system

Abstract

The system according to this embodiment aims to optimize drone battery management and charging schedules, as well as optimize flight paths considering weather conditions and social media information. [Solution] The system according to the embodiment comprises a management unit, a scheduling unit, a charging unit, an optimization unit, and an analysis unit. The management unit manages the battery status of the drone. The scheduling unit creates a charging schedule based on the battery status managed by the management unit. The charging unit charges the drone based on the charging schedule created by the scheduling unit. The optimization unit analyzes weather data and optimizes the flight path. The analysis unit analyzes information from social media and avoids inappropriate routes.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, battery management of drones, optimization of charging schedules, and optimization of flight routes considering weather conditions and SNS information have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to perform battery management of drones, optimization of charging schedules, and optimization of flight routes considering weather conditions and SNS information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, a scheduling unit, a charging unit, an optimization unit, and an analysis unit. The management unit manages the battery status of the drone. The scheduling unit creates a charging schedule based on the battery status managed by the management unit. The charging unit charges the drone based on the charging schedule created by the scheduling unit. The optimization unit analyzes weather data and optimizes the flight path. The analysis unit analyzes information from social media and avoids inappropriate routes. [Effects of the Invention]

[0007] The system according to this embodiment can manage the drone's battery, optimize its charging schedule, and optimize its flight path considering weather conditions and social media information. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The drone network system according to an embodiment of the present invention is a system that solves the drone battery problem by effectively utilizing mobile phone base stations and constructs a nationwide drone network. The drone network system places drone ports within the premises of base stations deployed throughout the country. If a drone's battery runs low during flight, it will fly to its destination while charging via a drone port installed at a base station. At this time, it is necessary to manage the battery status and charging time of an unspecified number of drones and perform swarm control. Furthermore, a generative AI is incorporated to optimize the flight path by taking into account weather conditions and SNS information. The system manages the battery status of drones. If a drone's battery runs low during flight, it will land at a drone port installed at the nearest base station and charge. Once charging is complete, it will resume flight and fly to its destination. At this time, the generative AI monitors the drone's battery status in real time and creates an optimal charging schedule. The system optimizes the flight path in response to changes in weather conditions. The generative AI analyzes weather data and calculates the optimal flight path to avoid bad weather. For example, if bad weather such as strong winds or heavy rain is predicted, the generative AI will change the drone's flight path based on that information and select a safe route. The system avoids inappropriate routes based on SNS information. The generating AI analyzes information from social media and calculates flight paths that avoid inappropriate routes such as densely populated areas and disaster-stricken regions. For example, if disaster information is posted on social media, the generating AI will use that information to change the drone's flight path and select a safe route. It also controls the charging of multiple drones. The generating AI monitors the charging status of drone ports installed at base stations nationwide in real time and optimizes the charging schedule of multiple drones. This allows drones to charge efficiently and fly to their destinations. This solves the problem of drone battery issues and enables the construction of a nationwide drone network. For example, in drone delivery services, it can solve the problem of insufficient battery power when drones fly long distances, enabling efficient delivery. Furthermore, drones can be used for various purposes such as transporting supplies during disasters and delivering emergency medical supplies.This will enable the drone network system to solve the drone battery problem and build a nationwide drone network.

[0029] The drone network system according to this embodiment comprises a management unit, a scheduling unit, a charging unit, an optimization unit, and an analysis unit. The management unit manages the battery status of the drones. For example, the management unit monitors the remaining battery level of the drones and records the number of battery charge cycles. The management unit can also monitor the battery temperature and issue a warning if there is an abnormality. For example, if the drone's battery level drops, the management unit instructs it to land at a drone port installed at the nearest base station. The management unit can also recommend battery replacement if the number of battery charge cycles exceeds a certain number. Furthermore, if the battery temperature is abnormally high, the management unit can temporarily suspend the drone's flight and allow it to cool down. The scheduling unit creates a charging schedule based on the battery status managed by the management unit. For example, the scheduling unit determines the optimal charging timing by considering the drone's battery level and the number of charge cycles. The scheduling unit can also coordinate the charging schedules of multiple drones to achieve efficient charging. For example, if the drone's battery level drops, the scheduling unit instructs it to land at a drone port installed at the nearest base station and starts charging. The scheduling unit can also adjust charging schedules to minimize waiting times when multiple drones require charging simultaneously. Furthermore, the scheduling unit can set appropriate charging cycles to extend the drone's battery life. The charging unit charges the drones based on the charging schedule created by the scheduling unit. For example, the charging unit automatically starts charging when a drone lands at a drone port installed at a base station. The charging unit can also notify the drone when charging is complete and instruct it to resume flight. The charging unit can also monitor the battery temperature during charging and temporarily suspend charging if an abnormality is detected.Furthermore, the charging unit can check the drone's battery level when charging is complete and perform additional charging if necessary. The optimization unit analyzes weather data and calculates the optimal flight path to avoid bad weather. For example, the optimization unit collects weather data in real time and changes the drone's flight path if bad weather such as strong winds or heavy rain is predicted. The optimization unit can also adjust the drone's flight altitude and speed based on weather data. For example, the optimization unit collects weather data in real time and changes the drone's flight path to select a safe route if bad weather such as strong winds or heavy rain is predicted. The optimization unit can also optimize fuel consumption by adjusting the drone's flight altitude and speed based on weather data. Furthermore, the optimization unit can optimize the drone's flight time based on weather data to minimize battery consumption. The analysis unit analyzes information from social media and calculates a flight path to avoid inappropriate routes. For example, the analysis unit collects information from social media in real time and avoids inappropriate routes such as densely populated areas or disaster-prone areas. The analysis unit can also change the drone's flight path based on information from social media. For example, the analysis unit collects information from social media in real time and calculates a flight path to avoid inappropriate routes such as densely populated areas and disaster-stricken areas. The analysis unit can also change the drone's flight path based on the social media information and select a safe route. Furthermore, the analysis unit can optimize the drone's flight time based on the social media information and minimize battery consumption. As a result, the drone network system according to this embodiment can efficiently manage the drone's battery, charge it, optimize its flight path, and avoid inappropriate routes.

