The input is the prediction results obtained in step 3, and the output is a maintenance work plan.

[0343] Step 5:

[0344] The server acquires and analyzes data on the user's psychological state from their emotion sensor. Specifically, it determines whether the user is feeling anxious or stressed and adjusts the feedback accordingly. The input is emotional data, and the output is a notification tailored to the user's psychological state.

[0345] Step 6:

[0346] The server sends a notification to the terminal based on the analysis results. Specifically, it provides the user with a message that reassures them that "the situation is under control," as well as specific instructions for action. The input is the results of steps 4 and 5, and the output is the notification message displayed on the terminal.

[0347] (Application Example 2)

[0348] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0349] In recent years, the operation of wireless communication equipment has required efficient maintenance and management, as well as psychological stability for users. However, current systems are insufficient for detecting equipment anomalies and developing efficient maintenance plans, and they also have difficulty appropriately addressing users' emotions. This can lead to increased user anxiety and disrupt operations. To address this, a system is needed that integrates early detection of anomalies with notifications that provide reassurance.

[0350] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0351] In this invention, the server includes means for collecting multiple pieces of information, means for applying an artificial intelligence algorithm for analyzing the information, and means for recognizing the user's emotional state and dynamically adjusting the notification content. This enables efficient device management and improved user psychological stability.

[0352] "Multiple pieces of information" refers to various types of data acquired from sensors, such as vibration, temperature, and usage history.

[0353] An "artificial intelligence algorithm" is a computational method used to analyze collected information and perform anomaly detection and predictive analysis.

[0354] "Means for predicting equipment deterioration or abnormalities" refers to a function that uses artificial intelligence algorithms to evaluate the operating status of equipment and identify potential failures or problems in advance.

[0355] "Means of generating maintenance plans" refers to the process of creating an efficient and effective maintenance schedule based on predicted anomalies.

[0356] "Means for adjusting maintenance plans" refers to a function that optimizes existing maintenance plans in response to real-time data and new information.

[0357] "Means of recognizing the user's emotional state" refers to a function that detects the user's psychological response via an emotion engine and determines that state.

[0358] "Means for dynamically adjusting notification content" refers to a mechanism that changes the content of information and warning messages provided according to the user's emotional state.

[0359] A "means of supporting psychological stability" refers to a system that provides information and messages to reassure users when they are feeling anxious or stressed.

[0360] This invention provides a system that supports the efficient operation of wireless communication devices while also supporting the psychological well-being of users. The server aggregates data acquired in real time from multiple sensors and uses artificial intelligence algorithms to predict equipment degradation and abnormalities with high accuracy. This analysis process utilizes programming languages ​​such as Python and incorporates machine learning libraries such as TensorFlow and Scikit-learn.

[0361] The server uses historical data to improve the accuracy of its artificial intelligence algorithms and dynamically adjusts maintenance plans when anomalies are detected. It also utilizes an emotion recognition API to recognize the user's emotional state. This allows for analysis of the user's psychological condition and dynamic adjustment of notification content based on the results.

[0362] Furthermore, if the device detects that the user is feeling anxious, a reassuring message is immediately delivered. This establishes a system that allows users to respond calmly and effectively.

[0363] For example, if suspicious vibrations are detected within a facility and the AI ​​classifies them as an anomaly, the user will receive instructions for immediate action along with a message assuring them that safety is ensured. This system reduces user anxiety and enables reliable equipment management.

[0364] An example of an input prompt for the generating AI model might be something like, "Please have the AI ​​analyze the recent vibration patterns within the facility and clarify what this anomaly indicates."

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server collects data such as temperature, vibration, and usage history in real time from multiple sensors. This input data is aggregated as raw data on the server and centrally managed.

[0368] Step 2:

[0369] The server preprocesses the collected raw data and converts it into a format suitable for machine learning. This process includes denoising and normalizing the data. The preprocessed data is then prepared as a dataset that can be easily analyzed by artificial intelligence algorithms.

[0370] Step 3:

[0371] The server uses pre-processed data as input to run an artificial intelligence algorithm and predict equipment degradation and anomalies. In this step, historical data is also incorporated to improve the model's accuracy. An anomaly prediction report is generated as output.

[0372] Step 4:

[0373] The server creates an efficient maintenance plan based on anomaly prediction reports. It also has the capability to dynamically adjust the maintenance plan in response to real-time data changes. The output at this stage is the updated maintenance schedule.

[0374] Step 5:

[0375] The server analyzes emotional data collected through an emotion recognition API to recognize the user's emotional state. Based on the analysis results, it determines whether the user is feeling anxious.

[0376] Step 6:

[0377] The device generates and delivers notifications that are dynamically adjusted based on the user's emotional state. These notifications include necessary actions and reassuring content.

[0378] Step 7:

[0379] Users can check notifications received from their devices and respond to the situation calmly and effectively. This allows for both improved operational efficiency and psychological stability.

[0380] 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.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0387] 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.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0389] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] 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.

[0391] 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.

[0392] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0393] The 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.

[0394] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0396] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. To realize this system, a server plays a central role in acquiring information using multiple sensors and data acquisition devices. The server collects real-time data such as temperature, vibration, and usage history in bulk and uses an artificial intelligence model to analyze this data.

[0397] Based on the collected data, the server accurately predicts the deterioration status and signs of malfunction of the equipment. Based on this, the server automatically generates an appropriate maintenance plan using the prediction results. Furthermore, this plan is periodically reviewed and dynamically adjusted by the server in accordance with the operating environment and new data.

[0398] As a concrete example, if the temperature of a wireless base station exceeds a certain threshold, the server immediately detects this data anomaly and uses an AI model to predict the impact of this condition. Based on the prediction, the server sends a notification to the terminal and provides recommended steps for maintenance personnel to take appropriate action. This process enables users to respond effectively and quickly.

[0399] Furthermore, the server references historical data to improve the prediction accuracy of the AI ​​model, reduce unnecessary maintenance, and support rapid responses as needed. This allows for both reduced operating costs and improved service reliability.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The server collects real-time data on temperature, vibration, and usage history from sensors installed at wireless base stations. The sensors transmit the data to the server via IoT protocols, and the server stores this data in a database.

[0403] Step 2:

[0404] The data stored by the server is preprocessed. Specifically, noise is removed from the data and missing values ​​are filled in. In this process, a moving average filter is used to reduce noise.

[0405] Step 3:

[0406] The server inputs pre-processed data into an artificial intelligence model for data analysis. The analysis calculates an anomaly score and identifies signs of equipment deterioration or malfunction.

[0407] Step 4:

[0408] The server automatically generates a maintenance plan based on the analysis results. It determines the optimal timing and content by referring to past maintenance history. This plan is dynamically adjusted as needed.

[0409] Step 5:

[0410] If the server detects an anomaly, it automatically sends a notification to the terminal. The notification includes specific recommended actions, allowing users to take immediate action.

