System

Abstract

In the conventional charging method, charging is often performed at a constant output, which increases the charging time and may cause accelerated deterioration of the battery. Further, it is also a problem that an abnormal temperature rise or a rapid voltage fluctuation threatens the safety of the battery. In order to solve these problems, a system that monitors the state of the battery in real time and dynamically adjusts the optimal charging pattern is required.SOLUTION: A system comprising: means for monitoring voltage and temperature of a battery in real time; means for transmitting the monitoring data to a server; server means for calculating an optimal charging pattern and transmitting instructions to the terminal; and means for adjusting charging parameters based on the instructions from the server.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With the increasing use of modern portable electronic devices, shortening charging times and extending battery life have become important challenges. Conventional charging methods often involve charging at a constant output, which lengthens charging times and can accelerate battery degradation. Abnormal temperature increases and sudden voltage fluctuations also pose problems, threatening battery safety. To address these challenges, a system is needed that can monitor battery status in real time and dynamically adjust the optimal charging pattern. [Means for solving the problem]

[0005] The present invention provides a means for monitoring battery voltage and temperature in real time, and transmits this monitoring data to a server, which then calculates an optimal charging pattern and sends instructions to the device, which then adjusts charging parameters based on these instructions to ensure efficient and safe charging.

[0006] The system also includes a means for detecting abnormal conditions and reporting them to a server, which then adjusts charging parameters accordingly to ensure battery safety. It also stores charging session data and uses it to improve the algorithm for the next charging session, further improving charging efficiency and safety over time.

[0007] A "battery" is a rechargeable power supply for providing power to an electronic device.

[0008] "Voltage" is the difference in potential required to pass current, and is an indicator of the battery's state of charge.

[0009] "Temperature" is a physical quantity that indicates the thermal state of a battery, and is an important factor that affects the operation and safety of the battery.

[0010] "Real-time" means that processing and data collection occurs as fast as real time.

[0011] "Monitoring" means continuously measuring the battery's condition and collecting data.

[0012] "Send" is the act of transferring data from a terminal to a server.

[0013] A "server" is a central control device that analyzes data, calculates control instructions, and sends instructions to terminals.

[0014] "Charging pattern" refers to the charging current and voltage profile, which is dynamically adjusted according to the battery condition.

[0015] "Instruction" means an operation command sent from the server to the terminal.

[0016] "Terminal" refers to an electronic device that is equipped with a battery and is subject to charging control.

[0017] "Adjusting" refers to changing the charging profile based on data or instructions.

[0018] "Abnormal condition" refers to abnormal temperature increases or voltage fluctuations that threaten the safety of the battery.

[0019] "Notification" is the act of notifying the server of an abnormal condition.

[0020] "Charging session" means the entire charging process.

[0021] "Data" refers to information about the battery monitoring results and charging process.

[0022] An "algorithm" is a set of calculation procedures or rules for solving a problem. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[0045] Program Overview

[0046] Initialization

[0047] 1. The server manages the latest version of the charging control algorithm and sends updates to the device as needed.

[0048] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature).

[0049] Start charging

[0050] 1. When a user connects a device to a charger, the device begins a charging session.

[0051] 2. The terminal sets the charging current and voltage based on the latest charging control algorithm received from the server.

[0052] Real-time monitoring and adjustment

[0053] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[0054] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[0055] 3. The device adjusts the charging current and voltage according to instructions from the server.

[0056] Anomaly detection and response

[0057] 1. When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server.

[0058] 2. The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging.

[0059] 3. The device adjusts or pauses the charging current according to instructions from the server.

[0060] Charging completed

[0061] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0062] 2. The server stores the charging session data and uses it to improve the charging control algorithm for the next time.

[0063] Specific examples

[0064] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[0065] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[0066] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[0067] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[0068] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[0069] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[0070] In this way, the present invention provides efficient and safe charging while maintaining good battery operating condition.

[0071] The processing flow will be explained below.

[0072] I understand. Below I will explain the program's processing in concrete steps.

[0073] Step 1:

[0074] The server prepares the latest version of the charging control algorithm and sends updates to the device as needed.

[0075] Step 2:

[0076] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[0077] Step 3:

[0078] The user connects the terminal to a charger and the terminal begins a charging session.

[0079] Step 4:

[0080] The terminal applies the latest charging control algorithm received from the server to set the initial charging current and voltage.

[0081] Step 5:

[0082] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[0083] Step 6:

[0084] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[0085] Step 7:

[0086] The terminal adjusts the charging current and voltage according to instructions from the server.

[0087] Step 8:

[0088] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0089] Step 9:

[0090] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[0091] Step 10:

[0092] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[0093] Step 11:

[0094] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0095] Step 12:

[0096] The server stores data from the charging session to help improve the charging control algorithm for the next time.

[0097] The above are the specific processing steps of the program.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] To ensure both safety and efficiency during battery charging, it is important to monitor battery voltage and temperature in real time, detect abnormalities early, and respond accordingly. However, conventional systems have not fully established means to efficiently solve these issues, making it difficult to dynamically adjust the optimal charging pattern. The present invention aims to solve these issues and achieve safe and efficient battery charging.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for managing the latest charging control algorithm and transmitting updates to the terminal, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for detecting the user's connection of the terminal to a charger at the start of charging and starting a charging session, and means for periodically measuring the battery status and analyzing the data. This makes it possible to monitor the battery status in real time, detect abnormalities early, and dynamically adjust the optimal charging pattern.

[0103] A "battery" is a device that stores electrical energy as chemical energy and is used to provide electrical current.

[0104] "Voltage" refers to the potential difference between two points in an electrical circuit, and is an important indicator for evaluating the charge and discharge state of a battery.

[0105] "Temperature" is an indicator of the thermal state of an object, and is a factor that has a significant impact on the safety and performance of a battery.

[0106] "Real-time" refers to a state in which data processing and information collection occur immediately, without delay.

[0107] "Monitoring" is the act of continuously checking the condition and changes of an object and detecting abnormalities early.

[0108] "Terminal" refers to an electronic device that is equipped with a battery and has the role of controlling charging and discharging.

[0109] A "charge control algorithm" is a set of calculations and logic for optimizing the charging pattern of a battery.

[0110] "Server" means a computer system that processes and manages data on a network, and manages and updates the charging control algorithm.

[0111] "User" refers to the person or end user who operates the device and charges the battery.

[0112] "Data" is a collection of information including measurement results such as voltage and temperature, and serves as the basic information for processing by servers and terminals.

[0113] "Abnormal" refers to a condition in which the battery voltage or temperature deviates from the preset normal values, and immediate action is required.

[0114] "Charging current" refers to the current supplied to the battery, which affects the charging rate and the health of the battery.

[0115] "Charging parameters" refer to the set values ​​and conditions of the charging process, such as current, voltage, and time.

[0116] "Specifications" refer to the technical requirements and conditions of the system or method provided by the present invention.

[0117] MODE FOR CARRYING OUT THE INVENTION

[0118] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[0119] System Program Overview

[0120] The system starts with a server that manages the latest charging control algorithms and distributes them to devices as needed. The device uses these algorithms to measure battery voltage and temperature in real time and sends the data to the server. The server analyzes the data, calculates the optimal charging pattern, and sends instructions to the device. The user simply connects the device to the charger, and the charging process is automatically optimized.

[0121] Hardware and software used

[0122] 1. Server: Uses cloud services (e.g., AWS®, Google® Cloud) to perform computational processing and data management. Uses generative AI models (e.g., TENSORFLOW®, PyTorch) to perform data analysis and optimization.

[0123] 2. Terminal: Uses a microcontroller (e.g., Arduino, Raspberry Pi) and a sensor device (e.g., voltage sensor, temperature sensor). Sends data to the server using a communication module (e.g., ESP8266).

[0124] 3. User: This represents a smartphone or other device that needs to be charged. The user connects the device to the charger.

[0125] Specific examples

[0126] For example, consider the following scenario where a user plugs a smartphone into a charger:

[0127] Initialization

[0128] 1. The server manages the latest charging control algorithm and sends updates to the device as needed. The server uses a cloud service to keep the algorithm up to date.

[0129] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature). For example, the initial voltage is 3.7V and the temperature is 25°C.

[0130] Start charging

[0131] 1. When a user connects a smartphone to a charger, the device sets an initial charging current of 1A and begins the charging session.

[0132] Real-time monitoring and adjustment

[0133] 1. The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. The data is sent in real time using the communication module.

[0134] 2. The server analyzes the received data, calculates the optimal charging current (e.g., 1.5A), and notifies the device. Generative AI models are used to analyze and optimize the data.

[0135] 3. The device adjusts the charging current to 1.5A according to instructions from the server.

[0136] Anomaly detection and response

[0137] 1. When the device detects that the battery temperature has risen to 30°C, it immediately notifies the server.

[0138] 2. The server analyzes this abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[0139] 3. The device follows the instruction and reduces the charging current to 0.5A.

[0140] Charging completed

[0141] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0142] 2. The server saves the data from this charging session and stores it in a database for use in improving the algorithm next time.

[0143] Prompt Sentence Examples

[0144] Below are some example prompts to input to a generative AI model:

[0145] Explain the battery management system of a smartphone, taking into account the following points:

[0146] 1. Roles of servers, terminals, and users.

[0147] 2. How to monitor battery voltage and temperature.

[0148] 3. How to update and apply the charging control algorithm.

[0149] 4. How to respond when an abnormality is detected.

[0150] 5. How to store and use data from charging sessions.

[0151] Thus, the present invention provides a comprehensive system for efficiently and safely managing the battery charging process.

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] System program processing flow

[0154] Step 1: Initialization

[0155] The server manages and regularly updates the latest charging control algorithms, which are used to calculate safe charging profiles for batteries and are optimized using AI models.

[0156] Input: Current charge control algorithm and historical data about the battery.

[0157] Data processing / calculation: Analyzes data using an AI model and optimizes the charging control algorithm.

[0158] Output: Generation of a new charge control algorithm.

[0159] What it does: The server uses TensorFlow to perform the analysis and saves the new algorithm to the cloud.

[0160] Step 2: Algorithm Updates

[0161] The server transmits the latest charging control algorithm to the terminal.

[0162] Input: A new charge control algorithm.

[0163] Data processing / calculation: Communication processing to send the algorithm to the terminal.

[0164] Output: The new algorithm is applied to the device.

[0165] Specific operation: The server sends the algorithm to the terminal using the HTTP protocol.

[0166] Step 3: Measuring the initial battery condition

[0167] The device measures the initial battery condition (voltage and temperature).

[0168] Input: Sensor data from the battery.

[0169] Data processing / calculation: Collect sensor data and record the initial state.

[0170] Output: Initial voltage and temperature data.

[0171] Specific operation: The terminal acquires data using the voltage sensor and temperature sensor, and measures an initial voltage of 3.7V and an initial temperature of 25°C.

[0172] Step 4: Detecting the start of charging

[0173] When a user connects a terminal to a charger, the terminal begins a charging session.

[0174] Input: Power is supplied to the device's charging port.

[0175] Data processing / calculation: Detects the signal to start charging and sets the initial charging current.

[0176] Output: Start of charging session.

[0177] Specific operation: The device detects power supply from the charger and sets the initial charging current to 1A.

[0178] Step 5: Real-time monitoring

[0179] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and sends the data to the server.

[0180] Input: Sensor data (voltage and temperature).

[0181] Data processing / computation: Sensor data collection and transmission processing.

[0182] Output: Latest voltage and temperature data sent to the server.

[0183] Specific operation: The device uses the Wi-Fi module to send data to the server every 5 seconds.

[0184] Step 6: Data analysis and adjustment instructions

[0185] The server analyzes the received data and calculates the optimal charging current and voltage.

[0186] Input: Battery voltage and temperature data sent from the device.

[0187] Data processing / calculation: Optimal charging patterns are calculated using AI models.

[0188] Output: Adjustment instructions to the terminal.

[0189] Specific operation: The server uses the generative AI model to determine that a charging current of 1.5A is optimal and sends that instruction to the device.

[0190] Step 7: Adjust charging parameters

[0191] The terminal adjusts the charging current and voltage based on instructions from the server.

[0192] Input: Adjustment instructions from the server.

[0193] Data processing / calculation: Set charging parameters according to instructions.

[0194] Output: Regulated charging current and voltage.

[0195] Specific operation: The device adjusts the charging current to 1.5A.

[0196] Step 8: Anomaly detection and response

[0197] If an abnormality occurs in the battery temperature or voltage, the device will immediately notify the server.

[0198] Input: Abnormal sensor data (e.g. temperature above 30°C).

[0199] Data processing / calculation: Filters out abnormal data and notifies the server.

[0200] Output: Error notification data to the server.

[0201] Specific operation: The device detects abnormal data and immediately sends the data to the server using the Wi-Fi module.

[0202] Step 9: Instructions for handling abnormal situations

[0203] The server analyzes the anomaly data and determines the appropriate response (e.g., reducing the charging current).

[0204] Input: Abnormality notification data from the terminal.

[0205] Data processing / calculation: Analyzing abnormal data and determining countermeasures.

[0206] Output: Instructions to the terminal.

[0207] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[0208] Step 10: Charging complete and notification

[0209] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0210] Input: Battery state-of-charge data.

[0211] Data processing / calculation: Determining when charging is complete and notifying the user.

[0212] Output: Charging completion notification.

[0213] Specific operation: The device detects when the battery is 100% charged, stops charging, and notifies the user via an LED display or other means.

[0214] Step 11: Save Data

[0215] The server stores all data about the charging session and uses it to improve the charging control algorithm for future charging sessions.

[0216] Input: Charging session data.

[0217] Data processing / computation: Data storage and analysis.

[0218] Output: Stored charging data.

[0219] What it does: The server stores the details of the charging session in a database and analyzes them to improve the algorithm next time.

[0220] In this way, at each step, the server, terminal, and user play their respective roles to efficiently and safely manage the battery charging process.

[0221] (Application example 1)

[0222] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0223] Current battery management systems for autonomous mobile machines have problems with both charging efficiency and safety. Furthermore, they lack the means to quickly detect and respond to abnormal battery conditions, which increases operating costs and threatens machine safety. Furthermore, insufficient optimization of charging patterns shortens battery life and results in increased maintenance frequency.

[0224] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0225] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for storing charging session data and using it to improve the algorithm for the next charging session, and means for automating battery management for the autonomous mobile machine. This makes it possible to monitor the battery status in real time and dynamically adjust the optimal charging pattern. In addition, by quickly detecting abnormal conditions and adjusting the charging current, safety and efficiency are improved and operational costs are reduced.

[0226] A "battery" is a device that stores electrical energy as chemical energy and supplies it as electrical energy when needed.

[0227] "Voltage" is a physical quantity that indicates the electrical potential difference required to cause current to flow, and is expressed in volts (V).

[0228] "Temperature" is an indicator of the thermal energy state of a substance and is expressed in degrees Celsius (℃) or Fahrenheit (℉).

[0229] "Real-time monitoring means" refers to a device or system that instantly acquires data and instantly analyzes and displays the acquired data.

[0230] "Monitoring data" is data that collects monitored physical or environmental information, such as battery voltage or temperature.

[0231] A "server" is a computer system that provides various services and functions to client terminals via a network.

[0232] An "optimal charging pattern" is a charging process in which current and voltage are adjusted to achieve efficient charging of the battery.

[0233] A "terminal" is a computer device or machine that receives instructions from a server and performs a specific function based on those instructions.

[0234] "Charging parameters" refer to various control values ​​(e.g., current, voltage) that are set in the charging process.

[0235] A "charging session" is the entire process from when a battery is connected to a charger until it is fully charged.

[0236] An "algorithm" is a set of logical steps that represents a procedure or computational method for solving a particular problem.

[0237] An "autonomous mobile machine" is a machine that has the ability to make its own decisions and take action without requiring external instructions or control.

[0238] "Abnormal condition" refers to a situation where a phenomenon beyond the normal operating range (e.g., overheating, overvoltage) occurs.

[0239] "Operating costs" are all costs incurred to operate a system or machine (e.g., energy costs, labor costs).

[0240] The present invention relates to a system for efficiently and safely managing batteries for autonomous mobile machines in factories. This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. The configuration and operation of this system are described in detail below.

[0241] server

[0242] The server manages the latest version of the charging control algorithm and sends updates to the device as needed. It receives and analyzes battery voltage and temperature data sent in real time, calculates the optimal charging pattern, and sends instructions to the device. It also analyzes abnormal conditions and sends instructions to the device to take appropriate action. It also saves charging session data and uses it to improve the algorithm for the next time.

[0243] Terminal

[0244] The terminal receives instructions from the server and dynamically adjusts charging parameters (current and voltage) based on those instructions. It also measures the battery's voltage and temperature at regular intervals and sends the data to the server. If it detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server and adjusts or suspends the charging current according to the server's instructions.

[0245] User

[0246] Users initiate a charging session by connecting their device to a charger and are notified when the battery is fully charged so they can take appropriate action.

[0247] Specific examples

[0248] For example, consider an autonomous mobile machine used in a factory. When this machine arrives at a charging station, the system operates as follows:

[0249] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[0250] 2. Start charging: When the user connects the device to the charging station, charging begins. The terminal sets the initial charging current (e.g., 1A).

[0251] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[0252] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device complies with this instruction.

[0253] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[0254] Prompt Sentence Examples

[0255] "Design an app that intelligently manages the batteries of an autonomous mobile robot. Specifically, it monitors the battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. If an abnormal temperature rise or voltage fluctuation is detected, it immediately adjusts the charging. Implement this in Python."

[0256] This invention is expected to improve the efficiency of battery management for autonomous mobile machines in factories, improve safety, and reduce operating costs. Specifically, it enables real-time battery monitoring, optimization of charging patterns, and rapid response to abnormal conditions, significantly improving overall operational efficiency.

[0257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0258] Step 1:

[0259] The server maintains the latest version of the charging control algorithm and sends updates to the device as needed. Specifically, when the server receives an update request, it transfers the latest version of the algorithm to the device. At this time, the server properly packages the files required for the update and sends them in a format that the device can receive and extract.

[0260] Input: Update request from device

[0261] Output: Latest algorithm file

[0262] Step 2:

[0263] The device applies the latest algorithm file received from the server, measures the initial battery status (voltage and temperature), and sends it to the server. Specifically, the device measures the voltage and temperature using a sensor inside the device, formats the data, and sends it to the server. This measurement and data transmission is based on a certain protocol.

[0264] Input: Algorithm file from server

[0265] Output: Voltage and temperature data

[0266] Step 3:

[0267] When a user connects a device to a charger, the device starts a charging session. Specifically, when the device detects the charger, it generates a charging start signal and sets the initial charging current (e.g., 1A). At this point, the device sends its initial state data to the server.

[0268] Input: Connect to charger

[0269] Output: Charging start signal, initial charging current setting

[0270] Step 4:

[0271] The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. Specifically, it acquires data from sensors within the device and sends it to the server in a fixed format. This data is updated in real time and at a fixed interval.

[0272] Input: Voltage and temperature data from the battery

[0273] Output: Measurement data sent to the server

[0274] Step 5:

[0275] The server calculates the optimal charging current based on the received data and sends the instructions to the device. Specifically, it uses an AI model to predict the optimal charging current for the current voltage and temperature, and notifies the device of the results. This calculation is performed in real time and is updated as data is received.

[0276] Input: Voltage and temperature data from the terminal

[0277] Output: Optimal charging current indication

[0278] Step 6:

[0279] The device adjusts the charging current according to instructions from the server. Specifically, the charging control system in the device changes the setting value and adjusts the charging current accordingly. This operation is designed to be performed quickly.