[0030] The management department manages the drone's battery status. For example, it monitors the drone's battery level and records the number of battery charge cycles. Specifically, if the drone's battery level falls below a certain threshold, the management department automatically issues a warning and instructs the drone to land at the nearest base station's drone port. Also, if the number of battery charge cycles exceeds a certain number, the management department issues a notification recommending battery replacement. Furthermore, the management department monitors the battery temperature in real time, and if an abnormal temperature rise is detected, it instructs the drone to temporarily suspend flight and allow cooling. This prevents accidents caused by battery overheating. The management department records this information in a central database, centrally managing the drone's operational history and battery health. In addition, the management department coordinates the drone's battery status with other systems and departments to support efficient operation. For example, the management department can adjust the drone's flight schedule based on the battery status and suggest optimal operating methods to extend battery life. This allows the management department to efficiently and effectively manage the drone's battery, improving the overall reliability and safety of the system.

[0031] The scheduling unit creates a charging schedule based on the battery status managed by the management unit. The scheduling unit determines the optimal charging timing, for example, by considering the drone's battery level and the number of charging cycles. Specifically, when a drone's battery level drops, the scheduling unit instructs it to land at the drone port located at the nearest base station and begins charging. The scheduling unit can also coordinate the charging schedules of multiple drones to achieve efficient charging. For example, if multiple drones require charging simultaneously, the scheduling unit optimizes the charging order to minimize waiting times. Furthermore, the scheduling unit can set appropriate charging cycles to extend the drone's battery life. This allows the scheduling unit to efficiently manage drone batteries and improve the overall operational efficiency of the system. The scheduling unit records this information in a central database, centrally managing the drone's operational history and charging status. Additionally, the scheduling unit collaborates with other systems and departments to support efficient operation. For example, the scheduling unit can adjust the drone's flight schedule based on its battery status and propose optimal operating methods to extend battery life. This allows the scheduling unit to manage the drone's battery efficiently and effectively, improving the overall reliability and safety of the system.

[0032] The charging unit charges the drone based on the charging schedule created by the scheduling unit. For example, the charging unit automatically starts charging when the drone lands at a drone port installed at the base station. Specifically, when the drone lands at the drone port, the charging unit verifies the connection and starts the charging process. The charging unit can also notify the drone when charging is complete and instruct it to resume flight. For example, the charging unit notifies the drone when charging is complete and instructs it to resume flight. The charging unit also monitors the battery temperature during charging and can temporarily suspend charging if abnormalities are detected. Furthermore, the charging unit checks the drone's battery level when charging is complete and can perform additional charging if necessary. This allows the charging unit to efficiently charge the drone's battery and improve the overall operational efficiency of the system. The charging unit records this information in a central database, centrally managing the drone's charging history and battery status. In addition, the charging unit collaborates with other systems and departments to support efficient operation. For example, the charging unit can adjust the drone's operating schedule based on the battery status and suggest the optimal operating method to extend battery life. This allows the charging unit to manage the drone's battery efficiently and effectively, improving the reliability and safety of the entire system.

[0033] The optimization unit analyzes weather data and calculates the optimal flight path to avoid adverse weather conditions. For example, the optimization unit collects weather data in real time and changes the drone's flight path if adverse weather conditions such as strong winds or heavy rain are predicted. Specifically, the optimization unit can also adjust the drone's flight altitude and speed based on weather data. For example, the optimization unit collects weather data in real time and changes the drone's flight path to select a safe route if adverse weather conditions such as strong winds or heavy rain are predicted. The optimization unit can also optimize fuel consumption by adjusting the drone's flight altitude and speed based on weather data. Furthermore, the optimization unit can optimize the drone's flight time based on weather data to minimize battery consumption. This allows the optimization unit to operate the drone efficiently and safely. The optimization unit records this information in a central database and centrally manages the drone's flight history and weather data. Furthermore, the optimization unit collaborates with other systems and departments to support efficient operation. For example, the optimization unit can adjust the drone's flight schedule based on weather data and propose the optimal operating method to extend battery life. This allows the optimization unit to operate the drone efficiently and effectively, improving the reliability and safety of the entire system.