[0411] Step 6:

[0412] Users can check information from the server via their terminals and perform maintenance tasks according to the indicated procedures. This allows for effective maintenance while eliminating unnecessary work.

[0413] Step 7:

[0414] The server continuously learns the AI ​​model while reflecting the latest data, improving prediction accuracy. This will also be effectively utilized in future maintenance planning.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] In modern communication infrastructure, the proper maintenance and operation of wireless base stations are crucial for ensuring service quality and reliability. However, conventional systems often lack sufficient early detection of anomalies and predictive maintenance planning, leading to high operating costs and increased risk of service downtime. In addition, unnecessary maintenance and sudden failure responses can occur, potentially reducing work efficiency.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes means for collecting real-time information from multiple detection devices, means for applying a machine learning model for analyzing the real-time information, and means for predicting device deterioration or abnormalities based on the analysis. This enables the detection of device abnormalities and the generation of predictive maintenance plans, thereby reducing operating costs and improving service reliability.

[0420] A "detection device" refers to sensors or devices used to collect data from the environment or equipment.

[0421] "Real-time information" refers to data that is available almost instantly and shows processes or states that are currently in progress.

[0422] A "machine learning model" is an algorithm that learns patterns and rules based on large amounts of data and uses that knowledge to make predictions and perform analyses on new data.

[0423] "Analysis" is the process of breaking down data and identifying useful information and trends based on that analysis.

[0424] "Degradation" refers to a state in which the performance of a device or system declines over time or with use.

[0425] An "anomaly" is an unexpected event or pattern that deviates from normal operation or state.

[0426] A "maintenance plan" is a set of schedules and procedures designed for the maintenance and management of equipment and systems.

[0427] "Dynamic adjustment" refers to the process of automatically modifying and adapting plans and operations in response to changing circumstances.

[0428] A "terminal" refers to an electronic device or computer device used to receive information.

[0429] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. The system is centered around a server and has the function of collecting real-time information using multiple detection devices. The server acquires data using temperature sensors and vibration sensors and applies machine learning models to analyze this data.

[0430] Specifically, the server uses software such as TensorFlow and PyTorch to create machine learning models and analyze data. Based on the analyzed data, the server predicts the deterioration status and signs of anomalies in the equipment. These predictions are useful for automatically generating maintenance plans.

[0431] Based on the prediction results, the server sends notifications to terminals and provides suggested steps for maintenance personnel to take appropriate action. This enables cost reduction and improved service reliability.

[0432] For example, if the temperature of a wireless base station becomes too high, the server detects this anomaly and uses an AI model to predict its impact. Based on this result, the server sends a notification to the terminal such as "Temperature anomaly detected: Please check the cooling system immediately," allowing the user to take prompt action accordingly.

[0433] As an example of a prompt, the AI ​​model might receive a request stating, "Predict the impact if the temperature of a wireless base station exceeds a certain threshold, and propose an appropriate maintenance plan."

[0434] This system allows servers to respond quickly to environmental changes and dynamically adjust maintenance plans, significantly improving operational efficiency and reliability.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The server collects real-time information such as temperature, vibration, and usage history from various detection devices. Input data includes data from temperature and vibration sensors. Based on this, the server constructs a raw dataset and prepares it for analysis.

[0438] Step 2:

[0439] The server feeds the collected data into a machine learning model. The input data consists of various parameters such as temperature and vibration, and the model performs analysis based on this data. Specific data processing includes data normalization and preprocessing to perform anomaly detection and pattern prediction. The output provides anomaly detection results and degradation prediction information.

[0440] Step 3:

[0441] The server automatically generates a maintenance plan based on the analysis results. The input is the results of anomaly detection obtained from the analyzed data. The server uses this to determine what maintenance is required and outputs specific maintenance procedures.

[0442] Step 4:

[0443] The server sends a notification to the terminal based on the generated maintenance plan. This step uses the created maintenance plan as input. The output to the terminal is a procedure designed to allow the maintenance user to respond quickly. It includes specific action instructions such as "Temperature anomaly detected: Check the cooling system immediately."

[0444] Step 5:

[0445] The server references historical data to further improve the prediction accuracy of the machine learning model. The input here consists of past maintenance history and operational data. Based on this, the server performs data calculations and retrains the model to improve accuracy. The output is the improved prediction model.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] Traditional data center equipment maintenance relies on periodic inspections and reactive measures after malfunctions occur, resulting in wasted costs and equipment downtime. Furthermore, the lack of real-time detection of potential problems and rapid response capabilities creates a need for improved equipment reliability.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes a device for acquiring multiple data sets, a device for applying a machine learning algorithm for analyzing the multiple data sets, a function for predicting equipment deterioration or abnormalities based on the analysis, a function for generating a maintenance plan based on the prediction, a function for dynamically adjusting the maintenance plan, a device for providing category-related guidance in real time, and a function for sending notifications to users based on the guidance. This makes it possible to predict equipment abnormalities in the data center in advance and provide guidance for immediate response, thereby reducing unnecessary maintenance and improving equipment reliability.

[0451] "Data" refers to information acquired by the system, such as temperature, vibration, and usage history.

[0452] A "machine learning algorithm" is a mathematical and statistical method used to analyze collected data.

[0453] The "function to predict deterioration or abnormalities" refers to the ability to predict changes in the device's state in advance based on analysis results.

[0454] The "maintenance plan generation function" is the process of creating an optimal maintenance schedule based on predicted deterioration and anomalies.

[0455] The "dynamic adjustment function" refers to the ability to automatically optimize existing plans and schedules in response to environmental changes or new data.

[0456] A "device that provides guidance" is a device that, based on the analytical information obtained, presents appropriate countermeasures to the user.

[0457] The "notification sending function" is a means of communicating important information from the system to the user.

[0458] The system implementing this invention is designed for the efficient operation and maintenance of equipment within a data center. A server plays a central role in the system, collecting and processing data from various sensors in real time. Specific hardware used includes temperature and vibration sensors installed in each server rack. Data from these sensors is transmitted via a network to a server in the cloud.

[0459] The server uses machine learning algorithms to analyze the collected data and predict the deterioration status and signs of abnormalities in the equipment. This analysis utilizes TensorFlow, enabling advanced pattern recognition. Based on the analysis results, the server automatically generates a maintenance plan based on the predictions and has the capability to dynamically adjust it according to the current operating environment.

[0460] Users receive notifications from the cloud server on their smartphones and tablets. These notifications include information on abnormal equipment conditions and recommended maintenance procedures, enabling rapid, real-time responses. For example, if the temperature of a specific server rack in the data center rises, this is immediately detected, and a notification suggesting cooling measures is sent to the user's device. This process reduces unnecessary maintenance and improves operational efficiency.