[0280] Input: Charging current instruction from the server

[0281] Output: Regulated charging current

[0282] Step 7:

[0283] When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server, which then analyzes the abnormal condition and instructs the appropriate response. Specifically, the abnormality data is sent to the server, which then runs an algorithm to identify the cause of the abnormality and sends an appropriate response (such as reducing the charging current) to the device.

[0284] Input: Abnormal voltage and temperature data

[0285] Output: Abnormal notification to server, instruction of analysis results

[0286] Step 8:

[0287] The terminal adjusts the charging current and suspends charging if necessary based on instructions from the server regarding abnormal conditions. Specifically, the charging control system receives instructions from the server and promptly adjusts or suspends charging current according to the instructions.

[0288] Input: Error handling instructions from the server

[0289] Output: Regulated charging current, suspended charging

[0290] Step 9:

[0291] When the battery is determined to be fully charged, the device stops charging and notifies the user. Specifically, when the device meets certain voltage and temperature conditions, it generates a charging completion signal and notifies the user of this information by displaying or audibly notifying the user.

[0292] Input: Fully charged battery data

[0293] Output: Charging stop signal, notification to user

[0294] Step 10:

[0295] The server stores data from all charging sessions and uses it to improve the algorithm for the next time. Specifically, the received data is stored in a database and the analytical algorithm uses this data to optimize future charging templates.

[0296] Input: Charging session data

[0297] Output: Data stored in database, improved algorithm

[0298] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0299] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system is mainly composed of three elements: a server, a terminal, and a user. The specific operation of the system and program is described below.

[0300] Program Overview

[0301] Initialization

[0302] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the device as needed.

[0303] 2. The device receives and applies updates, and measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[0304] Start charging

[0305] 1. When a user connects a device to a charger, the device begins a charging session.

[0306] 2. The device sets the charging current and voltage based on the latest charging control algorithm and emotion recognition algorithm received from the server.

[0307] Real-time monitoring and adjustment

[0308] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[0309] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[0310] 3. The terminal adjusts the charging parameters based on instructions from the server.

[0311] Emotion recognition and response

[0312] 1. The device activates an emotion engine that recognizes the user's emotions in real time and sends the data to the server.

[0313] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[0314] 3. Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[0315] Anomaly detection and response

[0316] 1. If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0317] 2. The server analyzes the abnormal condition and sends instructions to the terminal to pause or adjust charging as necessary.

[0318] 3. The device adjusts the charging current or temporarily suspends charging according to instructions from the server.

[0319] Charging completed

[0320] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0321] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[0322] Specific examples

[0323] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[0324] 1. Initialization: The server sends the latest charging control and emotion recognition algorithms to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C) and the user's emotional state (e.g., relaxed state).

[0325] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[0326] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature at regular intervals and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[0327] 4. Emotion Recognition and Response: If the device detects the user's stress state, it sends that information to the server. Based on this emotional state, the server customizes a charging progress notification message and sends it to the user (e.g., "Please relax. Charging is going well").

[0328] 5. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[0329] 6. Charging complete: When the battery is fully charged, the device stops charging and notifies the user. The server stores the data from this charging session and the emotion data for future algorithm improvements.

[0330] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[0331] The processing flow will be explained below.

[0332] Step 1:

[0333] The server prepares the latest versions of the charging control algorithm and emotion recognition algorithm and sends updates to the device as needed.

[0334] Step 2:

[0335] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[0336] Step 3:

[0337] The user connects the terminal to a charger and the terminal begins a charging session.

[0338] Step 4:

[0339] The terminal applies the latest charging control algorithm and emotion recognition algorithm received from the server to set the initial charging current and voltage.

[0340] Step 5:

[0341] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[0342] Step 6:

[0343] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[0344] Step 7:

[0345] The terminal adjusts the charging current and voltage according to instructions from the server.

[0346] Step 8:

[0347] The device activates an emotion engine that recognizes the user's emotions in real time and transmits the data to a server.

[0348] Step 9:

[0349] The server analyzes the received emotion data and sends instructions to the terminal to adjust charging parameters based on the recognized user emotion.

[0350] Step 10:

[0351] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[0352] Step 11:

[0353] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0354] Step 12:

[0355] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[0356] Step 13:

[0357] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[0358] Step 14:

[0359] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0360] Step 15:

[0361] The server stores charging session data and emotion data to help improve the algorithm next time.

[0362] The above are the specific processing steps of the program.

[0363] Example 2

[0364] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] Although conventional charging systems monitor battery status and provide optimal charging patterns, they are unable to take the user's emotional state into account, which results in a lack of user experience. Furthermore, the lack of automated anomaly detection and appropriate response leaves concerns about battery life and safety. These issues need to be addressed.

[0366] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0367] In this invention, the server includes means for monitoring the voltage and temperature of the battery in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting an instruction to the terminal, means for adjusting charging parameters based on the instruction from the server, means for recognizing a user's emotion, means for transmitting the recognized emotion data to the server, and means for adjusting charging parameters based on the user's emotion, thereby enabling the battery to be charged safely and efficiently while taking the user's emotional state into consideration.

[0368] "Means for monitoring battery voltage and temperature in real time" refers to sensors or modules that continuously measure the battery voltage and temperature, allowing the battery condition to be constantly monitored.

[0369] The "means for transmitting the monitoring data to the server" refers to a communication module or software that sends the voltage and temperature data obtained from the battery to the server via a network.

[0370] "Server means for calculating an optimum charging pattern and transmitting instructions to the terminal" refers to a server that analyzes the received data, calculates an optimum charging method, and transmits the instructions to the terminal.

[0371] "Means for adjusting charging parameters based on instructions from the server" refers to a component or software on the terminal side that automatically changes parameters such as charging current and voltage according to instructions provided by the server.

[0372] "Means for recognizing user emotions" refers to cameras and microphones that analyze the user's facial expressions, voice, and movements to identify emotions, as well as software that analyzes them.

[0373] The "means for transmitting the recognized emotion data to the server" refers to a communication module or software for sending the user's emotion data recognized on the terminal to the server via a network.

[0374] The "means for adjusting charging parameters based on the user's emotions" refers to a component or software on the terminal side that appropriately changes charging parameters depending on the user's emotional state.

[0375] MODE FOR CARRYING OUT THE INVENTION

[0376] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal, and a user. The specific operation of this system is explained below.

[0377] Initialization

[0378] 1. The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed. This is done using a management system on the server and network communication methods.

[0379] 2. After receiving and applying the update, the device measures and records the initial battery status (voltage and temperature) and the user's emotional state. The device is equipped with sensors and emotion recognition software. The sensors function as a voltmeter and thermometer, and the emotion recognition software detects the user's emotions via the camera and microphone.

[0380] Start charging

[0381] When a user connects a device to a charger, the device starts a charging session. The device sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server. This initial setting is performed using the device's internal charging control software.

[0382] Real-time monitoring and adjustment

[0383] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server. This process utilizes the device's internal sensors and communication module.

[0384] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device. The server contains an analytical algorithm and database that are used to determine the appropriate charging conditions.

[0385] 3. The device adjusts the charging parameters based on instructions from the server. This operation is performed by the charging control software within the device.

[0386] Emotion recognition and response

[0387] 1. The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to a server. Emotion recognition uses facial recognition and voice analysis technologies.

[0388] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion. The server is equipped with an emotion analysis algorithm.

[0389] 3. The device customizes the charging progress notification message based on instructions from the server and provides it to the user using the device's internal notification system.

[0390] Anomaly detection and response

[0391] 1. If the device detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. This process uses sensors and communication modules.

[0392] 2. The server analyzes the abnormal condition and sends instructions to the device to pause or adjust charging as necessary. The server is equipped with an abnormality analysis algorithm.

[0393] 3. The device adjusts the charging current and pauses charging according to instructions from the server, which is also done by the charging control software.

[0394] Charging completed

[0395] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user. The notification is done through the device's internal notification system.

[0396] 2. The server stores charging session data and emotion data to help improve the algorithm for the next time. A database system is used, which allows for long-term optimization.

[0397] Examples and prompts

[0398] For example, when a user connects a smartphone to a charger, the system operates as follows: When charging begins, the device receives the latest algorithm from the server and performs initial setup. It then monitors voltage and temperature in real time and performs emotion recognition. At the same time, it also detects anomalies to ensure safe charging. A specific example of a prompt is as follows:

[0399] "Please explain how your system works as a user charges their smartphone. Please provide detailed instructions, focusing especially on charging optimization and emotion recognition."

[0400] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[0401] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0402] Step 1:

[0403] The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed.

[0404] Input: The update management system on the server detects a new algorithm version.

[0405] Data processing: Packages the updates and prepares them for transmission.

[0406] Output: The update packet is sent to the device.

[0407] Specific operation: The server periodically scans the database and, if a new algorithm version is found, sends it to the device.

[0408] Step 2:

[0409] The terminal receives the updated algorithm and applies it to the system.

[0410] Input: The update packet sent by the server.

[0411] Data processing: Unpacking the update packet, analyzing the algorithm and applying it to the system.

[0412] Output: The system is updated with the new algorithm.

[0413] Specific operation: When the device receives the update packet, it unpacks it, installs the algorithm, and restarts the system.

[0414] Step 3:

[0415] The device measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[0416] Input: Raw data from battery sensors, camera and microphone.

[0417] Data processing: Analyze raw data, quantify voltage and temperature, and classify emotional states.

[0418] Output: Battery voltage, temperature data and user emotion data.

[0419] How it works: The device measures the battery voltage to be 3.7V and the temperature to be 25°C, and the emotion recognition software classifies the user's emotional state as "relaxed."

[0420] Step 4:

[0421] When a user connects a terminal to a charger, the terminal begins a charging session.

[0422] Input: The user plugs the device into a charger.

[0423] Data processing: Recognize as a trigger to start charging.

[0424] Output: Start of charging session.

[0425] Specific behavior: When a user plugs a device into a charger, the system automatically initiates the charging protocol.

[0426] Step 5:

[0427] The terminal sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server.

[0428] Input: Charging control algorithm from the server.

[0429] Data processing: Calculation of initial charging settings based on algorithms.

[0430] Output: Initial charging current and voltage settings.

[0431] Specific operation: The terminal sets the initial charging current to 1A and the voltage based on the received algorithm.

[0432] Step 6:

[0433] The terminal measures the battery voltage and temperature at regular intervals and transmits the data to the server.

[0434] Input: Voltage and temperature data from sensors.

[0435] Data processing: Collecting, organizing, and converting data into packets for transmission.

[0436] Output: Organized monitoring data.

[0437] Specific operation: The device measures the voltage and temperature every 5 seconds and sends the data to the server.

[0438] Step 7:

[0439] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the terminal.

[0440] Input: Monitoring data sent from the device.

[0441] Data processing: Analyzing received data and optimizing charging patterns.

[0442] Output: Instruction data to the terminal.

[0443] Specific operation: The server compares past data with real-time data and sends an instruction to the device to increase the charging current to 1.5A.

[0444] Step 8:

[0445] The terminal adjusts the charging parameters based on instructions from the server.

[0446] Input: Instruction data from the server.

[0447] Data processing: Adjusting charging parameters based on instruction data.

[0448] Output: New charging parameter settings.

[0449] Specific operation: The device adjusts the charging current to 1.5A.

[0450] Step 9:

[0451] The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to the server.

[0452] Input: Real-time data from camera and microphone.

[0453] Data processing: Data analysis using sentiment analysis algorithms.

[0454] Output: Emotional state data.

[0455] Specific operation: The device analyzes the user's facial expressions and voice, and if it determines that the user is in a stressful state, it sends that data to the server.

[0456] Step 10:

[0457] The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[0458] Input: User emotion data.

[0459] Data processing: Optimizing charging parameters based on emotion data.

[0460] Output: Instructions to adjust charging parameters to the device.

[0461] Specific operation: The server recognizes that the user is in a stressed state and sends instructions to customize the charging progress notification message.

[0462] Step 11:

[0463] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[0464] Input: Customization instructions from the server.

[0465] Data processing: Customizing notification messages based on your instructions.

[0466] Output: A customized notification message.

[0467] What it does: The device displays a message like "Relax, charging is going well."

[0468] Step 12:

[0469] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0470] Input: Anomaly data from sensors.

[0471] Data processing: Analyzes abnormal data and converts it into packets to be notified to the server.

[0472] Output: Anomaly notification packet.

[0473] Specific operation: The device detects a sudden rise in temperature (e.g., 30°C) and sends that information to the server.

[0474] Step 13:

[0475] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[0476] Input: Abnormality notification data from the terminal.

[0477] Data processing: Analyze abnormal data and calculate appropriate countermeasures.

[0478] Output: Countermeasure instruction data to the terminal.

[0479] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[0480] Step 14:

[0481] The terminal adjusts the charging current and temporarily suspends charging according to instructions from the server.

[0482] Input: Countermeasure instruction data from the server.

[0483] Data processing: Adjust charging parameters based on instructions.

[0484] Output: New charging parameter settings.

[0485] Specific action: The device reduces the charging current to 0.5A.

[0486] Step 15:

[0487] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0488] Input: Battery monitoring data.

[0489] Data processing: Battery level analysis and charging stop decision.

[0490] Output: Charging completion notification.

[0491] What it does: The device determines that the battery has reached 100% and notifies you that your battery is fully charged.

[0492] Step 16:

[0493] The server stores charging session data and emotion data to help improve the algorithm next time.

[0494] Input: Charging session data and emotion data from the device.

[0495] Data processing: Received data is stored in a database.

[0496] Output: Saved charging session data.

[0497] Specific operation: The server stores all charging data and emotion data in a database and analyzes it the next time the algorithm is updated.

[0498] (Application example 2)

[0499] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0500] To improve the efficiency and safety of battery charging systems, it is important to manage battery voltage and temperature, and a system that monitors these in real time and dynamically adjusts the optimal charging pattern is required. In addition, because the user's emotional state can affect the battery charging process, a system that combines emotion recognition functionality is required to optimally manage the charging status and provide appropriate information to the user.

[0501] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for recognizing the user's emotion, means for transmitting the recognized emotion data to the server, and means for customizing a charging progress notification message based on the emotion data. This makes it possible to efficiently and safely charge the battery while maintaining good operating conditions and taking the user's emotional state into consideration.

[0502] 1. "Battery" means a device that stores electricity and whose operating conditions, such as voltage and temperature, can be monitored.

[0503] 2. "Voltage" refers to the electrical pressure generated within the battery, which directly affects its state of charge and performance.

[0504] 3. "Temperature" refers to the state of thermal energy in the battery and its surrounding environment, and plays an important role in charging efficiency and safety.

[0505] 4. "Real-time" refers to the ability to process and reflect information and data immediately, without delay.

[0506] 5. "Monitoring" refers to the act of continuously observing and recording certain physical or electronic parameters.

[0507] 6. "Server" means a computer system connected to a network that is a centralized device for data processing and management.

[0508] 7. "Terminal" refers to a computer device or sensor device that is directly operated by a user and that exchanges information in conjunction with a server.

[0509] 8. "Charging pattern" refers to the method of controlling current and voltage during the battery charging process.

[0510] 9. “Emotion recognition” is a technology for understanding and assessing a user’s emotional state, using sensors to analyze facial expressions and voice data.

[0511] 10. "Charging progress notification message" is a message that informs the user of information such as the battery charging status and estimated completion time.

[0512] 11. "Abnormal Condition" means a condition, such as battery voltage or temperature, that is outside of the normal operating range and may affect safety or performance.

[0513] 12. "Data retention" refers to the long-term storage of collected information for later analysis or reference.

[0514] This invention is aimed at factory robots, and combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal (robot), and a user (worker).

[0515] Program Overview:

[0516] Initialization:

[0517] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed.

[0518] 2. The robot receives and applies updates, measures and records the initial battery condition (voltage and temperature) and the worker's emotional state.

[0519] Start charging:

[0520] 1. When a worker connects a robot to a charging station, the robot begins a charging session.

[0521] 2. The robot sets the charging current and voltage based on the latest charging control algorithm received from the server.

[0522] Real-time monitoring and adjustment:

[0523] 1. The robot measures the battery voltage and temperature at regular intervals and sends the data to the server.

[0524] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot.

[0525] 3. The robot adjusts the charging parameters based on instructions from the server.

[0526] Emotion recognition and response:

[0527] 1. The robot activates an emotion engine to recognize the worker's emotions in real time and sends the data to the server.

[0528] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized emotion of the worker.

[0529] 3. Based on instructions from the server, the robot customizes a charging progress notification message and provides it to the worker.

[0530] Anomaly detection and response:

[0531] 1. If the robot detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0532] 2. The server analyzes the abnormal condition and sends instructions to the robot to pause or adjust charging as necessary.

[0533] 3. The robot adjusts the charging current or pauses charging according to instructions from the server.

[0534] Charging complete:

[0535] 1. When the robot determines that the battery is fully charged, it stops charging and notifies the worker.

[0536] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[0537] Hardware and software used:

[0538] 1. Hardware:

[0539] Robot body: An industrial robot used in factories.

[0540] Emotion recognition sensors: camera and microphone.

[0541] Server: A centralized device that manages and analyzes data.

[0542] 2. Software:

[0543] Emotion recognition algorithm: Use emotion recognition APIs such as Azure (registered trademark) Cognitive Services.

[0544] Charging control algorithm: Uses machine learning frameworks such as TensorFlow and PyTorch to analyze real-time data.

[0545] Examples:

[0546] In a factory, a robot uses an emotion recognition sensor to detect when a worker is stressed. After obtaining this information, the robot sends the data to a server, which analyzes it. Based on the analysis results, the robot sends an instruction to adjust the charging parameters so that "the worker is tired, so reduce the charging speed to ensure safety."

[0547] Example prompt sentence:

[0548] "Detecting stress levels from workers' facial expressions"

[0549] "Calculates optimal charging parameters when battery temperature rises"

[0550] "Analyzes emotional and charging data in real time to propose optimal charging plans"

[0551] These detailed program and prompt example statements demonstrate that the invention is possible.

[0552] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0553] Step 1:

[0554] The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed. The input is the latest algorithm data, and the output is instructions for updating the algorithm to the robot. Specifically, the server sends the latest algorithm, and the robot receives it and applies it.

[0555] Step 2:

[0556] The robot receives and applies updates, and measures and records the initial battery state (voltage and temperature) and the worker's emotional state. The input is the measurement data of the initial state, and the output is data transmission to the server. Specifically, the robot uses sensors to measure voltage and temperature and transmits that data to the server.

[0557] Step 3:

[0558] When a worker connects a robot to a charging station, the robot begins a charging session. The input is the robot's connection to the charging station, and the output is the start of charging. Specifically, the robot checks the connection and begins charging.

[0559] Step 4:

[0560] The robot sets the charging current and voltage based on the latest charging control algorithm received from the server. The input is the algorithm instruction from the server, and the output is the set charging parameters. Specifically, the robot receives instructions from the server and performs the operation of setting the charging parameters.

[0561] Step 5:

[0562] The robot measures the battery voltage and temperature at regular intervals and sends the data to the server. The input is the battery voltage and temperature data, and the output is the data sent to the server. Specifically, the robot measures data with sensors and sends it to the server.

[0563] Step 6:

[0564] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot. The input is voltage and temperature data from the robot, and the output is the calculation result of the optimal charging pattern. Specifically, the server analyzes the data, calculates the charging pattern, and sends instructions to the robot.

[0565] Step 7:

[0566] The robot operates an emotion engine to recognize the emotions of the worker in real time and sends the data to a server. The input is the worker's emotional state data, and the output is the transmission of emotion data to the server. Specifically, the robot uses an emotion recognition sensor to acquire data and send it to the server.