[0034] The analysis unit analyzes social media information and calculates flight paths to avoid inappropriate routes. For example, the analysis unit collects social media information in real time and avoids inappropriate routes such as densely populated areas and disaster-stricken areas. Specifically, the analysis unit can change the drone's flight path based on social media information. For example, the analysis unit collects social media information in real time and calculates flight paths to avoid inappropriate routes such as densely populated areas and disaster-stricken areas. The analysis unit can also change the drone's flight path based on social media information and select a safe route. Furthermore, the analysis unit can optimize the drone's flight time and minimize battery consumption based on social media information. This allows the analysis unit to operate drones efficiently and safely. The analysis unit records this information in a central database and centrally manages drone operation history and social media information. Furthermore, the analysis unit collaborates with other systems and departments to support efficient operation. For example, the analysis unit can adjust the drone's operation schedule based on social media information and propose the optimal operation method to extend battery life. This allows the analysis unit to operate the drones efficiently and effectively, improving the reliability and safety of the entire system.

[0035] The control unit can monitor the drone's battery status in real time. For example, the control unit can monitor the drone's battery level in real time and record the number of battery charge cycles. The control unit can also monitor the battery temperature in real time and issue a warning if there is an abnormality. For example, if the drone's battery level drops, the control unit can instruct it to land at a drone port installed at the nearest base station. The control unit can also recommend battery replacement if the number of battery charge cycles exceeds a certain number. Furthermore, if the battery temperature is abnormally high, the control unit can temporarily suspend the drone's flight and allow it to cool down. This allows for charging at the appropriate time by monitoring the drone's battery status in real time. Real-time monitoring is performed based on criteria such as the frequency of monitoring updates and the delay time. Some or all of the above processes in the control unit may be performed using AI, or not. For example, the control unit can input the drone's battery level data into a generating AI and have the generating AI perform real-time monitoring.

[0036] The scheduling unit can create an optimal charging schedule based on the battery status managed by the management unit. For example, the scheduling unit determines the optimal charging timing by considering the drone's battery level and the number of charging cycles. The scheduling unit can also coordinate the charging schedules of multiple drones to achieve efficient charging. For example, if a drone's battery level is low, the scheduling unit can instruct it to land at the drone port installed at the nearest base station and begin charging. The scheduling unit can also coordinate the charging schedules to minimize waiting times when multiple drones require charging simultaneously. Furthermore, the scheduling unit can set appropriate charging cycles to extend the drone's battery life. This enables efficient drone operation by creating an optimal charging schedule. The optimal charging schedule is created based on criteria such as extending battery life and maximizing charging efficiency. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input drone battery status data into a generating AI and have the generating AI create an optimal charging schedule.

[0037] The charging unit can charge the drone based on a charging schedule created by the scheduling unit. For example, the charging unit automatically starts charging when the drone lands at a drone port installed at a base station. The charging unit can also notify the drone when charging is complete and instruct it to resume flight. For example, the charging unit automatically starts charging when the drone lands at a drone port installed at a base station, and notifies the drone when charging is complete and instructs it to resume flight. The charging unit can also monitor the battery temperature during charging and temporarily suspend charging if there is an abnormality. Furthermore, the charging unit can check the drone's battery level when charging is complete and perform additional charging if necessary. This enables efficient charging by charging the drone based on a charging schedule. Some or all of the above processes in the charging unit may be performed using AI, for example, or not using AI. For example, the charging unit can input the drone's charging schedule data into a generating AI and leave the execution of charging to the generating AI.

[0038] The optimization unit can analyze weather data and calculate the optimal flight path to avoid adverse weather conditions. For example, the optimization unit can collect weather data in real time and change the drone's flight path if adverse weather conditions such as strong winds or heavy rain are predicted. The optimization unit can also adjust the drone's flight altitude and speed based on the weather data. For example, the optimization unit can collect weather data in real time and change the drone's flight path to select a safe route if adverse weather conditions such as strong winds or heavy rain are predicted. The optimization unit can also optimize fuel consumption by adjusting the drone's flight altitude and speed based on the weather data. Furthermore, the optimization unit can optimize the drone's flight time based on the weather data to minimize battery consumption. In this way, by analyzing weather data, the optimal flight path to avoid adverse weather conditions can be provided. The optimal flight path is calculated based on criteria such as reducing flight time and ensuring safety. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input weather data into the generative AI and have the generative AI perform the calculation of the optimal flight path.