[0461] Because it is a generative AI model, it can combine historical data with real-time data to enable more accurate predictions, and based on those predictions, an appropriate maintenance plan can be provided. An example of a prompt message is, "Enter the current temperature data for the server rack and predict the next maintenance date."

[0462] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0463] Step 1:

[0464] The server collects data in real time from temperature and vibration sensors within the data center. The input consists of numerical temperature and vibration data sent from each sensor, which is stored in an initial database. The output is a dataset of sensor information that is updated periodically.

[0465] Step 2:

[0466] The server applies machine learning algorithms to the collected data and analyzes the dataset. Specifically, it uses an artificial intelligence model based on TensorFlow to identify patterns in the data and evaluate the possibility of equipment degradation or anomalies. Its input is the dataset obtained in step 1, and its output is an evaluation report that includes anomaly predictions.

[0467] Step 3:

[0468] Based on the analysis results, the server automatically generates a maintenance plan based on predicted degradation and anomalies. The input here is the evaluation report from step 2, and the output is the proposed maintenance schedule. Specifically, the server refers to historical data and incorporates the optimal maintenance timing and procedures into the plan.

[0469] Step 4:

[0470] The server dynamically adjusts the generated maintenance plan based on real-time data and operational status. Inputs include the latest sensor data and environmental information, and output is the adjusted, up-to-date maintenance schedule.

[0471] Step 5:

[0472] The terminal receives information from the server and sends notifications to the user. These notifications provide the user with information regarding anomalies, recommended actions, and maintenance schedules. The input is maintenance information sent from the server, and the output is operation guides and alert messages displayed on the user terminal.

[0473] These steps enable data center equipment to operate more efficiently and allow users to detect anomalies early and respond appropriately. By employing generative AI models, the system continuously learns from past data and better predicts future maintenance. An example of a prompt message is, "Analyze data from the anomaly sensor and adjust the maintenance plan as needed."

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] This invention provides a system that combines an emotion engine to improve the operational efficiency of wireless base stations and support the psychological stability of users. In this system, a server plays a central role. The server acquires data such as temperature, vibration, and usage history in real time from multiple sensors installed at the base station. The collected data is centrally managed and input into an artificial intelligence model for analysis.

[0476] Subsequently, the server uses an artificial intelligence model to accurately predict equipment degradation and anomalies, and based on this, creates an optimal maintenance plan. This includes a process of improving the model's accuracy by utilizing historical data. The maintenance plan is dynamically adjusted in response to real-time data.

[0477] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The server acquires the user's emotional data through emotion sensors and analyzes it to determine the user's state. This emotional data influences the content and method of notifications provided to the user. For example, if the user is feeling anxious, the server sends more detailed information or reassuring messages to the device.

[0478] To give a specific example, if the temperature at a base station suddenly rises, and the server detects the anomaly, and the emotion engine determines that the user of the assigned technician is feeling anxious, the server will not only take swift action but also issue an alert confirming that the situation is under control. This allows the user to respond to the situation calmly and effectively.

[0479] This integrated system aims to improve the user experience while ensuring the safety and efficient operation and maintenance of base stations.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The server collects data such as temperature, vibration, and usage history in real time from sensors installed at wireless base stations. The sensor data is periodically updated and stored in the server's database.

[0483] Step 2:

[0484] The server preprocesses the collected data, performing tasks such as noise reduction and missing value imputation. Algorithms such as moving averages are used to maintain data accuracy.

[0485] Step 3:

[0486] The server inputs pre-processed data into an artificial intelligence model to predict equipment degradation and detect anomalies. The AI ​​model utilizes historical data and employs a feedback loop to improve analysis accuracy.

[0487] Step 4:

[0488] If a server malfunction is predicted, an optimal maintenance plan is generated. The generated plan includes countermeasures and execution timing based on the predicted problem.

[0489] Step 5:

[0490] The server acquires user emotion data from emotion sensors and analyzes it using an emotion engine. Based on the user's emotional state, it generates specific response messages.

[0491] Step 6:

[0492] When the server sends an anomaly notification to the terminal, it considers the results of the emotion engine and sends the notification in an appropriate tone and content based on the user's emotional state. This allows for measures such as including additional information to alleviate anxiety.

[0493] Step 7:

[0494] The user receives a notification from their device and performs maintenance work based on the provided instructions. The device provides detailed information to support the user's actions.

[0495] Step 8:

[0496] The server performs the next analysis based on the latest sensor and sentiment data, continuously improving the model to enhance the overall predictive accuracy of the system. This increases system reliability and user satisfaction.

[0497] (Example 2)

[0498] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In wireless base stations and other equipment, there is a need to ensure efficient operation and maintenance, as well as security, while simultaneously providing information that takes into account the user's psychological state. However, conventional systems have struggled to accurately predict equipment deterioration or malfunctions, or to provide real-time feedback that appropriately reflects the user's emotions. Furthermore, there is a lack of dynamic adjustments to generated maintenance plans and notification methods that respond to the user's condition, resulting in challenges in responding quickly and appropriately.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0501] In this invention, the server includes means for acquiring multiple observation data, means for managing and pre-processing the observation data, and means for applying a generated AI model using the pre-processed observation data. This makes it possible to predict equipment deterioration and abnormalities with high accuracy. Furthermore, it enables dynamic adjustment of maintenance plans and the provision of feedback that takes into account the user's psychological state, thereby realizing efficient and safe operation.

[0502] "Observation data" refers to information acquired in real time from various sensors at wireless base stations, including temperature, vibration, and usage history.

[0503] A "generative AI model" is an artificial intelligence algorithm trained using a machine learning framework, and it is a model that analyzes observational data to predict equipment degradation and anomalies.

[0504] A "maintenance plan" is a work plan created based on predictions of equipment deterioration and malfunctions, aimed at maintaining the stable and efficient operation of the equipment.

[0505] "User's psychological state" refers to information about the user's emotions, such as anxiety and feelings of security, obtained through emotion sensors.

[0506] "Notifications" are a means of conveying information, warnings, and reassuring messages generated by a server to the user.

[0507] "Data preprocessing" refers to a series of procedures that remove outliers from acquired observational data and prepare it for analysis.

[0508] "Dynamic adjustment" is a process that modifies maintenance plans and notifications in real time in response to changes in the status of the equipment and users.

[0509] This invention is constructed as a system integrating multiple technologies to improve the operational efficiency of wireless base stations and support the psychological well-being of users. The server plays the primary role, and its details are described below.

[0510] The server acquires real-time observation data using temperature and vibration sensors installed at wireless base stations. These sensors provide data on the environmental conditions and physical operation of the system at the base stations. The server acquires data every five minutes and stores it in a central database. This data is managed and preprocessed to create an analyzable format with outliers removed.