[0567] Step 8:

[0568] The server analyzes the received emotion data and adjusts the charging parameters based on the recognized emotion of the worker. The input is the emotion data from the robot, and the output is the result of adjusting the optimal charging parameters. Specifically, the server analyzes the emotion data, performs calculations to adjust the charging parameters, and sends instructions to the robot.

[0569] Step 9:

[0570] The robot customizes the charging progress notification message based on instructions from the server and provides it to the worker. The input is the notification instruction from the server, and the output is the customized notification message. Specifically, the robot receives the instruction from the server and performs an operation to display an appropriate message to the worker.

[0571] Step 10:

[0572] If the robot detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. The input is abnormal status data, and the output is a notification to the server. Specifically, the robot detects an abnormality and sends that information to the server.

[0573] Step 11:

[0574] The server analyzes the abnormal condition and sends instructions to the robot to suspend or adjust charging as necessary. The input is abnormal condition data from the robot, and the output is adjustment instructions. Specifically, the server analyzes the abnormal data, calculates safe charging parameters, and sends instructions to the robot.

[0575] Step 12:

[0576] The robot adjusts the charging current or temporarily suspends charging according to instructions from the server. The input is the adjustment instruction from the server, and the output is the executed charging parameter adjustment. Specifically, the robot adjusts the charging based on the instructions from the server.

[0577] Step 13:

[0578] When the robot determines that the battery is fully charged, it stops charging and notifies the worker. The input is battery charge status data, and the output is a notification that charging is complete. Specifically, the robot monitors the battery status, confirms that charging is complete, and notifies the worker.

[0579] Step 14:

[0580] The server stores the charging session data and emotion data to help improve the algorithm next time. The input is the charging session data and emotion data, and the output is the stored data. Specifically, the server stores the data and prepares it for use in improving the algorithm next time.

[0581] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0582] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0583] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0584] [Second embodiment]

[0585] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0586] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0587] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0588] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0589] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0590] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0591] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0592] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0593] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0594] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0595] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0596] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0597] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[0598] Program Overview

[0599] Initialization

[0600] 1. The server manages the latest version of the charging control algorithm and sends updates to the device as needed.

[0601] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature).

[0602] Start charging

[0603] 1. When a user connects a device to a charger, the device begins a charging session.

[0604] 2. The terminal sets the charging current and voltage based on the latest charging control algorithm received from the server.

[0605] Real-time monitoring and adjustment

[0606] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[0607] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[0608] 3. The device adjusts the charging current and voltage according to instructions from the server.

[0609] Anomaly detection and response

[0610] 1. When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server.

[0611] 2. The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging.

[0612] 3. The device adjusts or pauses the charging current according to instructions from the server.

[0613] Charging completed

[0614] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0615] 2. The server stores the charging session data and uses it to improve the charging control algorithm for the next time.

[0616] Specific examples

[0617] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[0618] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[0619] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[0620] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[0621] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[0622] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[0623] In this way, the present invention provides efficient and safe charging while maintaining good battery operating condition.

[0624] The processing flow will be explained below.

[0625] I understand. Below I will explain the program's processing in concrete steps.

[0626] Step 1:

[0627] The server prepares the latest version of the charging control algorithm and sends updates to the device as needed.

[0628] Step 2:

[0629] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[0630] Step 3:

[0631] The user connects the terminal to a charger and the terminal begins a charging session.

[0632] Step 4:

[0633] The terminal applies the latest charging control algorithm received from the server to set the initial charging current and voltage.

[0634] Step 5:

[0635] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[0636] Step 6:

[0637] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[0638] Step 7:

[0639] The terminal adjusts the charging current and voltage according to instructions from the server.

[0640] Step 8:

[0641] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0642] Step 9:

[0643] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[0644] Step 10:

[0645] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[0646] Step 11:

[0647] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0648] Step 12:

[0649] The server stores data from the charging session to help improve the charging control algorithm for the next time.

[0650] The above are the specific processing steps of the program.

[0651] Example 1

[0652] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0653] To ensure both safety and efficiency during battery charging, it is important to monitor battery voltage and temperature in real time, detect abnormalities early, and respond accordingly. However, conventional systems have not fully established means to efficiently solve these issues, making it difficult to dynamically adjust the optimal charging pattern. The present invention aims to solve these issues and achieve safe and efficient battery charging.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0655] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for managing the latest charging control algorithm and transmitting updates to the terminal, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for detecting the user's connection of the terminal to a charger at the start of charging and starting a charging session, and means for periodically measuring the battery status and analyzing the data. This makes it possible to monitor the battery status in real time, detect abnormalities early, and dynamically adjust the optimal charging pattern.

[0656] A "battery" is a device that stores electrical energy as chemical energy and is used to provide electrical current.

[0657] "Voltage" refers to the potential difference between two points in an electrical circuit, and is an important indicator for evaluating the charge and discharge state of a battery.

[0658] "Temperature" is an indicator of the thermal state of an object, and is a factor that has a significant impact on the safety and performance of a battery.

[0659] "Real-time" refers to a state in which data processing and information collection occur immediately, without delay.

[0660] "Monitoring" is the act of continuously checking the condition and changes of an object and detecting abnormalities early.

[0661] "Terminal" refers to an electronic device that is equipped with a battery and has the role of controlling charging and discharging.

[0662] A "charge control algorithm" is a set of calculations and logic for optimizing the charging pattern of a battery.

[0663] "Server" means a computer system that processes and manages data on a network, and manages and updates the charging control algorithm.

[0664] "User" refers to the person or end user who operates the device and charges the battery.

[0665] "Data" is a collection of information including measurement results such as voltage and temperature, and serves as the basic information for processing by servers and terminals.

[0666] "Abnormal" refers to a condition in which the battery voltage or temperature deviates from the preset normal values, and immediate action is required.

[0667] "Charging current" refers to the current supplied to the battery, which affects the charging rate and the health of the battery.

[0668] "Charging parameters" refer to the set values ​​and conditions of the charging process, such as current, voltage, and time.

[0669] "Specifications" refer to the technical requirements and conditions of the system or method provided by the present invention.

[0670] MODE FOR CARRYING OUT THE INVENTION

[0671] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[0672] System Program Overview

[0673] The system starts with a server that manages the latest charging control algorithms and distributes them to devices as needed. The device uses these algorithms to measure battery voltage and temperature in real time and sends the data to the server. The server analyzes the data, calculates the optimal charging pattern, and sends instructions to the device. The user simply connects the device to the charger, and the charging process is automatically optimized.

[0674] Hardware and software used

[0675] 1. Server: Uses cloud services (e.g., AWS, Google Cloud) for computation and data management, and generative AI models (e.g., TensorFlow, PyTorch) for data analysis and optimization.

[0676] 2. Terminal: Uses a microcontroller (e.g., Arduino, Raspberry Pi) and a sensor device (e.g., voltage sensor, temperature sensor). Sends data to the server using a communication module (e.g., ESP8266).

[0677] 3. User: This represents a smartphone or other device that needs to be charged. The user connects the device to the charger.

[0678] Specific examples

[0679] For example, consider the following scenario where a user plugs a smartphone into a charger:

[0680] Initialization

[0681] 1. The server manages the latest charging control algorithm and sends updates to the device as needed. The server uses a cloud service to keep the algorithm up to date.

[0682] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature). For example, the initial voltage is 3.7V and the temperature is 25°C.

[0683] Start charging

[0684] 1. When a user connects a smartphone to a charger, the device sets an initial charging current of 1A and begins the charging session.

[0685] Real-time monitoring and adjustment

[0686] 1. The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. The data is sent in real time using the communication module.

[0687] 2. The server analyzes the received data, calculates the optimal charging current (e.g., 1.5A), and notifies the device. Generative AI models are used to analyze and optimize the data.

[0688] 3. The device adjusts the charging current to 1.5A according to instructions from the server.

[0689] Anomaly detection and response

[0690] 1. When the device detects that the battery temperature has risen to 30°C, it immediately notifies the server.

[0691] 2. The server analyzes this abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[0692] 3. The device follows the instruction and reduces the charging current to 0.5A.

[0693] Charging completed

[0694] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0695] 2. The server saves the data from this charging session and stores it in a database for use in improving the algorithm next time.

[0696] Prompt Sentence Examples

[0697] Below are some example prompts to input to a generative AI model:

[0698] Explain the battery management system of a smartphone, taking into account the following points:

[0699] 1. Roles of servers, terminals, and users.

[0700] 2. How to monitor battery voltage and temperature.

[0701] 3. How to update and apply the charging control algorithm.

[0702] 4. How to respond when an abnormality is detected.

[0703] 5. How to store and use data from charging sessions.

[0704] Thus, the present invention provides a comprehensive system for efficiently and safely managing the battery charging process.

[0705] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0706] System program processing flow

[0707] Step 1: Initialization

[0708] The server manages and regularly updates the latest charging control algorithms, which are used to calculate safe charging profiles for batteries and are optimized using AI models.

[0709] Input: Current charge control algorithm and historical data about the battery.

[0710] Data processing / calculation: Analyzes data using an AI model and optimizes the charging control algorithm.

[0711] Output: Generation of a new charge control algorithm.

[0712] What it does: The server uses TensorFlow to perform the analysis and saves the new algorithm to the cloud.

[0713] Step 2: Algorithm Updates

[0714] The server transmits the latest charging control algorithm to the terminal.

[0715] Input: A new charge control algorithm.

[0716] Data processing / calculation: Communication processing to send the algorithm to the terminal.

[0717] Output: The new algorithm is applied to the device.

[0718] Specific operation: The server sends the algorithm to the terminal using the HTTP protocol.

[0719] Step 3: Measuring the initial battery condition

[0720] The device measures the initial battery condition (voltage and temperature).

[0721] Input: Sensor data from the battery.

[0722] Data processing / calculation: Collect sensor data and record the initial state.

[0723] Output: Initial voltage and temperature data.

[0724] Specific operation: The terminal acquires data using the voltage sensor and temperature sensor, and measures an initial voltage of 3.7V and an initial temperature of 25°C.

[0725] Step 4: Detecting the start of charging

[0726] When a user connects a terminal to a charger, the terminal begins a charging session.

[0727] Input: Power is supplied to the device's charging port.

[0728] Data processing / calculation: Detects the signal to start charging and sets the initial charging current.

[0729] Output: Start of charging session.

[0730] Specific operation: The device detects power supply from the charger and sets the initial charging current to 1A.

[0731] Step 5: Real-time monitoring

[0732] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and sends the data to the server.

[0733] Input: Sensor data (voltage and temperature).

[0734] Data processing / computation: Sensor data collection and transmission processing.

[0735] Output: Latest voltage and temperature data sent to the server.

[0736] Specific operation: The device uses the Wi-Fi module to send data to the server every 5 seconds.

[0737] Step 6: Data analysis and adjustment instructions

[0738] The server analyzes the received data and calculates the optimal charging current and voltage.

[0739] Input: Battery voltage and temperature data sent from the device.

[0740] Data processing / calculation: Optimal charging patterns are calculated using AI models.

[0741] Output: Adjustment instructions to the terminal.

[0742] Specific operation: The server uses the generative AI model to determine that a charging current of 1.5A is optimal and sends that instruction to the device.

[0743] Step 7: Adjust charging parameters

[0744] The terminal adjusts the charging current and voltage based on instructions from the server.

[0745] Input: Adjustment instructions from the server.

[0746] Data processing / calculation: Set charging parameters according to instructions.

[0747] Output: Regulated charging current and voltage.

[0748] Specific operation: The device adjusts the charging current to 1.5A.

[0749] Step 8: Anomaly detection and response

[0750] If an abnormality occurs in the battery temperature or voltage, the device will immediately notify the server.

[0751] Input: Abnormal sensor data (e.g. temperature above 30°C).

[0752] Data processing / calculation: Filters out abnormal data and notifies the server.

[0753] Output: Error notification data to the server.

[0754] Specific operation: The device detects abnormal data and immediately sends the data to the server using the Wi-Fi module.

[0755] Step 9: Instructions for handling abnormal situations

[0756] The server analyzes the anomaly data and determines the appropriate response (e.g., reducing the charging current).

[0757] Input: Abnormality notification data from the terminal.

[0758] Data processing / calculation: Analyzing abnormal data and determining countermeasures.

[0759] Output: Instructions to the terminal.

[0760] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[0761] Step 10: Charging complete and notification

[0762] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0763] Input: Battery state-of-charge data.

[0764] Data processing / calculation: Determining when charging is complete and notifying the user.

[0765] Output: Charging completion notification.

[0766] Specific operation: The device detects when the battery is 100% charged, stops charging, and notifies the user via an LED display or other means.

[0767] Step 11: Save Data

[0768] The server stores all data about the charging session and uses it to improve the charging control algorithm for future charging sessions.

[0769] Input: Charging session data.

[0770] Data processing / computation: Data storage and analysis.

[0771] Output: Stored charging data.

[0772] What it does: The server stores the details of the charging session in a database and analyzes them to improve the algorithm next time.

[0773] In this way, at each step, the server, terminal, and user play their respective roles to efficiently and safely manage the battery charging process.

[0774] (Application example 1)

[0775] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0776] Current battery management systems for autonomous mobile machines have problems with both charging efficiency and safety. Furthermore, they lack the means to quickly detect and respond to abnormal battery conditions, which increases operating costs and threatens machine safety. Furthermore, insufficient optimization of charging patterns shortens battery life and results in increased maintenance frequency.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0778] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for storing charging session data and using it to improve the algorithm for the next charging session, and means for automating battery management for the autonomous mobile machine. This makes it possible to monitor the battery status in real time and dynamically adjust the optimal charging pattern. In addition, by quickly detecting abnormal conditions and adjusting the charging current, safety and efficiency are improved and operational costs are reduced.

[0779] A "battery" is a device that stores electrical energy as chemical energy and supplies it as electrical energy when needed.

[0780] "Voltage" is a physical quantity that indicates the electrical potential difference required to cause current to flow, and is expressed in volts (V).

[0781] "Temperature" is an indicator of the thermal energy state of a substance and is expressed in degrees Celsius (℃) or Fahrenheit (℉).

[0782] "Real-time monitoring means" refers to a device or system that instantly acquires data and instantly analyzes and displays the acquired data.

[0783] "Monitoring data" is data that collects monitored physical or environmental information, such as battery voltage or temperature.

[0784] A "server" is a computer system that provides various services and functions to client terminals via a network.

[0785] An "optimal charging pattern" is a charging process in which current and voltage are adjusted to achieve efficient charging of the battery.

[0786] A "terminal" is a computer device or machine that receives instructions from a server and performs a specific function based on those instructions.

[0787] "Charging parameters" refer to various control values ​​(e.g., current, voltage) that are set in the charging process.

[0788] A "charging session" is the entire process from when a battery is connected to a charger until it is fully charged.

[0789] An "algorithm" is a set of logical steps that represents a procedure or computational method for solving a particular problem.

[0790] An "autonomous mobile machine" is a machine that has the ability to make its own decisions and take action without requiring external instructions or control.

[0791] "Abnormal condition" refers to a situation where a phenomenon beyond the normal operating range (e.g., overheating, overvoltage) occurs.

[0792] "Operating costs" are all costs incurred to operate a system or machine (e.g., energy costs, labor costs).

[0793] The present invention relates to a system for efficiently and safely managing batteries for autonomous mobile machines in factories. This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. The configuration and operation of this system are described in detail below.

[0794] server

[0795] The server manages the latest version of the charging control algorithm and sends updates to the device as needed. It receives and analyzes battery voltage and temperature data sent in real time, calculates the optimal charging pattern, and sends instructions to the device. It also analyzes abnormal conditions and sends instructions to the device to take appropriate action. It also saves charging session data and uses it to improve the algorithm for the next time.

[0796] Terminal

[0797] The terminal receives instructions from the server and dynamically adjusts charging parameters (current and voltage) based on those instructions. It also measures the battery's voltage and temperature at regular intervals and sends the data to the server. If it detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server and adjusts or suspends the charging current according to the server's instructions.

[0798] User

[0799] Users initiate a charging session by connecting their device to a charger and are notified when the battery is fully charged so they can take appropriate action.

[0800] Specific examples

[0801] For example, consider an autonomous mobile machine used in a factory. When this machine arrives at a charging station, the system operates as follows:

[0802] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[0803] 2. Start charging: When the user connects the device to the charging station, charging begins. The terminal sets the initial charging current (e.g., 1A).

[0804] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[0805] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device complies with this instruction.

[0806] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[0807] Prompt Sentence Examples

[0808] "Design an app that intelligently manages the batteries of an autonomous mobile robot. Specifically, it monitors the battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. If an abnormal temperature rise or voltage fluctuation is detected, it immediately adjusts the charging. Implement this in Python."

[0809] This invention is expected to improve the efficiency of battery management for autonomous mobile machines in factories, improve safety, and reduce operating costs. Specifically, it enables real-time battery monitoring, optimization of charging patterns, and rapid response to abnormal conditions, significantly improving overall operational efficiency.

[0810] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0811] Step 1:

[0812] The server maintains the latest version of the charging control algorithm and sends updates to the device as needed. Specifically, when the server receives an update request, it transfers the latest version of the algorithm to the device. At this time, the server properly packages the files required for the update and sends them in a format that the device can receive and extract.

[0813] Input: Update request from device

[0814] Output: Latest algorithm file

[0815] Step 2:

[0816] The device applies the latest algorithm file received from the server, measures the initial battery status (voltage and temperature), and sends it to the server. Specifically, the device measures the voltage and temperature using a sensor inside the device, formats the data, and sends it to the server. This measurement and data transmission is based on a certain protocol.

[0817] Input: Algorithm file from server

[0818] Output: Voltage and temperature data

[0819] Step 3:

[0820] When a user connects a device to a charger, the device starts a charging session. Specifically, when the device detects the charger, it generates a charging start signal and sets the initial charging current (e.g., 1A). At this point, the device sends its initial state data to the server.

[0821] Input: Connect to charger

[0822] Output: Charging start signal, initial charging current setting

[0823] Step 4:

[0824] The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. Specifically, it acquires data from sensors within the device and sends it to the server in a fixed format. This data is updated in real time and at a fixed interval.

[0825] Input: Voltage and temperature data from the battery

[0826] Output: Measurement data sent to the server

[0827] Step 5:

[0828] The server calculates the optimal charging current based on the received data and sends the instructions to the device. Specifically, it uses an AI model to predict the optimal charging current for the current voltage and temperature, and notifies the device of the results. This calculation is performed in real time and is updated as data is received.

[0829] Input: Voltage and temperature data from the terminal

[0830] Output: Optimal charging current indication

[0831] Step 6:

[0832] The device adjusts the charging current according to instructions from the server. Specifically, the charging control system in the device changes the setting value and adjusts the charging current accordingly. This operation is designed to be performed quickly.

[0833] Input: Charging current instruction from the server

[0834] Output: Regulated charging current

[0835] Step 7:

[0836] When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server, which then analyzes the abnormal condition and instructs the appropriate response. Specifically, the abnormality data is sent to the server, which then runs an algorithm to identify the cause of the abnormality and sends an appropriate response (such as reducing the charging current) to the device.

[0837] Input: Abnormal voltage and temperature data

[0838] Output: Abnormal notification to server, instruction of analysis results

[0839] Step 8:

[0840] The terminal adjusts the charging current and suspends charging if necessary based on instructions from the server regarding abnormal conditions. Specifically, the charging control system receives instructions from the server and promptly adjusts or suspends charging current according to the instructions.

[0841] Input: Error handling instructions from the server

[0842] Output: Regulated charging current, suspended charging

[0843] Step 9:

[0844] When the battery is determined to be fully charged, the device stops charging and notifies the user. Specifically, when the device meets certain voltage and temperature conditions, it generates a charging completion signal and notifies the user of this information by displaying or audibly notifying the user.