[0039] The analysis unit can analyze SNS information and calculate a flight path that avoids inappropriate routes such as densely populated areas and disaster-prone areas. For example, the analysis unit can collect SNS information in real time and avoid inappropriate routes such as densely populated areas and disaster-prone areas. The analysis unit can also change the drone's flight path based on SNS information. For example, the analysis unit can collect SNS information in real time and calculate a flight path that avoids inappropriate routes such as densely populated areas and disaster-prone areas. The analysis unit can also change the drone's flight path based on SNS information and select a safe route. Furthermore, the analysis unit can optimize the drone's flight time and minimize battery consumption based on SNS information. In this way, by analyzing SNS information, it is possible to provide an optimal flight path that avoids inappropriate routes. Inappropriate routes are determined based on criteria such as densely populated areas, disaster-prone areas, and no-fly zones. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input SNS information into the generation AI and have the generation AI perform the avoidance of inappropriate routes.

[0040] The management department can analyze the drone's past battery usage history and select the optimal monitoring method. For example, if the drone has frequently run out of battery in the past, the management department can apply a more rigorous monitoring method. Conversely, if the drone has shown stable battery usage in the past, the management department can apply a normal monitoring method. Furthermore, the management department can analyze battery consumption patterns under specific conditions from the drone's past battery usage history and select an appropriate monitoring method. In this way, the optimal monitoring method can be selected by analyzing past battery usage history. The optimal monitoring method is selected based on criteria such as monitoring frequency and monitoring items. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the drone's past battery usage history data into a generating AI and have the generating AI select the optimal monitoring method.

[0041] The management unit can filter battery status data based on the drone's current flight status and mission objectives. For example, the management unit can intensify battery status monitoring when the drone is performing a critical mission. Alternatively, it can monitor battery status at a normal frequency when the drone is on a regular patrol flight. Furthermore, if the drone is responding to an emergency, the management unit can minimize battery status monitoring and allow it to focus on flight. This allows for appropriate monitoring through filtering based on flight status and mission objectives. Filtering is performed based on criteria such as the type of flight status or the importance of the mission objectives. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input drone flight status data into a generating AI and have the generating AI perform the filtering.

[0042] The management unit can prioritize monitoring relevant drones by considering their geographical location when monitoring battery status. For example, if a drone is in a densely populated area, the management unit can intensify battery status monitoring. Alternatively, if a drone is in a remote location, the management unit can monitor battery status at the normal frequency. Furthermore, if a drone is in a hazardous area, the management unit can minimize battery status monitoring and focus on flight. This allows for priority monitoring of relevant drones by considering geographical location. Geographical location information is obtained based on criteria such as GPS data or map information. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input the geographical location information of drones into a generating AI and have the generating AI perform priority monitoring of relevant drones.

[0043] The management department can analyze the drone's social media activity and obtain relevant battery information while monitoring battery status. For example, if the drone is frequently active on social media, the management department can intensify battery status monitoring. Conversely, if the drone is inactive on social media, the management department can monitor battery status at the normal frequency. Furthermore, the management department can analyze battery consumption patterns under specific conditions from the drone's social media activity and select an appropriate monitoring method. This allows for the acquisition of relevant battery information by analyzing social media activity. Social media activity is analyzed based on criteria such as post content and follower count. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input the drone's social media activity data into a generating AI and have the generating AI acquire relevant battery information.

[0044] The scheduling unit can adjust the level of detail in the charging schedule based on the importance of the battery when creating the schedule. For example, the scheduling unit can provide a detailed charging schedule to a drone performing an important mission. It can also provide a standard charging schedule to a drone performing a normal patrol flight. Furthermore, the scheduling unit can quickly display a charging schedule to a drone responding to an emergency, allowing charging to begin immediately. This allows for the provision of an appropriate charging schedule by adjusting the level of detail based on the importance of the battery. The importance of the battery is evaluated based on criteria such as battery level and mission importance. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the drone's battery information into a generating AI and have the generating AI perform the adjustment of the level of detail in the schedule.

[0045] The scheduling unit can apply different scheduling algorithms depending on the drone category when creating a charging schedule. For example, the scheduling unit can provide an efficient charging schedule for delivery drones. It can also provide a standard charging schedule for patrol drones. Furthermore, the scheduling unit can quickly display a charging schedule for emergency response drones, allowing them to start charging immediately. This allows for the provision of efficient charging schedules by applying different scheduling algorithms depending on the drone category. Drone categories are classified based on criteria such as application or size. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input drone category information into a generating AI and have the generating AI apply the scheduling algorithm.

[0046] The scheduling unit can determine the priority of charging schedules based on the battery submission date when creating a charging schedule. For example, the scheduling unit can provide priority charging schedules to batteries submitted early. It can also provide a normal charging schedule to batteries submitted at the normal time. Furthermore, the scheduling unit can quickly display a charging schedule to batteries submitted urgently, allowing charging to begin immediately. This allows for the provision of appropriate charging schedules by determining the priority of schedules based on the battery submission date. The battery submission date is determined based on criteria such as the start date of use or the replacement date. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input battery submission date data into a generating AI and have the generating AI perform the determination of schedule priorities.