[0511] Next, the server inputs the pre-processed observation data into a generative AI model using TensorFlow or PyTorch. This generative AI model is trained using historical data and can accurately predict equipment degradation and anomalies. Based on these predictions, the server dynamically generates an effective maintenance plan, constantly adjusting it according to the latest data.

[0512] Furthermore, the server analyzes the user's psychological state obtained from emotion sensors and provides appropriate feedback. If the user is feeling anxious, the server generates a reassuring message and sends it to the device to promote the user's psychological stability. Notifications are sent in real time, supporting the user's situation and the implementation of appropriate measures.

[0513] For example, if vibration data at a base station shows an abnormal pattern, the server detects the anomaly and sends a notification to the technician. If the emotional data indicates that the technician is feeling anxious, the server prompts them with a message such as, "The situation is under control. Necessary measures are being taken."

[0514] An example of a prompt message for a generated AI model would be: "Based on the base station's temperature data, predict the likelihood of an anomaly occurring and suggest the necessary actions."

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The server acquires observation data in real time from temperature and vibration sensors installed at wireless base stations. Specifically, data from each sensor is automatically sent to the server every 5 minutes and stored in a central database. It receives observation data from sensors as input and generates data stored in the central database as output.

[0518] Step 2:

[0519] The server preprocesses the acquired observational data. Specifically, it detects outliers and normalizes the data. Data processing involves removing abnormal temperature and vibration values ​​and converting the data into a format suitable for analysis. The input is the unprocessed observational data accumulated in step 1, and the output is a clean dataset.

[0520] Step 3:

[0521] The server inputs pre-processed data into a generating AI model to predict equipment degradation and anomalies. Because the model is trained using historical data, it can make highly accurate predictions. The data calculations include anomaly detection and degradation prediction by the model. The input is pre-processed data, and the output is the prediction result.

[0522] Step 4:

[0523] The server dynamically generates an optimal maintenance plan based on the prediction results from the generated AI model. Specifically, it plans the work required to address the predicted anomalies and assigns them to technicians as prioritized tasks. The input is the prediction results obtained in step 3, and the output is a maintenance work plan.

[0524] Step 5:

[0525] The server acquires and analyzes data on the user's psychological state from their emotion sensor. Specifically, it determines whether the user is feeling anxious or stressed and adjusts the feedback accordingly. The input is emotional data, and the output is a notification tailored to the user's psychological state.

[0526] Step 6:

[0527] The server sends a notification to the terminal based on the analysis results. Specifically, it provides the user with a message that reassures them that "the situation is under control," as well as specific instructions for action. The input is the results of steps 4 and 5, and the output is the notification message displayed on the terminal.

[0528] (Application Example 2)

[0529] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] In recent years, the operation of wireless communication equipment has required efficient maintenance and management, as well as psychological stability for users. However, current systems are insufficient for detecting equipment anomalies and developing efficient maintenance plans, and they also have difficulty appropriately addressing users' emotions. This can lead to increased user anxiety and disrupt operations. To address this, a system is needed that integrates early detection of anomalies with notifications that provide reassurance.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0532] In this invention, the server includes means for collecting multiple pieces of information, means for applying an artificial intelligence algorithm for analyzing the information, and means for recognizing the user's emotional state and dynamically adjusting the notification content. This enables efficient device management and improved user psychological stability.

[0533] "Multiple pieces of information" refers to various types of data acquired from sensors, such as vibration, temperature, and usage history.

[0534] An "artificial intelligence algorithm" is a computational method used to analyze collected information and perform anomaly detection and predictive analysis.

[0535] "Means for predicting equipment deterioration or abnormalities" refers to a function that uses artificial intelligence algorithms to evaluate the operating status of equipment and identify potential failures or problems in advance.

[0536] "Means of generating maintenance plans" refers to the process of creating an efficient and effective maintenance schedule based on predicted anomalies.

[0537] "Means for adjusting maintenance plans" refers to a function that optimizes existing maintenance plans in response to real-time data and new information.

[0538] "Means of recognizing the user's emotional state" refers to a function that detects the user's psychological response via an emotion engine and determines that state.

[0539] "Means for dynamically adjusting notification content" refers to a mechanism that changes the content of information and warning messages provided according to the user's emotional state.

[0540] A "means of supporting psychological stability" refers to a system that provides information and messages to reassure users when they are feeling anxious or stressed.

[0541] This invention provides a system that supports the efficient operation of wireless communication devices while also supporting the psychological well-being of users. The server aggregates data acquired in real time from multiple sensors and uses artificial intelligence algorithms to predict equipment degradation and abnormalities with high accuracy. This analysis process utilizes programming languages ​​such as Python and incorporates machine learning libraries such as TensorFlow and Scikit-learn.

[0542] The server uses historical data to improve the accuracy of its artificial intelligence algorithms and dynamically adjusts maintenance plans when anomalies are detected. It also utilizes an emotion recognition API to recognize the user's emotional state. This allows for analysis of the user's psychological condition and dynamic adjustment of notification content based on the results.

[0543] Furthermore, if the device detects that the user is feeling anxious, a reassuring message is immediately delivered. This establishes a system that allows users to respond calmly and effectively.

[0544] For example, if suspicious vibrations are detected within a facility and the AI ​​classifies them as an anomaly, the user will receive instructions for immediate action along with a message assuring them that safety is ensured. This system reduces user anxiety and enables reliable equipment management.

[0545] An example of an input prompt for the generating AI model might be something like, "Please have the AI ​​analyze the recent vibration patterns within the facility and clarify what this anomaly indicates."

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The server collects data such as temperature, vibration, and usage history in real time from multiple sensors. This input data is aggregated as raw data on the server and centrally managed.

[0549] Step 2:

[0550] The server preprocesses the collected raw data and converts it into a format suitable for machine learning. This process includes denoising and normalizing the data. The preprocessed data is then prepared as a dataset that can be easily analyzed by artificial intelligence algorithms.

[0551] Step 3:

[0552] The server uses pre-processed data as input to run an artificial intelligence algorithm and predict equipment degradation and anomalies. In this step, historical data is also incorporated to improve the model's accuracy. An anomaly prediction report is generated as output.

[0553] Step 4:

[0554] The server creates an efficient maintenance plan based on anomaly prediction reports. It also has the capability to dynamically adjust the maintenance plan in response to real-time data changes. The output at this stage is the updated maintenance schedule.

[0555] Step 5:

[0556] The server analyzes emotional data collected through an emotion recognition API to recognize the user's emotional state. Based on the analysis results, it determines whether the user is feeling anxious.

[0557] Step 6:

[0558] The device generates and delivers notifications that are dynamically adjusted based on the user's emotional state. These notifications include necessary actions and reassuring content.

[0559] Step 7:

[0560] Users can check notifications received from their devices and respond to the situation calmly and effectively. This allows for both improved operational efficiency and psychological stability.

[0561] 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.

[0562] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0563] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] 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.