[0845] Input: Fully charged battery data

[0846] Output: Charging stop signal, notification to user

[0847] Step 10:

[0848] The server stores data from all charging sessions and uses it to improve the algorithm for the next time. Specifically, the received data is stored in a database and the analytical algorithm uses this data to optimize future charging templates.

[0849] Input: Charging session data

[0850] Output: Data stored in database, improved algorithm

[0851] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0852] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system is mainly composed of three elements: a server, a terminal, and a user. The specific operation of the system and program is described below.

[0853] Program Overview

[0854] Initialization

[0855] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the device as needed.

[0856] 2. The device receives and applies updates, and measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[0857] Start charging

[0858] 1. When a user connects a device to a charger, the device begins a charging session.

[0859] 2. The device sets the charging current and voltage based on the latest charging control algorithm and emotion recognition algorithm received from the server.

[0860] Real-time monitoring and adjustment

[0861] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[0862] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[0863] 3. The terminal adjusts the charging parameters based on instructions from the server.

[0864] Emotion recognition and response

[0865] 1. The device activates an emotion engine that recognizes the user's emotions in real time and sends the data to the server.

[0866] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[0867] 3. Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[0868] Anomaly detection and response

[0869] 1. If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0870] 2. The server analyzes the abnormal condition and sends instructions to the terminal to pause or adjust charging as necessary.

[0871] 3. The device adjusts the charging current or temporarily suspends charging according to instructions from the server.

[0872] Charging completed

[0873] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0874] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[0875] Specific examples

[0876] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[0877] 1. Initialization: The server sends the latest charging control and emotion recognition algorithms to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C) and the user's emotional state (e.g., relaxed state).

[0878] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[0879] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature at regular intervals and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[0880] 4. Emotion Recognition and Response: If the device detects the user's stress state, it sends that information to the server. Based on this emotional state, the server customizes a charging progress notification message and sends it to the user (e.g., "Please relax. Charging is going well").

[0881] 5. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[0882] 6. Charging complete: When the battery is fully charged, the device stops charging and notifies the user. The server stores the data from this charging session and the emotion data for future algorithm improvements.

[0883] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] The server prepares the latest versions of the charging control algorithm and emotion recognition algorithm and sends updates to the device as needed.

[0887] Step 2:

[0888] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[0889] Step 3:

[0890] The user connects the terminal to a charger and the terminal begins a charging session.

[0891] Step 4:

[0892] The terminal applies the latest charging control algorithm and emotion recognition algorithm received from the server to set the initial charging current and voltage.

[0893] Step 5:

[0894] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[0895] Step 6:

[0896] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[0897] Step 7:

[0898] The terminal adjusts the charging current and voltage according to instructions from the server.

[0899] Step 8:

[0900] The device activates an emotion engine that recognizes the user's emotions in real time and transmits the data to a server.

[0901] Step 9:

[0902] The server analyzes the received emotion data and sends instructions to the terminal to adjust charging parameters based on the recognized user emotion.

[0903] Step 10:

[0904] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[0905] Step 11:

[0906] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[0907] Step 12:

[0908] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[0909] Step 13:

[0910] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[0911] Step 14:

[0912] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[0913] Step 15:

[0914] The server stores charging session data and emotion data to help improve the algorithm next time.

[0915] The above are the specific processing steps of the program.

[0916] Example 2

[0917] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0918] Although conventional charging systems monitor battery status and provide optimal charging patterns, they are unable to take the user's emotional state into account, which results in a lack of user experience. Furthermore, the lack of automated anomaly detection and appropriate response leaves concerns about battery life and safety. These issues need to be addressed.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0920] In this invention, the server includes means for monitoring the voltage and temperature of the battery in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting an instruction to the terminal, means for adjusting charging parameters based on the instruction from the server, means for recognizing a user's emotion, means for transmitting the recognized emotion data to the server, and means for adjusting charging parameters based on the user's emotion, thereby enabling the battery to be charged safely and efficiently while taking the user's emotional state into consideration.

[0921] "Means for monitoring battery voltage and temperature in real time" refers to sensors or modules that continuously measure the battery voltage and temperature, allowing the battery condition to be constantly monitored.

[0922] The "means for transmitting the monitoring data to the server" refers to a communication module or software that sends the voltage and temperature data obtained from the battery to the server via a network.

[0923] "Server means for calculating an optimum charging pattern and transmitting instructions to the terminal" refers to a server that analyzes the received data, calculates an optimum charging method, and transmits the instructions to the terminal.

[0924] "Means for adjusting charging parameters based on instructions from the server" refers to a component or software on the terminal side that automatically changes parameters such as charging current and voltage according to instructions provided by the server.

[0925] "Means for recognizing user emotions" refers to cameras and microphones that analyze the user's facial expressions, voice, and movements to identify emotions, as well as software that analyzes them.

[0926] The "means for transmitting the recognized emotion data to the server" refers to a communication module or software for sending the user's emotion data recognized on the terminal to the server via a network.

[0927] The "means for adjusting charging parameters based on the user's emotions" refers to a component or software on the terminal side that appropriately changes charging parameters depending on the user's emotional state.

[0928] MODE FOR CARRYING OUT THE INVENTION

[0929] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal, and a user. The specific operation of this system is explained below.

[0930] Initialization

[0931] 1. The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed. This is done using a management system on the server and network communication methods.

[0932] 2. After receiving and applying the update, the device measures and records the initial battery status (voltage and temperature) and the user's emotional state. The device is equipped with sensors and emotion recognition software. The sensors function as a voltmeter and thermometer, and the emotion recognition software detects the user's emotions via the camera and microphone.

[0933] Start charging

[0934] When a user connects a device to a charger, the device starts a charging session. The device sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server. This initial setting is performed using the device's internal charging control software.

[0935] Real-time monitoring and adjustment

[0936] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server. This process utilizes the device's internal sensors and communication module.

[0937] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device. The server contains an analytical algorithm and database that are used to determine the appropriate charging conditions.

[0938] 3. The device adjusts the charging parameters based on instructions from the server. This operation is performed by the charging control software within the device.

[0939] Emotion recognition and response

[0940] 1. The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to a server. Emotion recognition uses facial recognition and voice analysis technologies.

[0941] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion. The server is equipped with an emotion analysis algorithm.

[0942] 3. The device customizes the charging progress notification message based on instructions from the server and provides it to the user using the device's internal notification system.

[0943] Anomaly detection and response

[0944] 1. If the device detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. This process uses sensors and communication modules.

[0945] 2. The server analyzes the abnormal condition and sends instructions to the device to pause or adjust charging as necessary. The server is equipped with an abnormality analysis algorithm.

[0946] 3. The device adjusts the charging current and pauses charging according to instructions from the server, which is also done by the charging control software.

[0947] Charging completed

[0948] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user. The notification is done through the device's internal notification system.

[0949] 2. The server stores charging session data and emotion data to help improve the algorithm for the next time. A database system is used, which allows for long-term optimization.

[0950] Examples and prompts

[0951] For example, when a user connects a smartphone to a charger, the system operates as follows: When charging begins, the device receives the latest algorithm from the server and performs initial setup. It then monitors voltage and temperature in real time and performs emotion recognition. At the same time, it also detects anomalies to ensure safe charging. A specific example of a prompt is as follows:

[0952] "Please explain how your system works as a user charges their smartphone. Please provide detailed instructions, focusing especially on charging optimization and emotion recognition."

[0953] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[0954] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0955] Step 1:

[0956] The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed.

[0957] Input: The update management system on the server detects a new algorithm version.

[0958] Data processing: Packages the updates and prepares them for transmission.

[0959] Output: The update packet is sent to the device.

[0960] Specific operation: The server periodically scans the database and, if a new algorithm version is found, sends it to the device.

[0961] Step 2:

[0962] The terminal receives the updated algorithm and applies it to the system.

[0963] Input: The update packet sent by the server.

[0964] Data processing: Unpacking the update packet, analyzing the algorithm and applying it to the system.

[0965] Output: The system is updated with the new algorithm.

[0966] Specific operation: When the device receives the update packet, it unpacks it, installs the algorithm, and restarts the system.

[0967] Step 3:

[0968] The device measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[0969] Input: Raw data from battery sensors, camera and microphone.

[0970] Data processing: Analyze raw data, quantify voltage and temperature, and classify emotional states.

[0971] Output: Battery voltage, temperature data and user emotion data.

[0972] How it works: The device measures the battery voltage to be 3.7V and the temperature to be 25°C, and the emotion recognition software classifies the user's emotional state as "relaxed."

[0973] Step 4:

[0974] When a user connects a terminal to a charger, the terminal begins a charging session.

[0975] Input: The user plugs the device into a charger.

[0976] Data processing: Recognize as a trigger to start charging.

[0977] Output: Start of charging session.

[0978] Specific behavior: When a user plugs a device into a charger, the system automatically initiates the charging protocol.

[0979] Step 5:

[0980] The terminal sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server.

[0981] Input: Charging control algorithm from the server.

[0982] Data processing: Calculation of initial charging settings based on algorithms.

[0983] Output: Initial charging current and voltage settings.

[0984] Specific operation: The terminal sets the initial charging current to 1A and the voltage based on the received algorithm.

[0985] Step 6:

[0986] The terminal measures the battery voltage and temperature at regular intervals and transmits the data to the server.

[0987] Input: Voltage and temperature data from sensors.

[0988] Data processing: Collecting, organizing, and converting data into packets for transmission.

[0989] Output: Organized monitoring data.

[0990] Specific operation: The device measures the voltage and temperature every 5 seconds and sends the data to the server.

[0991] Step 7:

[0992] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the terminal.

[0993] Input: Monitoring data sent from the device.

[0994] Data processing: Analyzing received data and optimizing charging patterns.

[0995] Output: Instruction data to the terminal.

[0996] Specific operation: The server compares past data with real-time data and sends an instruction to the device to increase the charging current to 1.5A.

[0997] Step 8:

[0998] The terminal adjusts the charging parameters based on instructions from the server.

[0999] Input: Instruction data from the server.

[1000] Data processing: Adjusting charging parameters based on instruction data.

[1001] Output: New charging parameter settings.

[1002] Specific operation: The device adjusts the charging current to 1.5A.

[1003] Step 9:

[1004] The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to the server.

[1005] Input: Real-time data from camera and microphone.

[1006] Data processing: Data analysis using sentiment analysis algorithms.

[1007] Output: Emotional state data.

[1008] Specific operation: The device analyzes the user's facial expressions and voice, and if it determines that the user is in a stressful state, it sends that data to the server.

[1009] Step 10:

[1010] The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[1011] Input: User emotion data.

[1012] Data processing: Optimizing charging parameters based on emotion data.

[1013] Output: Instructions to adjust charging parameters to the device.

[1014] Specific operation: The server recognizes that the user is in a stressed state and sends instructions to customize the charging progress notification message.

[1015] Step 11:

[1016] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[1017] Input: Customization instructions from the server.

[1018] Data processing: Customizing notification messages based on your instructions.

[1019] Output: A customized notification message.

[1020] What it does: The device displays a message like "Relax, charging is going well."

[1021] Step 12:

[1022] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1023] Input: Anomaly data from sensors.

[1024] Data processing: Analyzes abnormal data and converts it into packets to be notified to the server.

[1025] Output: Anomaly notification packet.

[1026] Specific operation: The device detects a sudden rise in temperature (e.g., 30°C) and sends that information to the server.

[1027] Step 13:

[1028] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[1029] Input: Abnormality notification data from the terminal.

[1030] Data processing: Analyze abnormal data and calculate appropriate countermeasures.

[1031] Output: Countermeasure instruction data to the terminal.

[1032] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[1033] Step 14:

[1034] The terminal adjusts the charging current and temporarily suspends charging according to instructions from the server.

[1035] Input: Countermeasure instruction data from the server.

[1036] Data processing: Adjust charging parameters based on instructions.

[1037] Output: New charging parameter settings.

[1038] Specific action: The device reduces the charging current to 0.5A.

[1039] Step 15:

[1040] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1041] Input: Battery monitoring data.

[1042] Data processing: Battery level analysis and charging stop decision.

[1043] Output: Charging completion notification.

[1044] What it does: The device determines that the battery has reached 100% and notifies you that your battery is fully charged.

[1045] Step 16:

[1046] The server stores charging session data and emotion data to help improve the algorithm next time.

[1047] Input: Charging session data and emotion data from the device.

[1048] Data processing: Received data is stored in a database.

[1049] Output: Saved charging session data.

[1050] Specific operation: The server stores all charging data and emotion data in a database and analyzes it the next time the algorithm is updated.

[1051] (Application example 2)

[1052] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1053] To improve the efficiency and safety of battery charging systems, it is important to manage battery voltage and temperature, and a system that monitors these in real time and dynamically adjusts the optimal charging pattern is required. In addition, because the user's emotional state can affect the battery charging process, a system that combines emotion recognition functionality is required to optimally manage the charging status and provide appropriate information to the user.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for recognizing the user's emotion, means for transmitting the recognized emotion data to the server, and means for customizing a charging progress notification message based on the emotion data. This makes it possible to efficiently and safely charge the battery while maintaining good operating conditions and taking the user's emotional state into consideration.

[1055] 1. "Battery" means a device that stores electricity and whose operating conditions, such as voltage and temperature, can be monitored.

[1056] 2. "Voltage" refers to the electrical pressure generated within the battery, which directly affects its state of charge and performance.

[1057] 3. "Temperature" refers to the state of thermal energy in the battery and its surrounding environment, and plays an important role in charging efficiency and safety.

[1058] 4. "Real-time" refers to the ability to process and reflect information and data immediately, without delay.

[1059] 5. "Monitoring" refers to the act of continuously observing and recording certain physical or electronic parameters.

[1060] 6. "Server" means a computer system connected to a network that is a centralized device for data processing and management.

[1061] 7. "Terminal" refers to a computer device or sensor device that is directly operated by a user and that exchanges information in conjunction with a server.

[1062] 8. "Charging pattern" refers to the method of controlling current and voltage during the battery charging process.

[1063] 9. “Emotion recognition” is a technology for understanding and assessing a user’s emotional state, using sensors to analyze facial expressions and voice data.

[1064] 10. "Charging progress notification message" is a message that informs the user of information such as the battery charging status and estimated completion time.

[1065] 11. "Abnormal Condition" means a condition, such as battery voltage or temperature, that is outside of the normal operating range and may affect safety or performance.

[1066] 12. "Data retention" refers to the long-term storage of collected information for later analysis or reference.

[1067] This invention is aimed at factory robots, and combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal (robot), and a user (worker).

[1068] Program Overview:

[1069] Initialization:

[1070] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed.

[1071] 2. The robot receives and applies updates, measures and records the initial battery condition (voltage and temperature) and the worker's emotional state.

[1072] Start charging:

[1073] 1. When a worker connects a robot to a charging station, the robot begins a charging session.

[1074] 2. The robot sets the charging current and voltage based on the latest charging control algorithm received from the server.

[1075] Real-time monitoring and adjustment:

[1076] 1. The robot measures the battery voltage and temperature at regular intervals and sends the data to the server.

[1077] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot.

[1078] 3. The robot adjusts the charging parameters based on instructions from the server.

[1079] Emotion recognition and response:

[1080] 1. The robot activates an emotion engine to recognize the worker's emotions in real time and sends the data to the server.

[1081] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized emotion of the worker.

[1082] 3. Based on instructions from the server, the robot customizes a charging progress notification message and provides it to the worker.

[1083] Anomaly detection and response:

[1084] 1. If the robot detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1085] 2. The server analyzes the abnormal condition and sends instructions to the robot to pause or adjust charging as necessary.

[1086] 3. The robot adjusts the charging current or pauses charging according to instructions from the server.

[1087] Charging complete:

[1088] 1. When the robot determines that the battery is fully charged, it stops charging and notifies the worker.

[1089] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[1090] Hardware and software used:

[1091] 1. Hardware:

[1092] Robot body: An industrial robot used in factories.

[1093] Emotion recognition sensors: camera and microphone.

[1094] Server: A centralized device that manages and analyzes data.

[1095] 2. Software:

[1096] Emotion recognition algorithm: Use emotion recognition APIs such as Azure Cognitive Services.

[1097] Charging control algorithm: Uses machine learning frameworks such as TensorFlow and PyTorch to analyze real-time data.

[1098] Examples:

[1099] In a factory, a robot uses an emotion recognition sensor to detect when a worker is stressed. After obtaining this information, the robot sends the data to a server, which analyzes it. Based on the analysis results, the robot sends an instruction to adjust the charging parameters so that "the worker is tired, so reduce the charging speed to ensure safety."

[1100] Example prompt sentence:

[1101] "Detecting stress levels from workers' facial expressions"

[1102] "Calculates optimal charging parameters when battery temperature rises"

[1103] "Analyzes emotional and charging data in real time to propose optimal charging plans"

[1104] These detailed program and prompt example statements demonstrate that the invention is possible.

[1105] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1106] Step 1:

[1107] The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed. The input is the latest algorithm data, and the output is instructions for updating the algorithm to the robot. Specifically, the server sends the latest algorithm, and the robot receives it and applies it.

[1108] Step 2:

[1109] The robot receives and applies updates, and measures and records the initial battery state (voltage and temperature) and the worker's emotional state. The input is the measurement data of the initial state, and the output is data transmission to the server. Specifically, the robot uses sensors to measure voltage and temperature and transmits that data to the server.

[1110] Step 3:

[1111] When a worker connects a robot to a charging station, the robot begins a charging session. The input is the robot's connection to the charging station, and the output is the start of charging. Specifically, the robot checks the connection and begins charging.

[1112] Step 4:

[1113] The robot sets the charging current and voltage based on the latest charging control algorithm received from the server. The input is the algorithm instruction from the server, and the output is the set charging parameters. Specifically, the robot receives instructions from the server and performs the operation of setting the charging parameters.

[1114] Step 5:

[1115] The robot measures the battery voltage and temperature at regular intervals and sends the data to the server. The input is the battery voltage and temperature data, and the output is the data sent to the server. Specifically, the robot measures data with sensors and sends it to the server.

[1116] Step 6:

[1117] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot. The input is voltage and temperature data from the robot, and the output is the calculation result of the optimal charging pattern. Specifically, the server analyzes the data, calculates the charging pattern, and sends instructions to the robot.

[1118] Step 7:

[1119] The robot operates an emotion engine to recognize the emotions of the worker in real time and sends the data to a server. The input is the worker's emotional state data, and the output is the transmission of emotion data to the server. Specifically, the robot uses an emotion recognition sensor to acquire data and send it to the server.

[1120] Step 8:

[1121] The server analyzes the received emotion data and adjusts the charging parameters based on the recognized emotion of the worker. The input is the emotion data from the robot, and the output is the result of adjusting the optimal charging parameters. Specifically, the server analyzes the emotion data, performs calculations to adjust the charging parameters, and sends instructions to the robot.

[1122] Step 9:

[1123] The robot customizes the charging progress notification message based on instructions from the server and provides it to the worker. The input is the notification instruction from the server, and the output is the customized notification message. Specifically, the robot receives the instruction from the server and performs an operation to display an appropriate message to the worker.

[1124] Step 10:

[1125] If the robot detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. The input is abnormal status data, and the output is a notification to the server. Specifically, the robot detects an abnormality and sends that information to the server.

[1126] Step 11:

[1127] The server analyzes the abnormal condition and sends instructions to the robot to suspend or adjust charging as necessary. The input is abnormal condition data from the robot, and the output is adjustment instructions. Specifically, the server analyzes the abnormal data, calculates safe charging parameters, and sends instructions to the robot.

[1128] Step 12:

[1129] The robot adjusts the charging current or temporarily suspends charging according to instructions from the server. The input is the adjustment instruction from the server, and the output is the executed charging parameter adjustment. Specifically, the robot adjusts the charging based on the instructions from the server.