[0047] The scheduling unit can adjust the order of charging schedules based on battery relevance when creating them. For example, the scheduling unit can prioritize charging schedules for drones performing critical missions. It can also provide a standard charging schedule for drones performing normal patrol flights. Furthermore, the scheduling unit can quickly display a charging schedule for drones responding to emergencies, allowing them to start charging immediately. This allows for the provision of efficient charging schedules by adjusting the order of schedules based on battery relevance. Battery relevance is evaluated based on criteria such as being on the same mission or the same model. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input battery relevance data into a generating AI and have the generating AI perform the adjustment of the schedule order.

[0048] The charging unit can analyze the drone's past charging history to select the optimal charging method during charging. For example, if the drone previously required rapid charging, the charging unit will apply a similar charging method. Alternatively, if the drone has previously demonstrated stable charging, the charging unit can apply a standard charging method. Furthermore, the charging unit can select the optimal charging method under specific conditions based on the drone's past charging history. This allows for the selection of the optimal charging method by analyzing past charging history. The optimal charging method is selected based on criteria such as charging speed and battery life extension. Some or all of the above-described processes in the charging unit may be performed using AI or not. For example, the charging unit can input the drone's past charging history data into a generating AI and have the generating AI select the optimal charging method.

[0049] The charging unit can customize the charging method based on the drone's current flight status during charging. For example, if the drone is performing an important mission, the charging unit can select a method to complete charging quickly. It can also apply a standard charging method if the drone is performing a normal patrol flight. Furthermore, if the drone is responding to an emergency, the charging unit can select a method to complete charging in the shortest possible time. This allows for efficient charging by customizing the charging method based on the current flight status. The charging method is customized based on criteria such as fast charging or wireless charging. Some or all of the above processing in the charging unit may be performed using AI or not. For example, the charging unit can input drone flight status data into a generating AI and have the generating AI perform the customization of the charging method.

[0050] The charging unit can select the optimal charging method by considering the drone's geographical location information during charging. For example, if the drone is in a densely populated area, the charging unit can select a method that completes charging quickly. The charging unit can also apply a standard charging method if the drone is in a remote location. Furthermore, if the drone is in a hazardous area, the charging unit can select a method that completes charging in the shortest possible time. This allows for the selection of the optimal charging method by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or map information. Some or all of the above processing in the charging unit may be performed using AI, or not. For example, the charging unit can input the drone's geographical location information into a generating AI and have the generating AI select the optimal charging method.

[0051] The charging unit can analyze the drone's social media activity during charging and propose a charging method. For example, if the drone is frequently active on social media, the charging unit can propose a method to complete charging quickly. If the drone is inactive on social media, the charging unit can also propose a standard charging method. Furthermore, the charging unit can propose the optimal charging method under specific conditions based on the drone's social media activity. This allows for the proposal of the optimal charging method by analyzing social media activity. Social media activity is analyzed based on criteria such as post content and follower count. Some or all of the above processing in the charging unit may be performed using AI or not. For example, the charging unit can input the drone's social media activity data into a generating AI and have the generating AI propose a charging method.

[0052] The optimization unit can predict the optimal route by referring to past weather data during route optimization. For example, the optimization unit can predict the optimal route for a specific season or time of day from past weather data. The optimization unit can also predict routes to avoid bad weather based on past weather data. Furthermore, the optimization unit can analyze past weather data and predict the optimal route under specific weather conditions. In this way, the optimal route can be predicted by referring to past weather data. Past weather data is obtained based on criteria such as past wind speed, precipitation, and temperature. Some or all of the above processing in the optimization unit is performed using a generation AI. For example, the optimization unit can input past weather data into the generation AI and have the generation AI perform the prediction of the optimal route.

[0053] The optimization unit can apply different optimization algorithms to each drone category during route optimization. For example, the optimization unit can apply an efficient route optimization algorithm to delivery drones. It can also apply a standard route optimization algorithm to patrol drones. Furthermore, it can apply a route optimization algorithm that allows emergency response drones to reach their destination quickly. This enables efficient route optimization by applying different optimization algorithms to each drone category. Drone categories are classified based on criteria such as application or size. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input drone category information into the generative AI and have the generative AI execute the application of the optimization algorithm.

[0054] The optimization unit can analyze changes in optimization based on the drone's flight timing during route optimization. For example, if the drone flies during a specific season, the optimization unit can analyze the optimal route for that season. It can also analyze the optimal route for a specific time of day if the drone flies during that time. Furthermore, the optimization unit can analyze the optimal route by referring to past data based on the drone's flight timing. This allows for the provision of more appropriate routes by analyzing changes in optimization based on flight timing. Flight timing is determined based on criteria such as season and time of day. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input drone flight timing data into the generative AI and have the generative AI perform the analysis of changes in optimization.

[0055] The optimization unit can analyze the optimization process by referring to relevant market data for drones during route optimization. For example, if a drone is used in the delivery market, the optimization unit can analyze the optimal route by referring to that market data. It can also analyze the optimal route if the drone is used in the patrol market, and if the drone is used in the emergency response market. This allows the optimization unit to provide the optimal route by referring to relevant market data. Relevant market data is acquired based on criteria such as demand forecasting data and competitor information. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input relevant market data for drones into the generative AI and have the generative AI perform the optimization analysis.