[0567] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0568] 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.

[0569] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0570] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0571] 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.

[0572] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0573] 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.

[0574] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0575] The 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.

[0576] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0577] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0578] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. To realize this system, a server plays a central role in acquiring information using multiple sensors and data acquisition devices. The server collects real-time data such as temperature, vibration, and usage history in bulk and uses an artificial intelligence model to analyze this data.

[0579] Based on the collected data, the server accurately predicts the deterioration status and signs of malfunction of the equipment. Based on this, the server automatically generates an appropriate maintenance plan using the prediction results. Furthermore, this plan is periodically reviewed and dynamically adjusted by the server in accordance with the operating environment and new data.

[0580] As a concrete example, if the temperature of a wireless base station exceeds a certain threshold, the server immediately detects this data anomaly and uses an AI model to predict the impact of this condition. Based on the prediction, the server sends a notification to the terminal and provides recommended steps for maintenance personnel to take appropriate action. This process enables users to respond effectively and quickly.

[0581] Furthermore, the server references historical data to improve the prediction accuracy of the AI ​​model, reduce unnecessary maintenance, and support rapid responses as needed. This allows for both reduced operating costs and improved service reliability.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The server collects real-time data on temperature, vibration, and usage history from sensors installed at wireless base stations. The sensors transmit the data to the server via IoT protocols, and the server stores this data in a database.

[0585] Step 2:

[0586] The data stored by the server is preprocessed. Specifically, noise is removed from the data and missing values ​​are filled in. In this process, a moving average filter is used to reduce noise.

[0587] Step 3:

[0588] The server inputs pre-processed data into an artificial intelligence model for data analysis. The analysis calculates an anomaly score and identifies signs of equipment deterioration or malfunction.

[0589] Step 4:

[0590] The server automatically generates a maintenance plan based on the analysis results. It determines the optimal timing and content by referring to past maintenance history. This plan is dynamically adjusted as needed.

[0591] Step 5:

[0592] If the server detects an anomaly, it automatically sends a notification to the terminal. The notification includes specific recommended actions, allowing users to take immediate action.

[0593] Step 6:

[0594] Users can check information from the server via their terminals and perform maintenance tasks according to the indicated procedures. This allows for effective maintenance while eliminating unnecessary work.

[0595] Step 7:

[0596] The server continuously learns the AI ​​model while reflecting the latest data, improving prediction accuracy. This will also be effectively utilized in future maintenance planning.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] In modern communication infrastructure, the proper maintenance and operation of wireless base stations are crucial for ensuring service quality and reliability. However, conventional systems often lack sufficient early detection of anomalies and predictive maintenance planning, leading to high operating costs and increased risk of service downtime. In addition, unnecessary maintenance and sudden failure responses can occur, potentially reducing work efficiency.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes means for collecting real-time information from multiple detection devices, means for applying a machine learning model for analyzing the real-time information, and means for predicting device deterioration or abnormalities based on the analysis. This enables the detection of device abnormalities and the generation of predictive maintenance plans, thereby reducing operating costs and improving service reliability.

[0602] A "detection device" refers to sensors or devices used to collect data from the environment or equipment.

[0603] "Real-time information" refers to data that is available almost instantly and shows processes or states that are currently in progress.

[0604] A "machine learning model" is an algorithm that learns patterns and rules based on large amounts of data and uses that knowledge to make predictions and perform analyses on new data.

[0605] "Analysis" is the process of breaking down data and identifying useful information and trends based on that analysis.

[0606] "Degradation" refers to a state in which the performance of a device or system declines over time or with use.

[0607] An "anomaly" is an unexpected event or pattern that deviates from normal operation or state.

[0608] A "maintenance plan" is a set of schedules and procedures designed for the maintenance and management of equipment and systems.

[0609] "Dynamic adjustment" refers to the process of automatically modifying and adapting plans and operations in response to changing circumstances.

[0610] A "terminal" refers to an electronic device or computer device used to receive information.

[0611] This invention provides a system for efficiently managing information related to the operation of wireless base stations and optimizing maintenance. The system is centered around a server and has the function of collecting real-time information using multiple detection devices. The server acquires data using temperature sensors and vibration sensors and applies machine learning models to analyze this data.

[0612] Specifically, the server uses software such as TensorFlow and PyTorch to create machine learning models and analyze data. Based on the analyzed data, the server predicts the deterioration status and signs of anomalies in the equipment. These predictions are useful for automatically generating maintenance plans.

[0613] Based on the prediction results, the server sends notifications to terminals and provides suggested steps for maintenance personnel to take appropriate action. This enables cost reduction and improved service reliability.

[0614] For example, if the temperature of a wireless base station becomes too high, the server detects this anomaly and uses an AI model to predict its impact. Based on this result, the server sends a notification to the terminal such as "Temperature anomaly detected: Please check the cooling system immediately," allowing the user to take prompt action accordingly.

[0615] As an example of a prompt, the AI ​​model might receive a request stating, "Predict the impact if the temperature of a wireless base station exceeds a certain threshold, and propose an appropriate maintenance plan."

[0616] This system allows servers to respond quickly to environmental changes and dynamically adjust maintenance plans, significantly improving operational efficiency and reliability.

[0617] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0618] Step 1:

[0619] The server collects real-time information such as temperature, vibration, and usage history from various detection devices. Input data includes data from temperature and vibration sensors. Based on this, the server constructs a raw dataset and prepares it for analysis.

[0620] Step 2:

[0621] The server feeds the collected data into a machine learning model. The input data consists of various parameters such as temperature and vibration, and the model performs analysis based on this data. Specific data processing includes data normalization and preprocessing to perform anomaly detection and pattern prediction. The output provides anomaly detection results and degradation prediction information.

[0622] Step 3:

[0623] The server automatically generates a maintenance plan based on the analysis results. The input is the results of anomaly detection obtained from the analyzed data. The server uses this to determine what maintenance is required and outputs specific maintenance procedures.

[0624] Step 4:

[0625] The server sends a notification to the terminal based on the generated maintenance plan. This step uses the created maintenance plan as input. The output to the terminal is a procedure designed to allow the maintenance user to respond quickly. It includes specific action instructions such as "Temperature anomaly detected: Check the cooling system immediately."

[0626] Step 5:

[0627] The server references historical data to further improve the prediction accuracy of the machine learning model. The input here consists of past maintenance history and operational data. Based on this, the server performs data calculations and retrains the model to improve accuracy. The output is the improved prediction model.

[0628] (Application Example 1)

[0629] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] Traditional data center equipment maintenance relies on periodic inspections and reactive measures after malfunctions occur, resulting in wasted costs and equipment downtime. Furthermore, the lack of real-time detection of potential problems and rapid response capabilities creates a need for improved equipment reliability.