[1130] Step 13:

[1131] When the robot determines that the battery is fully charged, it stops charging and notifies the worker. The input is battery charge status data, and the output is a notification that charging is complete. Specifically, the robot monitors the battery status, confirms that charging is complete, and notifies the worker.

[1132] Step 14:

[1133] The server stores the charging session data and emotion data to help improve the algorithm next time. The input is the charging session data and emotion data, and the output is the stored data. Specifically, the server stores the data and prepares it for use in improving the algorithm next time.

[1134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1136] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1137] [Third embodiment]

[1138] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1142] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1146] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1148] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1149] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1150] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[1151] Program Overview

[1152] Initialization

[1153] 1. The server manages the latest version of the charging control algorithm and sends updates to the device as needed.

[1154] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature).

[1155] Start charging

[1156] 1. When a user connects a device to a charger, the device begins a charging session.

[1157] 2. The terminal sets the charging current and voltage based on the latest charging control algorithm received from the server.

[1158] Real-time monitoring and adjustment

[1159] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[1160] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[1161] 3. The device adjusts the charging current and voltage according to instructions from the server.

[1162] Anomaly detection and response

[1163] 1. When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server.

[1164] 2. The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging.

[1165] 3. The device adjusts or pauses the charging current according to instructions from the server.

[1166] Charging completed

[1167] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1168] 2. The server stores the charging session data and uses it to improve the charging control algorithm for the next time.

[1169] Specific examples

[1170] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[1171] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[1172] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[1173] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[1174] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[1175] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[1176] In this way, the present invention provides efficient and safe charging while maintaining good battery operating condition.

[1177] The processing flow will be explained below.

[1178] I understand. Below I will explain the program's processing in concrete steps.

[1179] Step 1:

[1180] The server prepares the latest version of the charging control algorithm and sends updates to the device as needed.

[1181] Step 2:

[1182] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[1183] Step 3:

[1184] The user connects the terminal to a charger and the terminal begins a charging session.

[1185] Step 4:

[1186] The terminal applies the latest charging control algorithm received from the server to set the initial charging current and voltage.

[1187] Step 5:

[1188] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[1189] Step 6:

[1190] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[1191] Step 7:

[1192] The terminal adjusts the charging current and voltage according to instructions from the server.

[1193] Step 8:

[1194] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1195] Step 9:

[1196] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[1197] Step 10:

[1198] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[1199] Step 11:

[1200] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1201] Step 12:

[1202] The server stores data from the charging session to help improve the charging control algorithm for the next time.

[1203] The above are the specific processing steps of the program.

[1204] Example 1

[1205] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1206] To ensure both safety and efficiency during battery charging, it is important to monitor battery voltage and temperature in real time, detect abnormalities early, and respond accordingly. However, conventional systems have not fully established means to efficiently solve these issues, making it difficult to dynamically adjust the optimal charging pattern. The present invention aims to solve these issues and achieve safe and efficient battery charging.

[1207] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1208] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for managing the latest charging control algorithm and transmitting updates to the terminal, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for detecting the user's connection of the terminal to a charger at the start of charging and starting a charging session, and means for periodically measuring the battery status and analyzing the data. This makes it possible to monitor the battery status in real time, detect abnormalities early, and dynamically adjust the optimal charging pattern.

[1209] A "battery" is a device that stores electrical energy as chemical energy and is used to provide electrical current.

[1210] "Voltage" refers to the potential difference between two points in an electrical circuit, and is an important indicator for evaluating the charge and discharge state of a battery.

[1211] "Temperature" is an indicator of the thermal state of an object, and is a factor that has a significant impact on the safety and performance of a battery.

[1212] "Real-time" refers to a state in which data processing and information collection occur immediately, without delay.

[1213] "Monitoring" is the act of continuously checking the condition and changes of an object and detecting abnormalities early.

[1214] "Terminal" refers to an electronic device that is equipped with a battery and has the role of controlling charging and discharging.

[1215] A "charge control algorithm" is a set of calculations and logic for optimizing the charging pattern of a battery.

[1216] "Server" means a computer system that processes and manages data on a network, and manages and updates the charging control algorithm.

[1217] "User" refers to the person or end user who operates the device and charges the battery.

[1218] "Data" is a collection of information including measurement results such as voltage and temperature, and serves as the basic information for processing by servers and terminals.

[1219] "Abnormal" refers to a condition in which the battery voltage or temperature deviates from the preset normal values, and immediate action is required.

[1220] "Charging current" refers to the current supplied to the battery, which affects the charging rate and the health of the battery.

[1221] "Charging parameters" refer to the set values ​​and conditions of the charging process, such as current, voltage, and time.

[1222] "Specifications" refer to the technical requirements and conditions of the system or method provided by the present invention.

[1223] MODE FOR CARRYING OUT THE INVENTION

[1224] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[1225] System Program Overview

[1226] The system starts with a server that manages the latest charging control algorithms and distributes them to devices as needed. The device uses these algorithms to measure battery voltage and temperature in real time and sends the data to the server. The server analyzes the data, calculates the optimal charging pattern, and sends instructions to the device. The user simply connects the device to the charger, and the charging process is automatically optimized.

[1227] Hardware and software used

[1228] 1. Server: Uses cloud services (e.g., AWS, Google Cloud) for computation and data management, and generative AI models (e.g., TensorFlow, PyTorch) for data analysis and optimization.

[1229] 2. Terminal: Uses a microcontroller (e.g., Arduino, Raspberry Pi) and a sensor device (e.g., voltage sensor, temperature sensor). Sends data to the server using a communication module (e.g., ESP8266).

[1230] 3. User: This represents a smartphone or other device that needs to be charged. The user connects the device to the charger.

[1231] Specific examples

[1232] For example, consider the following scenario where a user plugs a smartphone into a charger:

[1233] Initialization

[1234] 1. The server manages the latest charging control algorithm and sends updates to the device as needed. The server uses a cloud service to keep the algorithm up to date.

[1235] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature). For example, the initial voltage is 3.7V and the temperature is 25°C.

[1236] Start charging

[1237] 1. When a user connects a smartphone to a charger, the device sets an initial charging current of 1A and begins the charging session.

[1238] Real-time monitoring and adjustment

[1239] 1. The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. The data is sent in real time using the communication module.

[1240] 2. The server analyzes the received data, calculates the optimal charging current (e.g., 1.5A), and notifies the device. Generative AI models are used to analyze and optimize the data.

[1241] 3. The device adjusts the charging current to 1.5A according to instructions from the server.

[1242] Anomaly detection and response

[1243] 1. When the device detects that the battery temperature has risen to 30°C, it immediately notifies the server.

[1244] 2. The server analyzes this abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[1245] 3. The device follows the instruction and reduces the charging current to 0.5A.

[1246] Charging completed

[1247] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1248] 2. The server saves the data from this charging session and stores it in a database for use in improving the algorithm next time.

[1249] Prompt Sentence Examples

[1250] Below are some example prompts to input to a generative AI model:

[1251] Explain the battery management system of a smartphone, taking into account the following points:

[1252] 1. Roles of servers, terminals, and users.

[1253] 2. How to monitor battery voltage and temperature.

[1254] 3. How to update and apply the charging control algorithm.

[1255] 4. How to respond when an abnormality is detected.

[1256] 5. How to store and use data from charging sessions.

[1257] Thus, the present invention provides a comprehensive system for efficiently and safely managing the battery charging process.

[1258] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1259] System program processing flow

[1260] Step 1: Initialization

[1261] The server manages and regularly updates the latest charging control algorithms, which are used to calculate safe charging profiles for batteries and are optimized using AI models.

[1262] Input: Current charge control algorithm and historical data about the battery.

[1263] Data processing / calculation: Analyzes data using an AI model and optimizes the charging control algorithm.

[1264] Output: Generation of a new charge control algorithm.

[1265] What it does: The server uses TensorFlow to perform the analysis and saves the new algorithm to the cloud.

[1266] Step 2: Algorithm Updates

[1267] The server transmits the latest charging control algorithm to the terminal.

[1268] Input: A new charge control algorithm.

[1269] Data processing / calculation: Communication processing to send the algorithm to the terminal.

[1270] Output: The new algorithm is applied to the device.

[1271] Specific operation: The server sends the algorithm to the terminal using the HTTP protocol.

[1272] Step 3: Measuring the initial battery condition

[1273] The device measures the initial battery condition (voltage and temperature).

[1274] Input: Sensor data from the battery.

[1275] Data processing / calculation: Collect sensor data and record the initial state.

[1276] Output: Initial voltage and temperature data.

[1277] Specific operation: The terminal acquires data using the voltage sensor and temperature sensor, and measures an initial voltage of 3.7V and an initial temperature of 25°C.

[1278] Step 4: Detecting the start of charging

[1279] When a user connects a terminal to a charger, the terminal begins a charging session.

[1280] Input: Power is supplied to the device's charging port.

[1281] Data processing / calculation: Detects the signal to start charging and sets the initial charging current.

[1282] Output: Start of charging session.

[1283] Specific operation: The device detects power supply from the charger and sets the initial charging current to 1A.

[1284] Step 5: Real-time monitoring

[1285] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and sends the data to the server.

[1286] Input: Sensor data (voltage and temperature).

[1287] Data processing / computation: Sensor data collection and transmission processing.

[1288] Output: Latest voltage and temperature data sent to the server.

[1289] Specific operation: The device uses the Wi-Fi module to send data to the server every 5 seconds.

[1290] Step 6: Data analysis and adjustment instructions

[1291] The server analyzes the received data and calculates the optimal charging current and voltage.

[1292] Input: Battery voltage and temperature data sent from the device.

[1293] Data processing / calculation: Optimal charging patterns are calculated using AI models.

[1294] Output: Adjustment instructions to the terminal.

[1295] Specific operation: The server uses the generative AI model to determine that a charging current of 1.5A is optimal and sends that instruction to the device.

[1296] Step 7: Adjust charging parameters

[1297] The terminal adjusts the charging current and voltage based on instructions from the server.

[1298] Input: Adjustment instructions from the server.

[1299] Data processing / calculation: Set charging parameters according to instructions.

[1300] Output: Regulated charging current and voltage.

[1301] Specific operation: The device adjusts the charging current to 1.5A.

[1302] Step 8: Anomaly detection and response

[1303] If an abnormality occurs in the battery temperature or voltage, the device will immediately notify the server.

[1304] Input: Abnormal sensor data (e.g. temperature above 30°C).

[1305] Data processing / calculation: Filters out abnormal data and notifies the server.

[1306] Output: Error notification data to the server.

[1307] Specific operation: The device detects abnormal data and immediately sends the data to the server using the Wi-Fi module.

[1308] Step 9: Instructions for handling abnormal situations

[1309] The server analyzes the anomaly data and determines the appropriate response (e.g., reducing the charging current).

[1310] Input: Abnormality notification data from the terminal.

[1311] Data processing / calculation: Analyzing abnormal data and determining countermeasures.

[1312] Output: Instructions to the terminal.

[1313] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[1314] Step 10: Charging complete and notification

[1315] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1316] Input: Battery state-of-charge data.

[1317] Data processing / calculation: Determining when charging is complete and notifying the user.

[1318] Output: Charging completion notification.

[1319] Specific operation: The device detects when the battery is 100% charged, stops charging, and notifies the user via an LED display or other means.

[1320] Step 11: Save Data

[1321] The server stores all data about the charging session and uses it to improve the charging control algorithm for future charging sessions.

[1322] Input: Charging session data.

[1323] Data processing / computation: Data storage and analysis.

[1324] Output: Stored charging data.

[1325] What it does: The server stores the details of the charging session in a database and analyzes them to improve the algorithm next time.

[1326] In this way, at each step, the server, terminal, and user play their respective roles to efficiently and safely manage the battery charging process.

[1327] (Application example 1)

[1328] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1329] Current battery management systems for autonomous mobile machines have problems with both charging efficiency and safety. Furthermore, they lack the means to quickly detect and respond to abnormal battery conditions, which increases operating costs and threatens machine safety. Furthermore, insufficient optimization of charging patterns shortens battery life and results in increased maintenance frequency.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1331] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for storing charging session data and using it to improve the algorithm for the next charging session, and means for automating battery management for the autonomous mobile machine. This makes it possible to monitor the battery status in real time and dynamically adjust the optimal charging pattern. In addition, by quickly detecting abnormal conditions and adjusting the charging current, safety and efficiency are improved and operational costs are reduced.

[1332] A "battery" is a device that stores electrical energy as chemical energy and supplies it as electrical energy when needed.

[1333] "Voltage" is a physical quantity that indicates the electrical potential difference required to cause current to flow, and is expressed in volts (V).

[1334] "Temperature" is an indicator of the thermal energy state of a substance and is expressed in degrees Celsius (℃) or Fahrenheit (℉).

[1335] "Real-time monitoring means" refers to a device or system that instantly acquires data and instantly analyzes and displays the acquired data.

[1336] "Monitoring data" is data that collects monitored physical or environmental information, such as battery voltage or temperature.

[1337] A "server" is a computer system that provides various services and functions to client terminals via a network.

[1338] An "optimal charging pattern" is a charging process in which current and voltage are adjusted to achieve efficient charging of the battery.

[1339] A "terminal" is a computer device or machine that receives instructions from a server and performs a specific function based on those instructions.

[1340] "Charging parameters" refer to various control values ​​(e.g., current, voltage) that are set in the charging process.

[1341] A "charging session" is the entire process from when a battery is connected to a charger until it is fully charged.

[1342] An "algorithm" is a set of logical steps that represents a procedure or computational method for solving a particular problem.

[1343] An "autonomous mobile machine" is a machine that has the ability to make its own decisions and take action without requiring external instructions or control.

[1344] "Abnormal condition" refers to a situation where a phenomenon beyond the normal operating range (e.g., overheating, overvoltage) occurs.

[1345] "Operating costs" are all costs incurred to operate a system or machine (e.g., energy costs, labor costs).

[1346] The present invention relates to a system for efficiently and safely managing batteries for autonomous mobile machines in factories. This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. The configuration and operation of this system are described in detail below.

[1347] server

[1348] The server manages the latest version of the charging control algorithm and sends updates to the device as needed. It receives and analyzes battery voltage and temperature data sent in real time, calculates the optimal charging pattern, and sends instructions to the device. It also analyzes abnormal conditions and sends instructions to the device to take appropriate action. It also saves charging session data and uses it to improve the algorithm for the next time.

[1349] Terminal

[1350] The terminal receives instructions from the server and dynamically adjusts charging parameters (current and voltage) based on those instructions. It also measures the battery's voltage and temperature at regular intervals and sends the data to the server. If it detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server and adjusts or suspends the charging current according to the server's instructions.

[1351] User

[1352] Users initiate a charging session by connecting their device to a charger and are notified when the battery is fully charged so they can take appropriate action.

[1353] Specific examples

[1354] For example, consider an autonomous mobile machine used in a factory. When this machine arrives at a charging station, the system operates as follows:

[1355] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[1356] 2. Start charging: When the user connects the device to the charging station, charging begins. The terminal sets the initial charging current (e.g., 1A).

[1357] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[1358] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device complies with this instruction.

[1359] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[1360] Prompt Sentence Examples

[1361] "Design an app that intelligently manages the batteries of an autonomous mobile robot. Specifically, it monitors the battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. If an abnormal temperature rise or voltage fluctuation is detected, it immediately adjusts the charging. Implement this in Python."

[1362] This invention is expected to improve the efficiency of battery management for autonomous mobile machines in factories, improve safety, and reduce operating costs. Specifically, it enables real-time battery monitoring, optimization of charging patterns, and rapid response to abnormal conditions, significantly improving overall operational efficiency.

[1363] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1364] Step 1:

[1365] The server maintains the latest version of the charging control algorithm and sends updates to the device as needed. Specifically, when the server receives an update request, it transfers the latest version of the algorithm to the device. At this time, the server properly packages the files required for the update and sends them in a format that the device can receive and extract.

[1366] Input: Update request from device

[1367] Output: Latest algorithm file

[1368] Step 2:

[1369] The device applies the latest algorithm file received from the server, measures the initial battery status (voltage and temperature), and sends it to the server. Specifically, the device measures the voltage and temperature using a sensor inside the device, formats the data, and sends it to the server. This measurement and data transmission is based on a certain protocol.

[1370] Input: Algorithm file from server

[1371] Output: Voltage and temperature data

[1372] Step 3:

[1373] When a user connects a device to a charger, the device starts a charging session. Specifically, when the device detects the charger, it generates a charging start signal and sets the initial charging current (e.g., 1A). At this point, the device sends its initial state data to the server.

[1374] Input: Connect to charger

[1375] Output: Charging start signal, initial charging current setting

[1376] Step 4:

[1377] The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. Specifically, it acquires data from sensors within the device and sends it to the server in a fixed format. This data is updated in real time and at a fixed interval.

[1378] Input: Voltage and temperature data from the battery

[1379] Output: Measurement data sent to the server

[1380] Step 5:

[1381] The server calculates the optimal charging current based on the received data and sends the instructions to the device. Specifically, it uses an AI model to predict the optimal charging current for the current voltage and temperature, and notifies the device of the results. This calculation is performed in real time and is updated as data is received.

[1382] Input: Voltage and temperature data from the terminal

[1383] Output: Optimal charging current indication

[1384] Step 6:

[1385] The device adjusts the charging current according to instructions from the server. Specifically, the charging control system in the device changes the setting value and adjusts the charging current accordingly. This operation is designed to be performed quickly.

[1386] Input: Charging current instruction from the server

[1387] Output: Regulated charging current

[1388] Step 7:

[1389] When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server, which then analyzes the abnormal condition and instructs the appropriate response. Specifically, the abnormality data is sent to the server, which then runs an algorithm to identify the cause of the abnormality and sends an appropriate response (such as reducing the charging current) to the device.

[1390] Input: Abnormal voltage and temperature data

[1391] Output: Abnormal notification to server, instruction of analysis results

[1392] Step 8:

[1393] The terminal adjusts the charging current and suspends charging if necessary based on instructions from the server regarding abnormal conditions. Specifically, the charging control system receives instructions from the server and promptly adjusts or suspends charging current according to the instructions.

[1394] Input: Error handling instructions from the server

[1395] Output: Regulated charging current, suspended charging

[1396] Step 9:

[1397] When the battery is determined to be fully charged, the device stops charging and notifies the user. Specifically, when the device meets certain voltage and temperature conditions, it generates a charging completion signal and notifies the user of this information by displaying or audibly notifying the user.

[1398] Input: Fully charged battery data

[1399] Output: Charging stop signal, notification to user

[1400] Step 10:

[1401] The server stores data from all charging sessions and uses it to improve the algorithm for the next time. Specifically, the received data is stored in a database and the analytical algorithm uses this data to optimize future charging templates.

[1402] Input: Charging session data

[1403] Output: Data stored in database, improved algorithm

[1404] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1405] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system is mainly composed of three elements: a server, a terminal, and a user. The specific operation of the system and program is described below.

[1406] Program Overview

[1407] Initialization

[1408] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the device as needed.

[1409] 2. The device receives and applies updates, and measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[1410] Start charging

[1411] 1. When a user connects a device to a charger, the device begins a charging session.

[1412] 2. The device sets the charging current and voltage based on the latest charging control algorithm and emotion recognition algorithm received from the server.

[1413] Real-time monitoring and adjustment

[1414] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[1415] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[1416] 3. The terminal adjusts the charging parameters based on instructions from the server.

[1417] Emotion recognition and response

[1418] 1. The device activates an emotion engine that recognizes the user's emotions in real time and sends the data to the server.