[0056] The analysis unit can select the optimal analysis method by referring to past SNS data during analysis. For example, the analysis unit can select the optimal analysis method under specific conditions from past SNS data. The analysis unit can also select a method to complete the analysis quickly based on past SNS data. Furthermore, the analysis unit can analyze past SNS data and select the optimal analysis method under specific conditions. This allows the optimal analysis method to be selected by referring to past SNS data. Past SNS data is acquired based on criteria such as past post content and follower count. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input past SNS data into the generation AI and have the generation AI select the optimal analysis method.

[0057] The analysis unit can apply different analysis algorithms to each drone category during analysis. For example, it can apply an efficient analysis algorithm to delivery drones. It can also apply a standard analysis algorithm to patrol drones. Furthermore, it can apply an algorithm that completes the analysis quickly to emergency response drones. This allows for efficient analysis by applying different analysis algorithms to each drone category. Drone categories are classified based on criteria such as application or size. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input drone category information into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0058] The analysis unit can analyze changes in the analysis based on the timing of SNS posts during the analysis process. For example, the analysis unit can analyze changes in the analysis during specific time periods based on the timing of SNS posts. The analysis unit can also select a method to complete the analysis quickly based on the timing of SNS posts. Furthermore, the analysis unit can analyze the timing of SNS posts and select the optimal analysis method under specific conditions. This enables more appropriate analysis by analyzing changes in the analysis based on the timing of SNS posts. The timing of SNS posts is determined based on criteria such as posting frequency and time of day. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input SNS posting timing data into the generation AI and have the generation AI perform the analysis of changes in the analysis.

[0059] The analysis unit can perform analysis by referring to relevant market data for social networking services (SNS). For example, the analysis unit can refer to relevant market data for SNS and select the optimal analysis method under specific conditions. The analysis unit can also select a method to complete the analysis quickly based on the relevant market data for SNS. Furthermore, the analysis unit can analyze the relevant market data for SNS and select the optimal analysis method under specific conditions. This enables optimal analysis by referring to relevant market data. The relevant market data is acquired based on criteria such as demand forecast data and competitor information. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input relevant market data for SNS into the generating AI and leave the execution of the analysis to the generating AI.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] To further improve energy efficiency, drone network systems can also be equipped with drone ports featuring solar panels. For example, solar panels can be installed on the roof of a drone port to generate electricity using sunlight during the day. This generated electricity can then be stored in batteries and used to charge drones at night or on cloudy days. Furthermore, the installation of solar panels can reduce the operating costs of drone ports and lessen the environmental impact. In this way, drone network systems can achieve sustainable energy use and improve energy efficiency.

[0062] The drone network system can also incorporate a drone battery replacement function. For example, an automatic battery replacement device can be installed at the drone port to automatically replace the battery when the drone lands. The replaced battery can then be charged at the drone port, ready for the next use. Furthermore, the battery replacement device can monitor the battery status in real time and automatically discard degraded batteries. This extends the drone's flight time and prevents problems caused by battery degradation.

[0063] The drone network system can also incorporate wireless charging technology to supply energy to drones during flight. For example, wireless charging stations can be installed around drone ports to supply energy to drones while they are in flight. These wireless charging stations can also monitor the drones' battery levels in real time and supply energy as needed. Furthermore, by utilizing wireless charging technology, drone flight time can be extended and the frequency of battery replacements reduced. This allows the drone network system to achieve more efficient energy supply and reduce drone operating costs.

[0064] Drone network systems can also build mesh networks for real-time communication during drone flight. For example, drones can communicate with each other, sharing flight routes and battery status. Furthermore, using a mesh network allows drones to maintain stable communication even when they are far from a base station. The mesh network can also share information to help drones avoid obstacles during flight, ensuring safer operation. This allows drone network systems to provide more advanced communication capabilities and improve the operational efficiency of drones.