[0631] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0632] In this invention, the server includes a device for acquiring multiple data sets, a device for applying a machine learning algorithm for analyzing the multiple data sets, a function for predicting equipment deterioration or abnormalities based on the analysis, a function for generating a maintenance plan based on the prediction, a function for dynamically adjusting the maintenance plan, a device for providing category-related guidance in real time, and a function for sending notifications to users based on the guidance. This makes it possible to predict equipment abnormalities in the data center in advance and provide guidance for immediate response, thereby reducing unnecessary maintenance and improving equipment reliability.

[0633] "Data" refers to information acquired by the system, such as temperature, vibration, and usage history.

[0634] A "machine learning algorithm" is a mathematical and statistical method used to analyze collected data.

[0635] The "function to predict deterioration or abnormalities" refers to the ability to predict changes in the device's state in advance based on analysis results.

[0636] The "maintenance plan generation function" is the process of creating an optimal maintenance schedule based on predicted deterioration and anomalies.

[0637] The "dynamic adjustment function" refers to the ability to automatically optimize existing plans and schedules in response to environmental changes or new data.

[0638] A "device that provides guidance" is a device that, based on the analytical information obtained, presents appropriate countermeasures to the user.

[0639] The "notification sending function" is a means of communicating important information from the system to the user.

[0640] The system implementing this invention is designed for the efficient operation and maintenance of equipment within a data center. A server plays a central role in the system, collecting and processing data from various sensors in real time. Specific hardware used includes temperature and vibration sensors installed in each server rack. Data from these sensors is transmitted via a network to a server in the cloud.

[0641] The server uses machine learning algorithms to analyze the collected data and predict the deterioration status and signs of abnormalities in the equipment. This analysis utilizes TensorFlow, enabling advanced pattern recognition. Based on the analysis results, the server automatically generates a maintenance plan based on the predictions and has the capability to dynamically adjust it according to the current operating environment.

[0642] Users receive notifications from the cloud server on their smartphones and tablets. These notifications include information on abnormal equipment conditions and recommended maintenance procedures, enabling rapid, real-time responses. For example, if the temperature of a specific server rack in the data center rises, this is immediately detected, and a notification suggesting cooling measures is sent to the user's device. This process reduces unnecessary maintenance and improves operational efficiency.

[0643] Because it is a generative AI model, it can combine historical data with real-time data to enable more accurate predictions, and based on those predictions, an appropriate maintenance plan can be provided. An example of a prompt message is, "Enter the current temperature data for the server rack and predict the next maintenance date."

[0644] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0645] Step 1:

[0646] The server collects data in real time from temperature and vibration sensors within the data center. The input consists of numerical temperature and vibration data sent from each sensor, which is stored in an initial database. The output is a dataset of sensor information that is updated periodically.

[0647] Step 2:

[0648] The server applies machine learning algorithms to the collected data and analyzes the dataset. Specifically, it uses an artificial intelligence model based on TensorFlow to identify patterns in the data and evaluate the possibility of equipment degradation or anomalies. Its input is the dataset obtained in step 1, and its output is an evaluation report that includes anomaly predictions.

[0649] Step 3:

[0650] Based on the analysis results, the server automatically generates a maintenance plan based on predicted degradation and anomalies. The input here is the evaluation report from step 2, and the output is the proposed maintenance schedule. Specifically, the server refers to historical data and incorporates the optimal maintenance timing and procedures into the plan.

[0651] Step 4:

[0652] The server dynamically adjusts the generated maintenance plan based on real-time data and operational status. Inputs include the latest sensor data and environmental information, and output is the adjusted, up-to-date maintenance schedule.

[0653] Step 5:

[0654] The terminal receives information from the server and sends notifications to the user. These notifications provide the user with information regarding anomalies, recommended actions, and maintenance schedules. The input is maintenance information sent from the server, and the output is operation guides and alert messages displayed on the user terminal.

[0655] These steps enable data center equipment to operate more efficiently and allow users to detect anomalies early and respond appropriately. By employing generative AI models, the system continuously learns from past data and better predicts future maintenance. An example of a prompt message is, "Analyze data from the anomaly sensor and adjust the maintenance plan as needed."

[0656] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0657] This invention provides a system that combines an emotion engine to improve the operational efficiency of wireless base stations and support the psychological stability of users. In this system, a server plays a central role. The server acquires data such as temperature, vibration, and usage history in real time from multiple sensors installed at the base station. The collected data is centrally managed and input into an artificial intelligence model for analysis.

[0658] Subsequently, the server uses an artificial intelligence model to accurately predict equipment degradation and anomalies, and based on this, creates an optimal maintenance plan. This includes a process of improving the model's accuracy by utilizing historical data. The maintenance plan is dynamically adjusted in response to real-time data.

[0659] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The server acquires the user's emotional data through emotion sensors and analyzes it to determine the user's state. This emotional data influences the content and method of notifications provided to the user. For example, if the user is feeling anxious, the server sends more detailed information or reassuring messages to the device.

[0660] To give a specific example, if the temperature at a base station suddenly rises, and the server detects the anomaly, and the emotion engine determines that the user of the assigned technician is feeling anxious, the server will not only take swift action but also issue an alert confirming that the situation is under control. This allows the user to respond to the situation calmly and effectively.

[0661] This integrated system aims to improve the user experience while ensuring the safety and efficient operation and maintenance of base stations.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server collects data such as temperature, vibration, and usage history in real time from sensors installed at wireless base stations. The sensor data is periodically updated and stored in the server's database.

[0665] Step 2:

[0666] The server preprocesses the collected data, performing tasks such as noise reduction and missing value imputation. Algorithms such as moving averages are used to maintain data accuracy.

[0667] Step 3:

[0668] The server inputs pre-processed data into an artificial intelligence model to predict equipment degradation and detect anomalies. The AI ​​model utilizes historical data and employs a feedback loop to improve analysis accuracy.

[0669] Step 4:

[0670] If a server malfunction is predicted, an optimal maintenance plan is generated. The generated plan includes countermeasures and execution timing based on the predicted problem.

[0671] Step 5:

[0672] The server acquires user emotion data from emotion sensors and analyzes it using an emotion engine. Based on the user's emotional state, it generates specific response messages.

[0673] Step 6:

[0674] When the server sends an anomaly notification to the terminal, it considers the results of the emotion engine and sends the notification in an appropriate tone and content based on the user's emotional state. This allows for measures such as including additional information to alleviate anxiety.

[0675] Step 7:

[0676] The user receives a notification from their device and performs maintenance work based on the provided instructions. The device provides detailed information to support the user's actions.

[0677] Step 8:

[0678] The server performs the next analysis based on the latest sensor and sentiment data, continuously improving the model to enhance the overall predictive accuracy of the system. This increases system reliability and user satisfaction.