[1419] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[1420] 3. Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[1421] Anomaly detection and response

[1422] 1. If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1423] 2. The server analyzes the abnormal condition and sends instructions to the terminal to pause or adjust charging as necessary.

[1424] 3. The device adjusts the charging current or temporarily suspends charging according to instructions from the server.

[1425] Charging completed

[1426] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1427] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[1428] Specific examples

[1429] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[1430] 1. Initialization: The server sends the latest charging control and emotion recognition algorithms to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C) and the user's emotional state (e.g., relaxed state).

[1431] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[1432] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature at regular intervals and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[1433] 4. Emotion Recognition and Response: If the device detects the user's stress state, it sends that information to the server. Based on this emotional state, the server customizes a charging progress notification message and sends it to the user (e.g., "Please relax. Charging is going well").

[1434] 5. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[1435] 6. Charging complete: When the battery is fully charged, the device stops charging and notifies the user. The server stores the data from this charging session and the emotion data for future algorithm improvements.

[1436] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[1437] The processing flow will be explained below.

[1438] Step 1:

[1439] The server prepares the latest versions of the charging control algorithm and emotion recognition algorithm and sends updates to the device as needed.

[1440] Step 2:

[1441] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[1442] Step 3:

[1443] The user connects the terminal to a charger and the terminal begins a charging session.

[1444] Step 4:

[1445] The terminal applies the latest charging control algorithm and emotion recognition algorithm received from the server to set the initial charging current and voltage.

[1446] Step 5:

[1447] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[1448] Step 6:

[1449] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[1450] Step 7:

[1451] The terminal adjusts the charging current and voltage according to instructions from the server.

[1452] Step 8:

[1453] The device activates an emotion engine that recognizes the user's emotions in real time and transmits the data to a server.

[1454] Step 9:

[1455] The server analyzes the received emotion data and sends instructions to the terminal to adjust charging parameters based on the recognized user emotion.

[1456] Step 10:

[1457] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[1458] Step 11:

[1459] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1460] Step 12:

[1461] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[1462] Step 13:

[1463] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[1464] Step 14:

[1465] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1466] Step 15:

[1467] The server stores charging session data and emotion data to help improve the algorithm next time.

[1468] The above are the specific processing steps of the program.

[1469] Example 2

[1470] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1471] Although conventional charging systems monitor battery status and provide optimal charging patterns, they are unable to take the user's emotional state into account, which results in a lack of user experience. Furthermore, the lack of automated anomaly detection and appropriate response leaves concerns about battery life and safety. These issues need to be addressed.

[1472] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1473] In this invention, the server includes means for monitoring the voltage and temperature of the battery in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting an instruction to the terminal, means for adjusting charging parameters based on the instruction from the server, means for recognizing a user's emotion, means for transmitting the recognized emotion data to the server, and means for adjusting charging parameters based on the user's emotion, thereby enabling the battery to be charged safely and efficiently while taking the user's emotional state into consideration.

[1474] "Means for monitoring battery voltage and temperature in real time" refers to sensors or modules that continuously measure the battery voltage and temperature, allowing the battery condition to be constantly monitored.

[1475] The "means for transmitting the monitoring data to the server" refers to a communication module or software that sends the voltage and temperature data obtained from the battery to the server via a network.

[1476] "Server means for calculating an optimum charging pattern and transmitting instructions to the terminal" refers to a server that analyzes the received data, calculates an optimum charging method, and transmits the instructions to the terminal.

[1477] "Means for adjusting charging parameters based on instructions from the server" refers to a component or software on the terminal side that automatically changes parameters such as charging current and voltage according to instructions provided by the server.

[1478] "Means for recognizing user emotions" refers to cameras and microphones that analyze the user's facial expressions, voice, and movements to identify emotions, as well as software that analyzes them.

[1479] The "means for transmitting the recognized emotion data to the server" refers to a communication module or software for sending the user's emotion data recognized on the terminal to the server via a network.

[1480] The "means for adjusting charging parameters based on the user's emotions" refers to a component or software on the terminal side that appropriately changes charging parameters depending on the user's emotional state.

[1481] MODE FOR CARRYING OUT THE INVENTION

[1482] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal, and a user. The specific operation of this system is explained below.

[1483] Initialization

[1484] 1. The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed. This is done using a management system on the server and network communication methods.

[1485] 2. After receiving and applying the update, the device measures and records the initial battery status (voltage and temperature) and the user's emotional state. The device is equipped with sensors and emotion recognition software. The sensors function as a voltmeter and thermometer, and the emotion recognition software detects the user's emotions via the camera and microphone.

[1486] Start charging

[1487] When a user connects a device to a charger, the device starts a charging session. The device sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server. This initial setting is performed using the device's internal charging control software.

[1488] Real-time monitoring and adjustment

[1489] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server. This process utilizes the device's internal sensors and communication module.

[1490] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device. The server contains an analytical algorithm and database that are used to determine the appropriate charging conditions.

[1491] 3. The device adjusts the charging parameters based on instructions from the server. This operation is performed by the charging control software within the device.

[1492] Emotion recognition and response

[1493] 1. The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to a server. Emotion recognition uses facial recognition and voice analysis technologies.

[1494] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion. The server is equipped with an emotion analysis algorithm.

[1495] 3. The device customizes the charging progress notification message based on instructions from the server and provides it to the user using the device's internal notification system.

[1496] Anomaly detection and response

[1497] 1. If the device detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. This process uses sensors and communication modules.

[1498] 2. The server analyzes the abnormal condition and sends instructions to the device to pause or adjust charging as necessary. The server is equipped with an abnormality analysis algorithm.

[1499] 3. The device adjusts the charging current and pauses charging according to instructions from the server, which is also done by the charging control software.

[1500] Charging completed

[1501] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user. The notification is done through the device's internal notification system.

[1502] 2. The server stores charging session data and emotion data to help improve the algorithm for the next time. A database system is used, which allows for long-term optimization.

[1503] Examples and prompts

[1504] For example, when a user connects a smartphone to a charger, the system operates as follows: When charging begins, the device receives the latest algorithm from the server and performs initial setup. It then monitors voltage and temperature in real time and performs emotion recognition. At the same time, it also detects anomalies to ensure safe charging. A specific example of a prompt is as follows:

[1505] "Please explain how your system works as a user charges their smartphone. Please provide detailed instructions, focusing especially on charging optimization and emotion recognition."

[1506] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[1507] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1508] Step 1:

[1509] The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed.

[1510] Input: The update management system on the server detects a new algorithm version.

[1511] Data processing: Packages the updates and prepares them for transmission.

[1512] Output: The update packet is sent to the device.

[1513] Specific operation: The server periodically scans the database and, if a new algorithm version is found, sends it to the device.

[1514] Step 2:

[1515] The terminal receives the updated algorithm and applies it to the system.

[1516] Input: The update packet sent by the server.

[1517] Data processing: Unpacking the update packet, analyzing the algorithm and applying it to the system.

[1518] Output: The system is updated with the new algorithm.

[1519] Specific operation: When the device receives the update packet, it unpacks it, installs the algorithm, and restarts the system.

[1520] Step 3:

[1521] The device measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[1522] Input: Raw data from battery sensors, camera and microphone.

[1523] Data processing: Analyze raw data, quantify voltage and temperature, and classify emotional states.

[1524] Output: Battery voltage, temperature data and user emotion data.

[1525] How it works: The device measures the battery voltage to be 3.7V and the temperature to be 25°C, and the emotion recognition software classifies the user's emotional state as "relaxed."

[1526] Step 4:

[1527] When a user connects a terminal to a charger, the terminal begins a charging session.

[1528] Input: The user plugs the device into a charger.

[1529] Data processing: Recognize as a trigger to start charging.

[1530] Output: Start of charging session.

[1531] Specific behavior: When a user plugs a device into a charger, the system automatically initiates the charging protocol.

[1532] Step 5:

[1533] The terminal sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server.

[1534] Input: Charging control algorithm from the server.

[1535] Data processing: Calculation of initial charging settings based on algorithms.

[1536] Output: Initial charging current and voltage settings.

[1537] Specific operation: The terminal sets the initial charging current to 1A and the voltage based on the received algorithm.

[1538] Step 6:

[1539] The terminal measures the battery voltage and temperature at regular intervals and transmits the data to the server.

[1540] Input: Voltage and temperature data from sensors.

[1541] Data processing: Collecting, organizing, and converting data into packets for transmission.

[1542] Output: Organized monitoring data.

[1543] Specific operation: The device measures the voltage and temperature every 5 seconds and sends the data to the server.

[1544] Step 7:

[1545] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the terminal.

[1546] Input: Monitoring data sent from the device.

[1547] Data processing: Analyzing received data and optimizing charging patterns.

[1548] Output: Instruction data to the terminal.

[1549] Specific operation: The server compares past data with real-time data and sends an instruction to the device to increase the charging current to 1.5A.

[1550] Step 8:

[1551] The terminal adjusts the charging parameters based on instructions from the server.

[1552] Input: Instruction data from the server.

[1553] Data processing: Adjusting charging parameters based on instruction data.

[1554] Output: New charging parameter settings.

[1555] Specific operation: The device adjusts the charging current to 1.5A.

[1556] Step 9:

[1557] The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to the server.

[1558] Input: Real-time data from camera and microphone.

[1559] Data processing: Data analysis using sentiment analysis algorithms.

[1560] Output: Emotional state data.

[1561] Specific operation: The device analyzes the user's facial expressions and voice, and if it determines that the user is in a stressful state, it sends that data to the server.

[1562] Step 10:

[1563] The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[1564] Input: User emotion data.

[1565] Data processing: Optimizing charging parameters based on emotion data.

[1566] Output: Instructions to adjust charging parameters to the device.

[1567] Specific operation: The server recognizes that the user is in a stressed state and sends instructions to customize the charging progress notification message.

[1568] Step 11:

[1569] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[1570] Input: Customization instructions from the server.

[1571] Data processing: Customizing notification messages based on your instructions.

[1572] Output: A customized notification message.

[1573] What it does: The device displays a message like "Relax, charging is going well."

[1574] Step 12:

[1575] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1576] Input: Anomaly data from sensors.

[1577] Data processing: Analyzes abnormal data and converts it into packets to be notified to the server.

[1578] Output: Anomaly notification packet.

[1579] Specific operation: The device detects a sudden rise in temperature (e.g., 30°C) and sends that information to the server.

[1580] Step 13:

[1581] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[1582] Input: Abnormality notification data from the terminal.

[1583] Data processing: Analyze abnormal data and calculate appropriate countermeasures.

[1584] Output: Countermeasure instruction data to the terminal.

[1585] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[1586] Step 14:

[1587] The terminal adjusts the charging current and temporarily suspends charging according to instructions from the server.

[1588] Input: Countermeasure instruction data from the server.

[1589] Data processing: Adjust charging parameters based on instructions.

[1590] Output: New charging parameter settings.

[1591] Specific action: The device reduces the charging current to 0.5A.

[1592] Step 15:

[1593] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1594] Input: Battery monitoring data.

[1595] Data processing: Battery level analysis and charging stop decision.

[1596] Output: Charging completion notification.

[1597] What it does: The device determines that the battery has reached 100% and notifies you that your battery is fully charged.

[1598] Step 16:

[1599] The server stores charging session data and emotion data to help improve the algorithm next time.

[1600] Input: Charging session data and emotion data from the device.

[1601] Data processing: Received data is stored in a database.

[1602] Output: Saved charging session data.

[1603] Specific operation: The server stores all charging data and emotion data in a database and analyzes it the next time the algorithm is updated.

[1604] (Application example 2)

[1605] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1606] To improve the efficiency and safety of battery charging systems, it is important to manage battery voltage and temperature, and a system that monitors these in real time and dynamically adjusts the optimal charging pattern is required. In addition, because the user's emotional state can affect the battery charging process, a system that combines emotion recognition functionality is required to optimally manage the charging status and provide appropriate information to the user.

[1607] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for recognizing the user's emotion, means for transmitting the recognized emotion data to the server, and means for customizing a charging progress notification message based on the emotion data. This makes it possible to efficiently and safely charge the battery while maintaining good operating conditions and taking the user's emotional state into consideration.

[1608] 1. "Battery" means a device that stores electricity and whose operating conditions, such as voltage and temperature, can be monitored.

[1609] 2. "Voltage" refers to the electrical pressure generated within the battery, which directly affects its state of charge and performance.

[1610] 3. "Temperature" refers to the state of thermal energy in the battery and its surrounding environment, and plays an important role in charging efficiency and safety.

[1611] 4. "Real-time" refers to the ability to process and reflect information and data immediately, without delay.

[1612] 5. "Monitoring" refers to the act of continuously observing and recording certain physical or electronic parameters.

[1613] 6. "Server" means a computer system connected to a network that is a centralized device for data processing and management.

[1614] 7. "Terminal" refers to a computer device or sensor device that is directly operated by a user and that exchanges information in conjunction with a server.

[1615] 8. "Charging pattern" refers to the method of controlling current and voltage during the battery charging process.

[1616] 9. “Emotion recognition” is a technology for understanding and assessing a user’s emotional state, using sensors to analyze facial expressions and voice data.

[1617] 10. "Charging progress notification message" is a message that informs the user of information such as the battery charging status and estimated completion time.

[1618] 11. "Abnormal Condition" means a condition, such as battery voltage or temperature, that is outside of the normal operating range and may affect safety or performance.

[1619] 12. "Data retention" refers to the long-term storage of collected information for later analysis or reference.

[1620] This invention is aimed at factory robots, and combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal (robot), and a user (worker).

[1621] Program Overview:

[1622] Initialization:

[1623] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed.

[1624] 2. The robot receives and applies updates, measures and records the initial battery condition (voltage and temperature) and the worker's emotional state.

[1625] Start charging:

[1626] 1. When a worker connects a robot to a charging station, the robot begins a charging session.

[1627] 2. The robot sets the charging current and voltage based on the latest charging control algorithm received from the server.

[1628] Real-time monitoring and adjustment:

[1629] 1. The robot measures the battery voltage and temperature at regular intervals and sends the data to the server.

[1630] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot.

[1631] 3. The robot adjusts the charging parameters based on instructions from the server.

[1632] Emotion recognition and response:

[1633] 1. The robot activates an emotion engine to recognize the worker's emotions in real time and sends the data to the server.

[1634] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized emotion of the worker.

[1635] 3. Based on instructions from the server, the robot customizes a charging progress notification message and provides it to the worker.

[1636] Anomaly detection and response:

[1637] 1. If the robot detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1638] 2. The server analyzes the abnormal condition and sends instructions to the robot to pause or adjust charging as necessary.

[1639] 3. The robot adjusts the charging current or pauses charging according to instructions from the server.

[1640] Charging complete:

[1641] 1. When the robot determines that the battery is fully charged, it stops charging and notifies the worker.

[1642] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[1643] Hardware and software used:

[1644] 1. Hardware:

[1645] Robot body: An industrial robot used in factories.

[1646] Emotion recognition sensors: camera and microphone.

[1647] Server: A centralized device that manages and analyzes data.

[1648] 2. Software:

[1649] Emotion recognition algorithm: Use emotion recognition APIs such as Azure Cognitive Services.

[1650] Charging control algorithm: Uses machine learning frameworks such as TensorFlow and PyTorch to analyze real-time data.

[1651] Examples:

[1652] In a factory, a robot uses an emotion recognition sensor to detect when a worker is stressed. After obtaining this information, the robot sends the data to a server, which analyzes it. Based on the analysis results, the robot sends an instruction to adjust the charging parameters so that "the worker is tired, so reduce the charging speed to ensure safety."

[1653] Example prompt sentence:

[1654] "Detecting stress levels from workers' facial expressions"

[1655] "Calculates optimal charging parameters when battery temperature rises"

[1656] "Analyzes emotional and charging data in real time to propose optimal charging plans"

[1657] These detailed program and prompt example statements demonstrate that the invention is possible.

[1658] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1659] Step 1:

[1660] The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed. The input is the latest algorithm data, and the output is instructions for updating the algorithm to the robot. Specifically, the server sends the latest algorithm, and the robot receives it and applies it.

[1661] Step 2:

[1662] The robot receives and applies updates, and measures and records the initial battery state (voltage and temperature) and the worker's emotional state. The input is the measurement data of the initial state, and the output is data transmission to the server. Specifically, the robot uses sensors to measure voltage and temperature and transmits that data to the server.

[1663] Step 3:

[1664] When a worker connects a robot to a charging station, the robot begins a charging session. The input is the robot's connection to the charging station, and the output is the start of charging. Specifically, the robot checks the connection and begins charging.

[1665] Step 4:

[1666] The robot sets the charging current and voltage based on the latest charging control algorithm received from the server. The input is the algorithm instruction from the server, and the output is the set charging parameters. Specifically, the robot receives instructions from the server and performs the operation of setting the charging parameters.

[1667] Step 5:

[1668] The robot measures the battery voltage and temperature at regular intervals and sends the data to the server. The input is the battery voltage and temperature data, and the output is the data sent to the server. Specifically, the robot measures data with sensors and sends it to the server.

[1669] Step 6:

[1670] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot. The input is voltage and temperature data from the robot, and the output is the calculation result of the optimal charging pattern. Specifically, the server analyzes the data, calculates the charging pattern, and sends instructions to the robot.

[1671] Step 7:

[1672] The robot operates an emotion engine to recognize the emotions of the worker in real time and sends the data to a server. The input is the worker's emotional state data, and the output is the transmission of emotion data to the server. Specifically, the robot uses an emotion recognition sensor to acquire data and send it to the server.

[1673] Step 8:

[1674] The server analyzes the received emotion data and adjusts the charging parameters based on the recognized emotion of the worker. The input is the emotion data from the robot, and the output is the result of adjusting the optimal charging parameters. Specifically, the server analyzes the emotion data, performs calculations to adjust the charging parameters, and sends instructions to the robot.

[1675] Step 9:

[1676] The robot customizes the charging progress notification message based on instructions from the server and provides it to the worker. The input is the notification instruction from the server, and the output is the customized notification message. Specifically, the robot receives the instruction from the server and performs an operation to display an appropriate message to the worker.

[1677] Step 10:

[1678] If the robot detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. The input is abnormal status data, and the output is a notification to the server. Specifically, the robot detects an abnormality and sends that information to the server.

[1679] Step 11:

[1680] The server analyzes the abnormal condition and sends instructions to the robot to suspend or adjust charging as necessary. The input is abnormal condition data from the robot, and the output is adjustment instructions. Specifically, the server analyzes the abnormal data, calculates safe charging parameters, and sends instructions to the robot.

[1681] Step 12:

[1682] The robot adjusts the charging current or temporarily suspends charging according to instructions from the server. The input is the adjustment instruction from the server, and the output is the executed charging parameter adjustment. Specifically, the robot adjusts the charging based on the instructions from the server.

[1683] Step 13:

[1684] When the robot determines that the battery is fully charged, it stops charging and notifies the worker. The input is battery charge status data, and the output is a notification that charging is complete. Specifically, the robot monitors the battery status, confirms that charging is complete, and notifies the worker.

[1685] Step 14:

[1686] The server stores the charging session data and emotion data to help improve the algorithm next time. The input is the charging session data and emotion data, and the output is the stored data. Specifically, the server stores the data and prepares it for use in improving the algorithm next time.

[1687] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1688] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1689] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1690] [Fourth embodiment]

[1691] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1692] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1693] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1694] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1695] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1696] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1697] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1698] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1699] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1700] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1701] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1702] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1703] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1704] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[1705] Program Overview

[1706] Initialization

[1707] 1. The server manages the latest version of the charging control algorithm and sends updates to the device as needed.

[1708] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature).

[1709] Start charging

[1710] 1. When a user connects a device to a charger, the device begins a charging session.

[1711] 2. The terminal sets the charging current and voltage based on the latest charging control algorithm received from the server.