[0065] The drone network system can also be equipped with sensors to collect environmental data in real time while the drones are in flight. For example, temperature and humidity sensors can be mounted on the drones to collect environmental data during flight. The collected data can then be analyzed by data analysis equipment installed at the drone port and used for weather forecasting and environmental monitoring. Furthermore, the drone's flight route can be optimized based on the environmental data, enabling more efficient flight. In this way, the drone network system can provide more advanced services through the collection and analysis of environmental data.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The management team manages the drone's battery status. Specifically, they monitor the battery level, record the number of charge cycles, and monitor the battery temperature, issuing warnings if any abnormalities are detected. If the battery level drops, they instruct the drone to land at the nearest base station's drone port, and if the number of charge cycles exceeds a certain number, they recommend replacing the battery. Furthermore, if the battery temperature is abnormally high, they temporarily suspend the drone's flight and allow it to cool down. Step 2: The scheduling unit creates a charging schedule based on the battery status managed by the management unit. Specifically, it determines the optimal charging timing considering the remaining battery level and the number of charging cycles, and adjusts the charging schedules for multiple drones. It also makes adjustments to minimize waiting time for charging and sets appropriate charging cycles to extend battery life. Step 3: The charging unit charges the drone based on the charging schedule created by the scheduling unit. Specifically, it automatically starts charging when the drone lands at the drone port installed at the base station, and notifies the drone when charging is complete, instructing it to resume flight. It can also monitor the battery temperature during charging and temporarily suspend charging if there is an abnormality. Furthermore, it can check the battery level when charging is complete and perform additional charging if necessary. Step 4: The optimization unit analyzes weather data and calculates the optimal flight path to avoid adverse weather conditions. Specifically, it collects weather data in real time and changes the drone's flight path if adverse weather conditions such as strong winds or heavy rain are predicted. It can also adjust flight altitude and speed based on weather data to optimize fuel efficiency. Furthermore, it can optimize flight time based on weather data to minimize battery consumption. Step 5: The analysis unit analyzes SNS information and calculates a flight path to avoid inappropriate routes. Specifically, it collects SNS information in real time and avoids inappropriate routes such as densely populated areas and disaster-prone areas. It can also change the flight path based on SNS information to select a safe route. Furthermore, it can optimize flight time based on SNS information to minimize battery consumption.

[0068] (Example of form 2) The drone network system according to an embodiment of the present invention is a system that solves the drone battery problem by effectively utilizing mobile phone base stations and constructs a nationwide drone network. The drone network system places drone ports within the premises of base stations deployed throughout the country. If a drone's battery runs low during flight, it will fly to its destination while charging via a drone port installed at a base station. At this time, it is necessary to manage the battery status and charging time of an unspecified number of drones and perform swarm control. Furthermore, a generative AI is incorporated to optimize the flight path by taking into account weather conditions and SNS information. The system manages the battery status of drones. If a drone's battery runs low during flight, it will land at a drone port installed at the nearest base station and charge. Once charging is complete, it will resume flight and fly to its destination. At this time, the generative AI monitors the drone's battery status in real time and creates an optimal charging schedule. The system optimizes the flight path in response to changes in weather conditions. The generative AI analyzes weather data and calculates the optimal flight path to avoid bad weather. For example, if bad weather such as strong winds or heavy rain is predicted, the generative AI will change the drone's flight path based on that information and select a safe route. The system avoids inappropriate routes based on SNS information. The generating AI analyzes information from social media and calculates flight paths that avoid inappropriate routes such as densely populated areas and disaster-stricken regions. For example, if disaster information is posted on social media, the generating AI will use that information to change the drone's flight path and select a safe route. It also controls the charging of multiple drones. The generating AI monitors the charging status of drone ports installed at base stations nationwide in real time and optimizes the charging schedule of multiple drones. This allows drones to charge efficiently and fly to their destinations. This solves the problem of drone battery issues and enables the construction of a nationwide drone network. For example, in drone delivery services, it can solve the problem of insufficient battery power when drones fly long distances, enabling efficient delivery. Furthermore, drones can be used for various purposes such as transporting supplies during disasters and delivering emergency medical supplies.This will enable the drone network system to solve the drone battery problem and build a nationwide drone network.

[0069] The drone network system according to this embodiment comprises a management unit, a scheduling unit, a charging unit, an optimization unit, and an analysis unit. The management unit manages the battery status of the drones. For example, the management unit monitors the remaining battery level of the drones and records the number of battery charge cycles. The management unit can also monitor the battery temperature and issue a warning if there is an abnormality. For example, if the drone's battery level drops, the management unit instructs it to land at a drone port installed at the nearest base station. The management unit can also recommend battery replacement if the number of battery charge cycles exceeds a certain number. Furthermore, if the battery temperature is abnormally high, the management unit can temporarily suspend the drone's flight and allow it to cool down. The scheduling unit creates a charging schedule based on the battery status managed by the management unit. For example, the scheduling unit determines the optimal charging timing by considering the drone's battery level and the number of charge cycles. The scheduling unit can also coordinate the charging schedules of multiple drones to achieve efficient charging. For example, if the drone's battery level drops, the scheduling unit instructs it to land at a drone port installed at the nearest base station and starts charging. The scheduling unit can also adjust charging schedules to minimize waiting times when multiple drones require charging simultaneously. Furthermore, the scheduling unit can set appropriate charging cycles to extend the drone's battery life. The charging unit charges the drones based on the charging schedule created by the scheduling unit. For example, the charging unit automatically starts charging when a drone lands at a drone port installed at a base station. The charging unit can also notify the drone when charging is complete and instruct it to resume flight. The charging unit can also monitor the battery temperature during charging and temporarily suspend charging if an abnormality is detected.Furthermore, the charging unit can check the drone's battery level when charging is complete and perform additional charging if necessary. The optimization unit analyzes weather data and calculates the optimal flight path to avoid bad weather. For example, the optimization unit collects weather data in real time and changes the drone's flight path if bad weather such as strong winds or heavy rain is predicted. The optimization unit can also adjust the drone's flight altitude and speed based on weather data. For example, the optimization unit collects weather data in real time and changes the drone's flight path to select a safe route if bad weather such as strong winds or heavy rain is predicted. The optimization unit can also optimize fuel consumption by adjusting the drone's flight altitude and speed based on weather data. Furthermore, the optimization unit can optimize the drone's flight time based on weather data to minimize battery consumption. The analysis unit analyzes information from social media and calculates a flight path to avoid inappropriate routes. For example, the analysis unit collects information from social media in real time and avoids inappropriate routes such as densely populated areas or disaster-prone areas. The analysis unit can also change the drone's flight path based on information from social media. For example, the analysis unit collects information from social media in real time and calculates a flight path to avoid inappropriate routes such as densely populated areas and disaster-stricken areas. The analysis unit can also change the drone's flight path based on the social media information and select a safe route. Furthermore, the analysis unit can optimize the drone's flight time based on the social media information and minimize battery consumption. As a result, the drone network system according to this embodiment can efficiently manage the drone's battery, charge it, optimize its flight path, and avoid inappropriate routes.