[0679] (Example 2)

[0680] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] In wireless base stations and other equipment, there is a need to ensure efficient operation and maintenance, as well as security, while simultaneously providing information that takes into account the user's psychological state. However, conventional systems have struggled to accurately predict equipment deterioration or malfunctions, or to provide real-time feedback that appropriately reflects the user's emotions. Furthermore, there is a lack of dynamic adjustments to generated maintenance plans and notification methods that respond to the user's condition, resulting in challenges in responding quickly and appropriately.

[0682] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0683] In this invention, the server includes means for acquiring multiple observation data, means for managing and pre-processing the observation data, and means for applying a generated AI model using the pre-processed observation data. This makes it possible to predict equipment deterioration and abnormalities with high accuracy. Furthermore, it enables dynamic adjustment of maintenance plans and the provision of feedback that takes into account the user's psychological state, thereby realizing efficient and safe operation.

[0684] "Observation data" refers to information acquired in real time from various sensors at wireless base stations, including temperature, vibration, and usage history.

[0685] A "generative AI model" is an artificial intelligence algorithm trained using a machine learning framework, and it is a model that analyzes observational data to predict equipment degradation and anomalies.

[0686] A "maintenance plan" is a work plan created based on predictions of equipment deterioration and malfunctions, aimed at maintaining the stable and efficient operation of the equipment.

[0687] "User's psychological state" refers to information about the user's emotions, such as anxiety and feelings of security, obtained through emotion sensors.

[0688] "Notifications" are a means of conveying information, warnings, and reassuring messages generated by a server to the user.

[0689] "Data preprocessing" refers to a series of procedures that remove outliers from acquired observational data and prepare it for analysis.

[0690] "Dynamic adjustment" is a process that modifies maintenance plans and notifications in real time in response to changes in the status of the equipment and users.

[0691] This invention is constructed as a system integrating multiple technologies to improve the operational efficiency of wireless base stations and support the psychological well-being of users. The server plays the primary role, and its details are described below.

[0692] The server acquires real-time observation data using temperature and vibration sensors installed at wireless base stations. These sensors provide data on the environmental conditions and physical operation of the system at the base stations. The server acquires data every five minutes and stores it in a central database. This data is managed and preprocessed to create an analyzable format with outliers removed.

[0693] Next, the server inputs the pre-processed observation data into a generative AI model using TensorFlow or PyTorch. This generative AI model is trained using historical data and can accurately predict equipment degradation and anomalies. Based on these predictions, the server dynamically generates an effective maintenance plan, constantly adjusting it according to the latest data.

[0694] Furthermore, the server analyzes the user's psychological state obtained from emotion sensors and provides appropriate feedback. If the user is feeling anxious, the server generates a reassuring message and sends it to the device to promote the user's psychological stability. Notifications are sent in real time, supporting the user's situation and the implementation of appropriate measures.

[0695] For example, if vibration data at a base station shows an abnormal pattern, the server detects the anomaly and sends a notification to the technician. If the emotional data indicates that the technician is feeling anxious, the server prompts them with a message such as, "The situation is under control. Necessary measures are being taken."

[0696] An example of a prompt message for a generated AI model would be: "Based on the base station's temperature data, predict the likelihood of an anomaly occurring and suggest the necessary actions."

[0697] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0698] Step 1:

[0699] The server acquires observation data in real time from temperature and vibration sensors installed at wireless base stations. Specifically, data from each sensor is automatically sent to the server every 5 minutes and stored in a central database. It receives observation data from sensors as input and generates data stored in the central database as output.

[0700] Step 2:

[0701] The server preprocesses the acquired observational data. Specifically, it detects outliers and normalizes the data. Data processing involves removing abnormal temperature and vibration values ​​and converting the data into a format suitable for analysis. The input is the unprocessed observational data accumulated in step 1, and the output is a clean dataset.

[0702] Step 3:

[0703] The server inputs pre-processed data into a generating AI model to predict equipment degradation and anomalies. Because the model is trained using historical data, it can make highly accurate predictions. The data calculations include anomaly detection and degradation prediction by the model. The input is pre-processed data, and the output is the prediction result.

[0704] Step 4:

[0705] The server dynamically generates an optimal maintenance plan based on the prediction results from the generated AI model. Specifically, it plans the work required to address the predicted anomalies and assigns them to technicians as prioritized tasks. The input is the prediction results obtained in step 3, and the output is a maintenance work plan.

[0706] Step 5:

[0707] The server acquires and analyzes data on the user's psychological state from their emotion sensor. Specifically, it determines whether the user is feeling anxious or stressed and adjusts the feedback accordingly. The input is emotional data, and the output is a notification tailored to the user's psychological state.

[0708] Step 6:

[0709] The server sends a notification to the terminal based on the analysis results. Specifically, it provides the user with a message that reassures them that "the situation is under control," as well as specific instructions for action. The input is the results of steps 4 and 5, and the output is the notification message displayed on the terminal.

[0710] (Application Example 2)

[0711] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] In recent years, the operation of wireless communication equipment has required efficient maintenance and management, as well as psychological stability for users. However, current systems are insufficient for detecting equipment anomalies and developing efficient maintenance plans, and they also have difficulty appropriately addressing users' emotions. This can lead to increased user anxiety and disrupt operations. To address this, a system is needed that integrates early detection of anomalies with notifications that provide reassurance.

[0713] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0714] In this invention, the server includes means for collecting multiple pieces of information, means for applying an artificial intelligence algorithm for analyzing the information, and means for recognizing the user's emotional state and dynamically adjusting the notification content. This enables efficient device management and improved user psychological stability.

[0715] "Multiple pieces of information" refers to various types of data acquired from sensors, such as vibration, temperature, and usage history.

[0716] An "artificial intelligence algorithm" is a computational method used to analyze collected information and perform anomaly detection and predictive analysis.

[0717] "Means for predicting equipment deterioration or abnormalities" refers to a function that uses artificial intelligence algorithms to evaluate the operating status of equipment and identify potential failures or problems in advance.

[0718] "Means of generating maintenance plans" refers to the process of creating an efficient and effective maintenance schedule based on predicted anomalies.

[0719] "Means for adjusting maintenance plans" refers to a function that optimizes existing maintenance plans in response to real-time data and new information.

[0720] "Means of recognizing the user's emotional state" refers to a function that detects the user's psychological response via an emotion engine and determines that state.

[0721] "Means for dynamically adjusting notification content" refers to a mechanism that changes the content of information and warning messages provided according to the user's emotional state.

[0722] A "means of supporting psychological stability" refers to a system that provides information and messages to reassure users when they are feeling anxious or stressed.

[0723] This invention provides a system that supports the efficient operation of wireless communication devices while also supporting the psychological well-being of users. The server aggregates data acquired in real time from multiple sensors and uses artificial intelligence algorithms to predict equipment degradation and abnormalities with high accuracy. This analysis process utilizes programming languages ​​such as Python and incorporates machine learning libraries such as TensorFlow and Scikit-learn.