[1712] Real-time monitoring and adjustment

[1713] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[1714] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[1715] 3. The device adjusts the charging current and voltage according to instructions from the server.

[1716] Anomaly detection and response

[1717] 1. When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server.

[1718] 2. The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging.

[1719] 3. The device adjusts or pauses the charging current according to instructions from the server.

[1720] Charging completed

[1721] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1722] 2. The server stores the charging session data and uses it to improve the charging control algorithm for the next time.

[1723] Specific examples

[1724] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[1725] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[1726] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[1727] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[1728] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[1729] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[1730] In this way, the present invention provides efficient and safe charging while maintaining good battery operating condition.

[1731] The processing flow will be explained below.

[1732] I understand. Below I will explain the program's processing in concrete steps.

[1733] Step 1:

[1734] The server prepares the latest version of the charging control algorithm and sends updates to the device as needed.

[1735] Step 2:

[1736] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[1737] Step 3:

[1738] The user connects the terminal to a charger and the terminal begins a charging session.

[1739] Step 4:

[1740] The terminal applies the latest charging control algorithm received from the server to set the initial charging current and voltage.

[1741] Step 5:

[1742] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[1743] Step 6:

[1744] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[1745] Step 7:

[1746] The terminal adjusts the charging current and voltage according to instructions from the server.

[1747] Step 8:

[1748] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1749] Step 9:

[1750] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[1751] Step 10:

[1752] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[1753] Step 11:

[1754] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1755] Step 12:

[1756] The server stores data from the charging session to help improve the charging control algorithm for the next time.

[1757] The above are the specific processing steps of the program.

[1758] Example 1

[1759] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1760] To ensure both safety and efficiency during battery charging, it is important to monitor battery voltage and temperature in real time, detect abnormalities early, and respond accordingly. However, conventional systems have not fully established means to efficiently solve these issues, making it difficult to dynamically adjust the optimal charging pattern. The present invention aims to solve these issues and achieve safe and efficient battery charging.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1762] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for managing the latest charging control algorithm and transmitting updates to the terminal, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for detecting the user's connection of the terminal to a charger at the start of charging and starting a charging session, and means for periodically measuring the battery status and analyzing the data. This makes it possible to monitor the battery status in real time, detect abnormalities early, and dynamically adjust the optimal charging pattern.

[1763] A "battery" is a device that stores electrical energy as chemical energy and is used to provide electrical current.

[1764] "Voltage" refers to the potential difference between two points in an electrical circuit, and is an important indicator for evaluating the charge and discharge state of a battery.

[1765] "Temperature" is an indicator of the thermal state of an object, and is a factor that has a significant impact on the safety and performance of a battery.

[1766] "Real-time" refers to a state in which data processing and information collection occur immediately, without delay.

[1767] "Monitoring" is the act of continuously checking the condition and changes of an object and detecting abnormalities early.

[1768] "Terminal" refers to an electronic device that is equipped with a battery and has the role of controlling charging and discharging.

[1769] A "charge control algorithm" is a set of calculations and logic for optimizing the charging pattern of a battery.

[1770] "Server" means a computer system that processes and manages data on a network, and manages and updates the charging control algorithm.

[1771] "User" refers to the person or end user who operates the device and charges the battery.

[1772] "Data" is a collection of information including measurement results such as voltage and temperature, and serves as the basic information for processing by servers and terminals.

[1773] "Abnormal" refers to a condition in which the battery voltage or temperature deviates from the preset normal values, and immediate action is required.

[1774] "Charging current" refers to the current supplied to the battery, which affects the charging rate and the health of the battery.

[1775] "Charging parameters" refer to the set values ​​and conditions of the charging process, such as current, voltage, and time.

[1776] "Specifications" refer to the technical requirements and conditions of the system or method provided by the present invention.

[1777] MODE FOR CARRYING OUT THE INVENTION

[1778] This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. This system consists of three main components: a server, a terminal, and a user.

[1779] System Program Overview

[1780] The system starts with a server that manages the latest charging control algorithms, which are then sent to the device as needed. The device uses these algorithms to measure battery voltage and temperature in real time and transmits the data to the server. The server then analyzes the data, calculates the optimal charging pattern, and sends instructions to the device. The user simply connects the device to the charger, and the charging process is automatically optimized.

[1781] Hardware and software used

[1782] 1. Server: Uses cloud services (e.g., AWS, Google Cloud) for computation and data management, and generative AI models (e.g., TensorFlow, PyTorch) for data analysis and optimization.

[1783] 2. Terminal: Uses a microcontroller (e.g., Arduino, Raspberry Pi) and a sensor device (e.g., voltage sensor, temperature sensor). Sends data to the server using a communication module (e.g., ESP8266).

[1784] 3. User: This represents a smartphone or other device that needs to be charged. The user connects the device to the charger.

[1785] Specific examples

[1786] For example, consider the following scenario where a user plugs a smartphone into a charger:

[1787] Initialization

[1788] 1. The server manages the latest charging control algorithm and sends updates to the device as needed. The server uses a cloud service to keep the algorithm up to date.

[1789] 2. The device receives and applies the update, and measures and records the initial battery status (voltage and temperature). For example, the initial voltage is 3.7V and the temperature is 25°C.

[1790] Start charging

[1791] 1. When a user connects a smartphone to a charger, the device sets an initial charging current of 1A and begins the charging session.

[1792] Real-time monitoring and adjustment

[1793] 1. The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. The data is sent in real time using the communication module.

[1794] 2. The server analyzes the received data, calculates the optimal charging current (e.g., 1.5A), and notifies the device. Generative AI models are used to analyze and optimize the data.

[1795] 3. The device adjusts the charging current to 1.5A according to instructions from the server.

[1796] Anomaly detection and response

[1797] 1. When the device detects that the battery temperature has risen to 30°C, it immediately notifies the server.

[1798] 2. The server analyzes this abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[1799] 3. The device follows the instruction and reduces the charging current to 0.5A.

[1800] Charging completed

[1801] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1802] 2. The server saves the data from this charging session and stores it in a database for use in improving the algorithm next time.

[1803] Prompt Sentence Examples

[1804] Below are some example prompts to input to a generative AI model:

[1805] Explain the battery management system of a smartphone, taking into account the following points:

[1806] 1. Roles of servers, terminals, and users.

[1807] 2. How to monitor battery voltage and temperature.

[1808] 3. How to update and apply the charging control algorithm.

[1809] 4. How to respond when an abnormality is detected.

[1810] 5. How to store and use data from charging sessions.

[1811] Thus, the present invention provides a comprehensive system for efficiently and safely managing the battery charging process.

[1812] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1813] System program processing flow

[1814] Step 1: Initialization

[1815] The server manages and regularly updates the latest charging control algorithms, which are used to calculate safe charging profiles for batteries and are optimized using AI models.

[1816] Input: Current charge control algorithm and historical data about the battery.

[1817] Data processing / calculation: Analyzes data using an AI model and optimizes the charging control algorithm.

[1818] Output: Generation of a new charge control algorithm.

[1819] What it does: The server uses TensorFlow to perform the analysis and saves the new algorithm to the cloud.

[1820] Step 2: Algorithm Updates

[1821] The server transmits the latest charging control algorithm to the terminal.

[1822] Input: A new charge control algorithm.

[1823] Data processing / calculation: Communication processing to send the algorithm to the terminal.

[1824] Output: The new algorithm is applied to the device.

[1825] Specific operation: The server sends the algorithm to the terminal using the HTTP protocol.

[1826] Step 3: Measuring the initial battery condition

[1827] The device measures the initial battery condition (voltage and temperature).

[1828] Input: Sensor data from the battery.

[1829] Data processing / calculation: Collect sensor data and record the initial state.

[1830] Output: Initial voltage and temperature data.

[1831] Specific operation: The terminal acquires data using the voltage sensor and temperature sensor, and measures an initial voltage of 3.7V and an initial temperature of 25°C.

[1832] Step 4: Detecting the start of charging

[1833] When a user connects a terminal to a charger, the terminal begins a charging session.

[1834] Input: Power is supplied to the device's charging port.

[1835] Data processing / calculation: Detects the signal to start charging and sets the initial charging current.

[1836] Output: Start of charging session.

[1837] Specific operation: The device detects power supply from the charger and sets the initial charging current to 1A.

[1838] Step 5: Real-time monitoring

[1839] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and sends the data to the server.

[1840] Input: Sensor data (voltage and temperature).

[1841] Data processing / computation: Sensor data collection and transmission processing.

[1842] Output: Latest voltage and temperature data sent to the server.

[1843] Specific operation: The device uses the Wi-Fi module to send data to the server every 5 seconds.

[1844] Step 6: Data analysis and adjustment instructions

[1845] The server analyzes the received data and calculates the optimal charging current and voltage.

[1846] Input: Battery voltage and temperature data sent from the device.

[1847] Data processing / calculation: Optimal charging patterns are calculated using AI models.

[1848] Output: Adjustment instructions to the terminal.

[1849] Specific operation: The server uses the generative AI model to determine that a charging current of 1.5A is optimal and sends that instruction to the device.

[1850] Step 7: Adjust charging parameters

[1851] The terminal adjusts the charging current and voltage based on instructions from the server.

[1852] Input: Adjustment instructions from the server.

[1853] Data processing / calculation: Set charging parameters according to instructions.

[1854] Output: Regulated charging current and voltage.

[1855] Specific operation: The device adjusts the charging current to 1.5A.

[1856] Step 8: Anomaly detection and response

[1857] If an abnormality occurs in the battery temperature or voltage, the device will immediately notify the server.

[1858] Input: Abnormal sensor data (e.g. temperature above 30°C).

[1859] Data processing / calculation: Filters out abnormal data and notifies the server.

[1860] Output: Error notification data to the server.

[1861] Specific operation: The device detects abnormal data and immediately sends the data to the server using the Wi-Fi module.

[1862] Step 9: Instructions for handling abnormal situations

[1863] The server analyzes the anomaly data and determines the appropriate response (e.g., reducing the charging current).

[1864] Input: Abnormality notification data from the terminal.

[1865] Data processing / calculation: Analyzing abnormal data and determining countermeasures.

[1866] Output: Instructions to the terminal.

[1867] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[1868] Step 10: Charging complete and notification

[1869] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1870] Input: Battery state-of-charge data.

[1871] Data processing / calculation: Determining when charging is complete and notifying the user.

[1872] Output: Charging completion notification.

[1873] Specific operation: The device detects when the battery is 100% charged, stops charging, and notifies the user via an LED display or other means.

[1874] Step 11: Save Data

[1875] The server stores all data about the charging session and uses it to improve the charging control algorithm for future charging sessions.

[1876] Input: Charging session data.

[1877] Data processing / computation: Data storage and analysis.

[1878] Output: Stored charging data.

[1879] What it does: The server stores the details of the charging session in a database and analyzes them to improve the algorithm next time.

[1880] In this way, at each step, the server, terminal, and user play their respective roles to efficiently and safely manage the battery charging process.

[1881] (Application example 1)

[1882] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1883] Current battery management systems for autonomous mobile machines have problems with both charging efficiency and safety. Furthermore, they lack the means to quickly detect and respond to abnormal battery conditions, which increases operating costs and threatens machine safety. Furthermore, insufficient optimization of charging patterns shortens battery life and results in increased maintenance frequency.

[1884] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1885] In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for storing charging session data and using it to improve the algorithm for the next charging session, and means for automating battery management for the autonomous mobile machine. This makes it possible to monitor the battery status in real time and dynamically adjust the optimal charging pattern. In addition, by quickly detecting abnormal conditions and adjusting the charging current, safety and efficiency are improved and operational costs are reduced.

[1886] A "battery" is a device that stores electrical energy as chemical energy and supplies it as electrical energy when needed.

[1887] "Voltage" is a physical quantity that indicates the electrical potential difference required to cause current to flow, and is expressed in volts (V).

[1888] "Temperature" is an indicator of the thermal energy state of a substance and is expressed in degrees Celsius (℃) or Fahrenheit (℉).

[1889] "Real-time monitoring means" refers to a device or system that instantly acquires data and instantly analyzes and displays the acquired data.

[1890] "Monitoring data" is data that collects monitored physical or environmental information, such as battery voltage or temperature.

[1891] A "server" is a computer system that provides various services and functions to client terminals via a network.

[1892] An "optimal charging pattern" is a charging process in which current and voltage are adjusted to achieve efficient charging of the battery.

[1893] A "terminal" is a computer device or machine that receives instructions from a server and performs a specific function based on those instructions.

[1894] "Charging parameters" refer to various control values ​​(e.g., current, voltage) that are set in the charging process.

[1895] A "charging session" is the entire process from when a battery is connected to a charger until it is fully charged.

[1896] An "algorithm" is a set of logical steps that represents a procedure or computational method for solving a particular problem.

[1897] An "autonomous mobile machine" is a machine that has the ability to make its own decisions and take action without requiring external instructions or control.

[1898] "Abnormal condition" refers to a situation where a phenomenon beyond the normal operating range (e.g., overheating, overvoltage) occurs.

[1899] "Operating costs" are all costs incurred to operate a system or machine (e.g., energy costs, labor costs).

[1900] The present invention relates to a system for efficiently and safely managing batteries for autonomous mobile machines in factories. This system monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. The configuration and operation of this system are described in detail below.

[1901] server

[1902] The server manages the latest version of the charging control algorithm and sends updates to the device as needed. It receives and analyzes battery voltage and temperature data sent in real time, calculates the optimal charging pattern, and sends instructions to the device. It also analyzes abnormal conditions and sends instructions to the device to take appropriate action. It also saves charging session data and uses it to improve the algorithm for the next time.

[1903] Terminal

[1904] The terminal receives instructions from the server and dynamically adjusts charging parameters (current and voltage) based on those instructions. It also measures the battery's voltage and temperature at regular intervals and sends the data to the server. If it detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server and adjusts or suspends the charging current according to the server's instructions.

[1905] User

[1906] Users initiate a charging session by connecting their device to a charger and are notified when the battery is fully charged so they can take appropriate action.

[1907] Specific examples

[1908] For example, consider an autonomous mobile machine used in a factory. When this machine arrives at a charging station, the system operates as follows:

[1909] 1. Initialization: The server sends the latest charging control algorithm to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C).

[1910] 2. Start charging: When the user connects the device to the charging station, charging begins. The terminal sets the initial charging current (e.g., 1A).

[1911] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature every 5 seconds and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[1912] 4. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device complies with this instruction.

[1913] 5. Charging complete: When the battery is determined to be fully charged, the device stops charging and notifies the user. The server stores data from this charging session and uses it to improve the algorithm next time.

[1914] Prompt Sentence Examples

[1915] "Design an app that intelligently manages the batteries of an autonomous mobile robot. Specifically, it monitors the battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern. If an abnormal temperature rise or voltage fluctuation is detected, it immediately adjusts the charging. Implement this in Python."

[1916] This invention is expected to improve the efficiency of battery management for autonomous mobile machines in factories, improve safety, and reduce operating costs. Specifically, it enables real-time battery monitoring, optimization of charging patterns, and rapid response to abnormal conditions, significantly improving overall operational efficiency.

[1917] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1918] Step 1:

[1919] The server maintains the latest version of the charging control algorithm and sends updates to the device as needed. Specifically, when the server receives an update request, it transfers the latest version of the algorithm to the device. At this time, the server properly packages the files required for the update and sends them in a format that the device can receive and extract.

[1920] Input: Update request from device

[1921] Output: Latest algorithm file

[1922] Step 2:

[1923] The device applies the latest algorithm file received from the server, measures the initial battery status (voltage and temperature), and sends it to the server. Specifically, the device measures the voltage and temperature using a sensor inside the device, formats the data, and sends it to the server. This measurement and data transmission is based on a certain protocol.

[1924] Input: Algorithm file from server

[1925] Output: Voltage and temperature data

[1926] Step 3:

[1927] When a user connects a device to a charger, the device starts a charging session. Specifically, when the device detects the charger, it generates a charging start signal and sets the initial charging current (e.g., 1A). At this point, the device sends its initial state data to the server.

[1928] Input: Connect to charger

[1929] Output: Charging start signal, initial charging current setting

[1930] Step 4:

[1931] The device measures the battery voltage and temperature every 5 seconds and sends the data to the server. Specifically, it acquires data from sensors within the device and sends it to the server in a fixed format. This data is updated in real time and at a fixed interval.

[1932] Input: Voltage and temperature data from the battery

[1933] Output: Measurement data sent to the server

[1934] Step 5:

[1935] The server calculates the optimal charging current based on the received data and sends the instructions to the device. Specifically, it uses an AI model to predict the optimal charging current for the current voltage and temperature, and notifies the device of the results. This calculation is performed in real time and is updated as data is received.

[1936] Input: Voltage and temperature data from the terminal

[1937] Output: Optimal charging current indication

[1938] Step 6:

[1939] The device adjusts the charging current according to instructions from the server. Specifically, the charging control system in the device changes the setting value and adjusts the charging current accordingly. This operation is designed to be performed quickly.

[1940] Input: Charging current instruction from the server

[1941] Output: Regulated charging current

[1942] Step 7:

[1943] When a device detects an abnormal temperature rise or voltage fluctuation, it immediately notifies the server, which then analyzes the abnormal condition and instructs the appropriate response. Specifically, the abnormality data is sent to the server, which then runs an algorithm to identify the cause of the abnormality and sends an appropriate response (such as reducing the charging current) to the device.

[1944] Input: Abnormal voltage and temperature data

[1945] Output: Abnormal notification to server, instruction of analysis results

[1946] Step 8:

[1947] The terminal adjusts the charging current and suspends charging if necessary based on instructions from the server regarding abnormal conditions. Specifically, the charging control system receives instructions from the server and promptly adjusts or suspends charging current according to the instructions.

[1948] Input: Error handling instructions from the server

[1949] Output: Regulated charging current, suspended charging

[1950] Step 9:

[1951] When the battery is determined to be fully charged, the device stops charging and notifies the user. Specifically, when the device meets certain voltage and temperature conditions, it generates a charging completion signal and notifies the user of this information by displaying or audibly notifying the user.

[1952] Input: Fully charged battery data

[1953] Output: Charging stop signal, notification to user

[1954] Step 10:

[1955] The server stores data from all charging sessions and uses it to improve the algorithm for the next time. Specifically, the received data is stored in a database and the analytical algorithm uses this data to optimize future charging templates.

[1956] Input: Charging session data

[1957] Output: Data stored in database, improved algorithm

[1958] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1959] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system is mainly composed of three elements: a server, a terminal, and a user. The specific operation of the system and program is described below.

[1960] Program Overview

[1961] Initialization

[1962] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the device as needed.

[1963] 2. The device receives and applies updates, and measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[1964] Start charging

[1965] 1. When a user connects a device to a charger, the device begins a charging session.

[1966] 2. The device sets the charging current and voltage based on the latest charging control algorithm and emotion recognition algorithm received from the server.

[1967] Real-time monitoring and adjustment

[1968] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server.

[1969] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device.

[1970] 3. The terminal adjusts the charging parameters based on instructions from the server.

[1971] Emotion recognition and response

[1972] 1. The device activates an emotion engine that recognizes the user's emotions in real time and sends the data to the server.

[1973] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[1974] 3. Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[1975] Anomaly detection and response

[1976] 1. If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[1977] 2. The server analyzes the abnormal condition and sends instructions to the terminal to pause or adjust charging as necessary.

[1978] 3. The device adjusts the charging current or temporarily suspends charging according to instructions from the server.

[1979] Charging completed

[1980] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user.

[1981] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[1982] Specific examples

[1983] For example, consider a scenario where a user plugs a smartphone into a charger. In this case, the system behaves as follows:

[1984] 1. Initialization: The server sends the latest charging control and emotion recognition algorithms to the device, and the device measures the initial battery state (e.g., voltage 3.7V, temperature 25°C) and the user's emotional state (e.g., relaxed state).

[1985] 2. Charging begins: When the user connects the smartphone to the charger, charging begins. The device sets the initial charging current (e.g., 1A).

[1986] 3. Real-time monitoring and adjustment: The device measures the voltage and temperature at regular intervals and sends the data to the server. The server calculates the optimal charging current (e.g., 1.5A) and notifies the device. The device then adjusts the charging current to 1.5A.

[1987] 4. Emotion Recognition and Response: If the device detects the user's stress state, it sends that information to the server. Based on this emotional state, the server customizes a charging progress notification message and sends it to the user (e.g., "Please relax. Charging is going well").

[1988] 5. Anomaly detection and response: The device detects that the battery temperature has risen to 30°C and notifies the server. The server analyzes this anomaly and sends an instruction to the device to reduce the charging current to 0.5A. The device follows this instruction.

[1989] 6. Charging complete: When the battery is fully charged, the device stops charging and notifies the user. The server stores the data from this charging session and the emotion data for future algorithm improvements.

[1990] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[1991] The processing flow will be explained below.

[1992] Step 1:

[1993] The server prepares the latest versions of the charging control algorithm and emotion recognition algorithm and sends updates to the device as needed.

[1994] Step 2:

[1995] The device applies the received update and initializes the sensors. It measures and records the initial battery status (voltage and temperature).

[1996] Step 3:

[1997] The user connects the terminal to a charger and the terminal begins a charging session.

[1998] Step 4:

[1999] The terminal applies the latest charging control algorithm and emotion recognition algorithm received from the server to set the initial charging current and voltage.

[2000] Step 5:

[2001] The device measures the battery voltage and temperature at regular intervals (e.g., every 5 seconds) and transmits the data to the server.

[2002] Step 6:

[2003] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to adjust the charging current and voltage to the terminal.

[2004] Step 7:

[2005] The terminal adjusts the charging current and voltage according to instructions from the server.

[2006] Step 8:

[2007] The device activates an emotion engine that recognizes the user's emotions in real time and transmits the data to a server.

[2008] Step 9:

[2009] The server analyzes the received emotion data and sends instructions to the terminal to adjust charging parameters based on the recognized user emotion.

[2010] Step 10:

[2011] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[2012] Step 11:

[2013] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[2014] Step 12:

[2015] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[2016] Step 13:

[2017] The terminal adjusts the charging current or temporarily suspends charging according to instructions from the server.

[2018] Step 14:

[2019] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[2020] Step 15:

[2021] The server stores charging session data and emotion data to help improve the algorithm next time.

[2022] The above are the specific processing steps of the program.

[2023] Example 2

[2024] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2025] Although conventional charging systems monitor battery status and provide optimal charging patterns, they are unable to take the user's emotional state into account, which results in a lack of user experience. Furthermore, the lack of automated anomaly detection and appropriate response leaves concerns about battery life and safety. These issues need to be addressed.

[2026] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2027] In this invention, the server includes means for monitoring the voltage and temperature of the battery in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting an instruction to the terminal, means for adjusting charging parameters based on the instruction from the server, means for recognizing a user's emotion, means for transmitting the recognized emotion data to the server, and means for adjusting charging parameters based on the user's emotion, thereby enabling the battery to be charged safely and efficiently while taking the user's emotional state into consideration.

[2028] "Means for monitoring battery voltage and temperature in real time" refers to sensors or modules that continuously measure the battery voltage and temperature, allowing the battery condition to be constantly monitored.

[2029] The "means for transmitting the monitoring data to the server" refers to a communication module or software that sends the voltage and temperature data obtained from the battery to the server via a network.

[2030] "Server means for calculating an optimum charging pattern and transmitting instructions to the terminal" refers to a server that analyzes the received data, calculates an optimum charging method, and transmits the instructions to the terminal.

[2031] "Means for adjusting charging parameters based on instructions from the server" refers to a component or software on the terminal side that automatically changes parameters such as charging current and voltage according to instructions provided by the server.

[2032] "Means for recognizing user emotions" refers to cameras and microphones that analyze the user's facial expressions, voice, and movements to identify emotions, as well as software that analyzes them.

[2033] The "means for transmitting the recognized emotion data to the server" refers to a communication module or software for sending the user's emotion data recognized on the terminal to the server via a network.

[2034] The "means for adjusting charging parameters based on the user's emotions" refers to a component or software on the terminal side that appropriately changes charging parameters depending on the user's emotional state.

[2035] MODE FOR CARRYING OUT THE INVENTION

[2036] This invention combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal, and a user. The specific operation of this system is explained below.

[2037] Initialization

[2038] 1. The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed. This is done using a management system on the server and network communication methods.

[2039] 2. After receiving and applying the update, the device measures and records the initial battery status (voltage and temperature) and the user's emotional state. The device is equipped with sensors and emotion recognition software. The sensors function as a voltmeter and thermometer, and the emotion recognition software detects the user's emotions via the camera and microphone.

[2040] Start charging

[2041] When a user connects a device to a charger, the device starts a charging session. The device sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server. This initial setting is performed using the device's internal charging control software.

[2042] Real-time monitoring and adjustment

[2043] 1. The device measures the battery voltage and temperature at regular intervals and sends the data to the server. This process utilizes the device's internal sensors and communication module.

[2044] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the device. The server contains an analytical algorithm and database that are used to determine the appropriate charging conditions.

[2045] 3. The device adjusts the charging parameters based on instructions from the server. This operation is performed by the charging control software within the device.

[2046] Emotion recognition and response

[2047] 1. The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to a server. Emotion recognition uses facial recognition and voice analysis technologies.

[2048] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion. The server is equipped with an emotion analysis algorithm.

[2049] 3. The device customizes the charging progress notification message based on instructions from the server and provides it to the user using the device's internal notification system.

[2050] Anomaly detection and response

[2051] 1. If the device detects an abnormal temperature rise or a sudden voltage fluctuation, it immediately notifies the server. This process uses sensors and communication modules.

[2052] 2. The server analyzes the abnormal condition and sends instructions to the device to pause or adjust charging as necessary. The server is equipped with an abnormality analysis algorithm.

[2053] 3. The device adjusts the charging current and pauses charging according to instructions from the server, which is also done by the charging control software.

[2054] Charging completed

[2055] 1. When the device determines that the battery is fully charged, it stops charging and notifies the user. The notification is done through the device's internal notification system.

[2056] 2. The server stores charging session data and emotion data to help improve the algorithm for the next time. A database system is used, which allows for long-term optimization.

[2057] Examples and prompts

[2058] For example, when a user connects a smartphone to a charger, the system operates as follows: When charging begins, the device receives the latest algorithm from the server and performs initial setup. It then monitors voltage and temperature in real time and performs emotion recognition. At the same time, it also detects anomalies to ensure safe charging. A specific example of a prompt is as follows:

[2059] "Please explain how your system works as a user charges their smartphone. Please provide detailed instructions, focusing especially on charging optimization and emotion recognition."

[2060] In this way, the present invention provides efficient and safe charging while taking into account the user's emotional state, while maintaining good battery operating condition.

[2061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2062] Step 1:

[2063] The server manages the latest charging control algorithms and emotion recognition algorithms and sends updates to the device as needed.

[2064] Input: The update management system on the server detects a new algorithm version.

[2065] Data processing: Packages the updates and prepares them for transmission.

[2066] Output: The update packet is sent to the device.

[2067] Specific operation: The server periodically scans the database and, if a new algorithm version is found, sends it to the device.

[2068] Step 2:

[2069] The terminal receives the updated algorithm and applies it to the system.

[2070] Input: The update packet sent by the server.

[2071] Data processing: Unpacking the update packet, analyzing the algorithm and applying it to the system.

[2072] Output: The system is updated with the new algorithm.

[2073] Specific operation: When the device receives the update packet, it unpacks it, installs the algorithm, and restarts the system.

[2074] Step 3:

[2075] The device measures and records the initial battery status (voltage and temperature) and the user's emotional state.

[2076] Input: Raw data from battery sensors, camera and microphone.

[2077] Data processing: Analyze raw data, quantify voltage and temperature, and classify emotional states.

[2078] Output: Battery voltage, temperature data and user emotion data.

[2079] How it works: The device measures the battery voltage to be 3.7V and the temperature to be 25°C, and the emotion recognition software classifies the user's emotional state as "relaxed."

[2080] Step 4:

[2081] When a user connects a terminal to a charger, the terminal begins a charging session.

[2082] Input: The user plugs the device into a charger.

[2083] Data processing: Recognize as a trigger to start charging.

[2084] Output: Start of charging session.

[2085] Specific behavior: When a user plugs a device into a charger, the system automatically initiates the charging protocol.

[2086] Step 5:

[2087] The terminal sets the appropriate charging current and voltage based on the latest charging control algorithm received from the server.

[2088] Input: Charging control algorithm from the server.

[2089] Data processing: Calculation of initial charging settings based on algorithms.

[2090] Output: Initial charging current and voltage settings.

[2091] Specific operation: The terminal sets the initial charging current to 1A and the voltage based on the received algorithm.

[2092] Step 6:

[2093] The terminal measures the battery voltage and temperature at regular intervals and transmits the data to the server.

[2094] Input: Voltage and temperature data from sensors.

[2095] Data processing: Collecting, organizing, and converting data into packets for transmission.

[2096] Output: Organized monitoring data.

[2097] Specific operation: The device measures the voltage and temperature every 5 seconds and sends the data to the server.

[2098] Step 7:

[2099] The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the terminal.

[2100] Input: Monitoring data sent from the device.

[2101] Data processing: Analyzing received data and optimizing charging patterns.

[2102] Output: Instruction data to the terminal.

[2103] Specific operation: The server compares past data with real-time data and sends an instruction to the device to increase the charging current to 1.5A.

[2104] Step 8:

[2105] The terminal adjusts the charging parameters based on instructions from the server.

[2106] Input: Instruction data from the server.

[2107] Data processing: Adjusting charging parameters based on instruction data.

[2108] Output: New charging parameter settings.

[2109] Specific operation: The device adjusts the charging current to 1.5A.

[2110] Step 9:

[2111] The device uses an emotion engine to recognize the user's emotions in real time and transmits the data to the server.

[2112] Input: Real-time data from camera and microphone.

[2113] Data processing: Data analysis using sentiment analysis algorithms.

[2114] Output: Emotional state data.

[2115] Specific operation: The device analyzes the user's facial expressions and voice, and if it determines that the user is in a stressful state, it sends that data to the server.

[2116] Step 10:

[2117] The server analyzes the received emotion data and adjusts charging parameters based on the recognized user emotion.

[2118] Input: User emotion data.

[2119] Data processing: Optimizing charging parameters based on emotion data.

[2120] Output: Instructions to adjust charging parameters to the device.

[2121] Specific operation: The server recognizes that the user is in a stressed state and sends instructions to customize the charging progress notification message.

[2122] Step 11:

[2123] Based on instructions from the server, the terminal customizes the charging progress notification message and provides it to the user.

[2124] Input: Customization instructions from the server.

[2125] Data processing: Customizing notification messages based on your instructions.

[2126] Output: A customized notification message.

[2127] What it does: The device displays a message like "Relax, charging is going well."

[2128] Step 12:

[2129] If the device detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[2130] Input: Anomaly data from sensors.

[2131] Data processing: Analyzes abnormal data and converts it into packets to be notified to the server.

[2132] Output: Anomaly notification packet.

[2133] Specific operation: The device detects a sudden rise in temperature (e.g., 30°C) and sends that information to the server.

[2134] Step 13:

[2135] The server analyzes the abnormal condition and sends instructions to the terminal to suspend or adjust charging as necessary.

[2136] Input: Abnormality notification data from the terminal.

[2137] Data processing: Analyze abnormal data and calculate appropriate countermeasures.

[2138] Output: Countermeasure instruction data to the terminal.

[2139] Specific operation: The server analyzes the abnormality and sends an instruction to the terminal to reduce the charging current to 0.5A.

[2140] Step 14:

[2141] The terminal adjusts the charging current and temporarily suspends charging according to instructions from the server.

[2142] Input: Countermeasure instruction data from the server.

[2143] Data processing: Adjust charging parameters based on instructions.

[2144] Output: New charging parameter settings.

[2145] Specific action: The device reduces the charging current to 0.5A.

[2146] Step 15:

[2147] When the device determines that the battery is fully charged, it stops charging and notifies the user.

[2148] Input: Battery monitoring data.

[2149] Data processing: Battery level analysis and charging stop decision.

[2150] Output: Charging completion notification.

[2151] What it does: The device determines that the battery has reached 100% and notifies you that your battery is fully charged.

[2152] Step 16:

[2153] The server stores charging session data and emotion data to help improve the algorithm next time.

[2154] Input: Charging session data and emotion data from the device.

[2155] Data processing: Received data is stored in a database.

[2156] Output: Saved charging session data.

[2157] Specific operation: The server stores all charging data and emotion data in a database and analyzes it the next time the algorithm is updated.

[2158] (Application example 2)

[2159] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2160] To improve the efficiency and safety of battery charging systems, it is important to manage battery voltage and temperature, and a system that monitors these in real time and dynamically adjusts the optimal charging pattern is required. In addition, because the user's emotional state can affect the battery charging process, a system that combines emotion recognition functionality is required to optimally manage the charging status and provide appropriate information to the user.

[2161] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring the battery voltage and temperature in real time, means for transmitting the monitoring data to the server, means for calculating an optimal charging pattern and transmitting instructions to the terminal, means for adjusting charging parameters based on instructions from the server, means for recognizing the user's emotion, means for transmitting the recognized emotion data to the server, and means for customizing a charging progress notification message based on the emotion data. This makes it possible to efficiently and safely charge the battery while maintaining good operating conditions and taking the user's emotional state into consideration.

[2162] 1. "Battery" means a device that stores electricity and whose operating conditions, such as voltage and temperature, can be monitored.

[2163] 2. "Voltage" refers to the electrical pressure generated within the battery, which directly affects its state of charge and performance.

[2164] 3. "Temperature" refers to the state of thermal energy in the battery and its surrounding environment, and plays an important role in charging efficiency and safety.

[2165] 4. "Real-time" refers to the ability to process and reflect information and data immediately, without delay.

[2166] 5. "Monitoring" refers to the act of continuously observing and recording certain physical or electronic parameters.

[2167] 6. "Server" means a computer system connected to a network that is a centralized device for data processing and management.

[2168] 7. "Terminal" refers to a computer device or sensor device that is directly operated by a user and that exchanges information in conjunction with a server.

[2169] 8. "Charging pattern" refers to the method of controlling current and voltage during the battery charging process.

[2170] 9. “Emotion recognition” is a technology for understanding and assessing a user’s emotional state, using sensors to analyze facial expressions and voice data.

[2171] 10. "Charging progress notification message" is a message that informs the user of information such as the battery charging status and estimated completion time.

[2172] 11. "Abnormal Condition" means a condition, such as battery voltage or temperature, that is outside of the normal operating range and may affect safety or performance.

[2173] 12. "Data retention" refers to the long-term storage of collected information for later analysis or reference.

[2174] This invention is aimed at factory robots, and combines a system that monitors battery voltage and temperature in real time and dynamically adjusts the optimal charging pattern with an emotion engine that recognizes the user's emotions. This system mainly consists of three elements: a server, a terminal (robot), and a user (worker).

[2175] Program Overview:

[2176] Initialization:

[2177] 1. The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed.

[2178] 2. The robot receives and applies updates, measures and records the initial battery condition (voltage and temperature) and the worker's emotional state.

[2179] Start charging:

[2180] 1. When a worker connects a robot to a charging station, the robot begins a charging session.

[2181] 2. The robot sets the charging current and voltage based on the latest charging control algorithm received from the server.

[2182] Real-time monitoring and adjustment:

[2183] 1. The robot measures the battery voltage and temperature at regular intervals and sends the data to the server.

[2184] 2. The server analyzes the received data, calculates the optimal charging pattern, and sends instructions to the robot.

[2185] 3. The robot adjusts the charging parameters based on instructions from the server.

[2186] Emotion recognition and response:

[2187] 1. The robot activates an emotion engine to recognize the worker's emotions in real time and sends the data to the server.

[2188] 2. The server analyzes the received emotion data and adjusts charging parameters based on the recognized emotion of the worker.

[2189] 3. Based on instructions from the server, the robot customizes a charging progress notification message and provides it to the worker.

[2190] Anomaly detection and response:

[2191] 1. If the robot detects an abnormal rise in temperature or a sudden voltage fluctuation, it immediately notifies the server.

[2192] 2. The server analyzes the abnormal condition and sends instructions to the robot to pause or adjust charging as necessary.

[2193] 3. The robot adjusts the charging current or pauses charging according to instructions from the server.

[2194] Charging complete:

[2195] 1. When the robot determines that the battery is fully charged, it stops charging and notifies the worker.

[2196] 2. The server stores the charging session data and emotion data to help improve the algorithm next time.

[2197] Hardware and software used:

[2198] 1. Hardware:

[2199] Robot body: An industrial robot used in factories.

[2200] Emotion recognition sensors: camera and microphone.

[2201] Server: A centralized device that manages and analyzes data.

[2202] 2. Software:

[2203] Emotion recognition algorithm: Use emotion recognition APIs such as Azure Cognitive Services.

[2204] Charging control algorithm: Uses machine learning frameworks such as TensorFlow and PyTorch to analyze real-time data.

[2205] Examples:

[2206] In a factory, a robot uses an emotion recognition sensor to detect when a worker is stressed. After obtaining this information, the robot sends the data to a server, which analyzes it. Based on the analysis results, the robot sends an instruction to adjust the charging parameters so that "the worker is tired, so reduce the charging speed to ensure safety."

[2207] Example prompt sentence:

[2208] "Detecting stress levels from workers' facial expressions"

[2209] "Calculates optimal charging parameters when battery temperature rises"

[2210] "Analyzes emotional and charging data in real time to propose optimal charging plans"

[2211] These detailed program and prompt example statements demonstrate that the invention is possible.

[2212] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2213] Step 1:

[2214] The server manages the latest versions of the charging control algorithm and emotion recognition algorithm, and sends updates to the robot as needed. The input is the latest algorithm data, and the output is instructions for updating the algorithm to the robot. Specifically, the server sends the latest algorithm, and the robot receives it and applies it.

[2215] Step 2:

[2216] The robot receives and applies updates, and measures and records the initial battery state (voltage and temperature) and the worker's emotional state. The input is the measurement data of the initial state, and the output is data transmission to the server. Specifically, the robot uses sensors to measure voltage and temperature and transmits that data to the server.

[2217] Step 3:

[2218] When a worker connects a robot to a charging station, the robot begins a charging session. The input is the robot's connection to the charging station, and the output is the start of charging. Specifically, the robot checks the connection and begins charging.

[2219] Step 4:

[2220] The robot sets the charging current and voltage based on the latest charging control algorithm received from the server. The input is the algorithm instruction from the server, and the output is the set charging parameters. Specifically, the robot receives instructions from the server and performs the operation of setting the charging parameters.

[2221] Step 5:

[2222] The robot measures the battery volt...

Claims

1. a means for monitoring the battery voltage and temperature in real time; means for transmitting the monitoring data to a server; A server means for calculating an optimal charging pattern and transmitting instructions to the terminal; means for adjusting charging parameters based on instructions from the server; A system including:

2. means for detecting an abnormal state and notifying the server; means for adjusting charging parameters based on the notified abnormal condition; The system of claim 1 further comprising:

3. 10. The system of claim 1, further comprising means for storing charging session data for subsequent algorithm improvements.

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