[0724] The server uses historical data to improve the accuracy of its artificial intelligence algorithms and dynamically adjusts maintenance plans when anomalies are detected. It also utilizes an emotion recognition API to recognize the user's emotional state. This allows for analysis of the user's psychological condition and dynamic adjustment of notification content based on the results.

[0725] Furthermore, if the device detects that the user is feeling anxious, a reassuring message is immediately delivered. This establishes a system that allows users to respond calmly and effectively.

[0726] For example, if suspicious vibrations are detected within a facility and the AI ​​classifies them as an anomaly, the user will receive instructions for immediate action along with a message assuring them that safety is ensured. This system reduces user anxiety and enables reliable equipment management.

[0727] An example of an input prompt for the generating AI model might be something like, "Please have the AI ​​analyze the recent vibration patterns within the facility and clarify what this anomaly indicates."

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The server collects data such as temperature, vibration, and usage history in real time from multiple sensors. This input data is aggregated as raw data on the server and centrally managed.

[0731] Step 2:

[0732] The server preprocesses the collected raw data and converts it into a format suitable for machine learning. This process includes denoising and normalizing the data. The preprocessed data is then prepared as a dataset that can be easily analyzed by artificial intelligence algorithms.

[0733] Step 3:

[0734] The server uses pre-processed data as input to run an artificial intelligence algorithm and predict equipment degradation and anomalies. In this step, historical data is also incorporated to improve the model's accuracy. An anomaly prediction report is generated as output.

[0735] Step 4:

[0736] The server creates an efficient maintenance plan based on anomaly prediction reports. It also has the capability to dynamically adjust the maintenance plan in response to real-time data changes. The output at this stage is the updated maintenance schedule.

[0737] Step 5:

[0738] The server analyzes emotional data collected through an emotion recognition API to recognize the user's emotional state. Based on the analysis results, it determines whether the user is feeling anxious.

[0739] Step 6:

[0740] The device generates and delivers notifications that are dynamically adjusted based on the user's emotional state. These notifications include necessary actions and reassuring content.

[0741] Step 7:

[0742] Users can check notifications received from their devices and respond to the situation calmly and effectively. This allows for both improved operational efficiency and psychological stability.

[0743] 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.

[0744] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0745] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0746] 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.

[0747] Figure 9 shows an 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0748] 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.

[0749] 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.

[0750] 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, motorcycles, etc., 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, for example, based 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.

[0751] 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."

[0752] 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.

[0753] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0754] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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 the like 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.

[0763] 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.

[0764] The following is further disclosed regarding the embodiments described above.

[0765] (Claim 1)

[0766] Multiple means of collecting information,

[0767] Means for applying an artificial intelligence model for analyzing the aforementioned multiple pieces of information,

[0768] A means for predicting deterioration or abnormalities of the device based on the aforementioned analysis,

[0769] Means for generating a maintenance plan based on the aforementioned prediction,

[0770] Means for adjusting the aforementioned maintenance plan,

[0771] A system equipped with [that feature].

[0772] (Claim 2)

[0773] The system according to claim 1, further comprising means for improving the accuracy of the artificial intelligence model using historical data.

[0774] (Claim 3)

[0775] The system according to claim 1, further comprising means for automatically issuing a notification when the aforementioned abnormality is detected.

[0776] "Example 1"

[0777] (Claim 1)

[0778] A means for collecting real-time information from multiple detection devices,

[0779] Means for applying a machine learning model to analyze the aforementioned real-time information,

[0780] A means for predicting deterioration or abnormalities of the device based on the aforementioned analysis,

[0781] A means for automatically generating a maintenance plan based on the aforementioned prediction,

[0782] Means for dynamically adjusting the aforementioned maintenance plan,

[0783] Means for transmitting action guidelines to a terminal based on the aforementioned predictions and anomalies,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, further comprising means for improving the prediction accuracy of the machine learning model using historical information.

[0787] (Claim 3)

[0788] The system according to claim 1, further comprising means for automatically transmitting a communication when the aforementioned abnormality is detected.

[0789] "Application Example 1"

[0790] (Claim 1)

[0791] A device that acquires multiple data,

[0792] A device for applying a machine learning algorithm to analyze the aforementioned multiple data sets,

[0793] A function to predict device deterioration or abnormalities based on the aforementioned analysis,

[0794] A function to generate a maintenance plan based on the aforementioned prediction,

[0795] A function to dynamically adjust the aforementioned maintenance plan,

[0796] A device that provides category-related guidance in real time,

[0797] A function to send notifications to users based on the above instructions,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising a device for improving the accuracy of the machine learning algorithm using historical data.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising means for providing instructions to the user when the aforementioned abnormality is detected.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] A means of acquiring multiple observational data,

[0806] Means for managing and preprocessing the aforementioned observational data,

[0807] means for applying a generated AI model using the aforementioned preprocessed observational data,

[0808] The aforementioned AI model provides a means for predicting device deterioration or abnormalities,

[0809] Means for dynamically generating and adjusting maintenance plans based on the aforementioned predictions,

[0810] A means of analyzing the user's psychological state and adjusting the feedback provided to the user,

[0811] Means for sending a notification based on the results of the analysis and prediction,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising means for improving the prediction accuracy of the generated AI model by utilizing past historical data.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for automatically issuing a notification that takes into account the user's psychological state in response to the prediction or detection of an anomaly.

[0817] "Application example 2 when combining with an emotional engine"

[0818] (Claim 1)

[0819] Multiple means of collecting information,

[0820] means for applying an artificial intelligence algorithm for analyzing the aforementioned multiple pieces of information,

[0821] A means for predicting equipment deterioration or abnormalities based on the aforementioned analysis,

[0822] Means for generating a maintenance plan based on the aforementioned prediction,

[0823] Means for adjusting the aforementioned maintenance plan,

[0824] A means of recognizing the user's emotional state and dynamically adjusting notification content based on it,

[0825] A means of supporting the user's psychological stability through an emotional engine,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for improving the accuracy of the artificial intelligence algorithm using past historical information.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for automatically sending a notification according to the user's emotional state when the aforementioned abnormality is detected. [Explanation of Symbols]

[0831] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring multiple data, A means for applying a machine learning algorithm to analyze the aforementioned multiple data sets, A means for predicting deterioration or abnormalities of the device based on the aforementioned analysis, Means for generating a maintenance plan based on the aforementioned prediction, Means for dynamically adjusting the aforementioned maintenance plan, A means of providing category-related guidance in real time, A means of sending a notification to the user based on the above instructions, A system that includes this.

2. The system according to claim 1, further comprising means for improving the accuracy of the machine learning algorithm using historical data.

3. The system according to claim 1, further comprising means for providing instructions to the user when the aforementioned abnormality is detected.

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