[0070] The management department manages the drone's battery status. For example, it monitors the drone's battery level and records the number of battery charge cycles. Specifically, if the drone's battery level falls below a certain threshold, the management department automatically issues a warning and instructs the drone to land at the nearest base station's drone port. Also, if the number of battery charge cycles exceeds a certain number, the management department issues a notification recommending battery replacement. Furthermore, the management department monitors the battery temperature in real time, and if an abnormal temperature rise is detected, it instructs the drone to temporarily suspend flight and allow cooling. This prevents accidents caused by battery overheating. The management department records this information in a central database, centrally managing the drone's operational history and battery health. In addition, the management department coordinates the drone's battery status with other systems and departments to support efficient operation. For example, the management department can adjust the drone's flight schedule based on the battery status and suggest optimal operating methods to extend battery life. This allows the management department to efficiently and effectively manage the drone's battery, improving the overall reliability and safety of the system.

[0071] The scheduling unit creates a charging schedule based on the battery status managed by the management unit. The scheduling unit determines the optimal charging timing, for example, by considering the drone's battery level and the number of charging cycles. Specifically, when a drone's battery level drops, the scheduling unit instructs it to land at the drone port located at the nearest base station and begins charging. The scheduling unit can also coordinate the charging schedules of multiple drones to achieve efficient charging. For example, if multiple drones require charging simultaneously, the scheduling unit optimizes the charging order to minimize waiting times. Furthermore, the scheduling unit can set appropriate charging cycles to extend the drone's battery life. This allows the scheduling unit to efficiently manage drone batteries and improve the overall operational efficiency of the system. The scheduling unit records this information in a central database, centrally managing the drone's operational history and charging status. Additionally, the scheduling unit collaborates with other systems and departments to support efficient operation. For example, the scheduling unit can adjust the drone's flight schedule based on its battery status and propose optimal operating methods to extend battery life. This allows the scheduling unit to manage the drone's battery efficiently and effectively, improving the overall reliability and safety of the system.

[0072] The charging unit charges the drone based on the charging schedule created by the scheduling unit. For example, the charging unit automatically starts charging when the drone lands at a drone port installed at the base station. Specifically, when the drone lands at the drone port, the charging unit verifies the connection and starts the charging process. The charging unit can also notify the drone when charging is complete and instruct it to resume flight. For example, the charging unit notifies the drone when charging is complete and instructs it to resume flight. The charging unit also monitors the battery temperature during charging and can temporarily suspend charging if abnormalities are detected. Furthermore, the charging unit checks the drone's battery level when charging is complete and can perform additional charging if necessary. This allows the charging unit to efficiently charge the drone's battery and improve the overall operational efficiency of the system. The charging unit records this information in a central database, centrally managing the drone's charging history and battery status. In addition, the charging unit collaborates with other systems and departments to support efficient operation. For example, the charging unit can adjust the drone's operating schedule based on the battery status and suggest the optimal operating method to extend battery life. This allows the charging unit to manage the drone's battery efficiently and effectively, improving the reliability and safety of the entire system.

[0073] The optimization unit analyzes weather data and calculates the optimal flight path to avoid adverse weather conditions. For example, the optimization unit collects weather data in real time and changes the drone's flight path if adverse weather conditions such as strong winds or heavy rain are predicted. Specifically, the optimization unit can also adjust the drone's flight altitude and speed based on weather data. For example, the optimization unit collects weather data in real time and changes the drone's flight path to select a safe route if adverse weather conditions such as strong winds or heavy rain are predicted. The optimization unit can also optimize fuel consumption by adjusting the drone's flight altitude and speed based on weather data. Furthermore, the optimization unit can optimize the drone's flight time based on weather data to minimize battery consumption. This allows the optimization unit to operate the drone efficiently and safely. The optimization unit records this information in a central database and centrally manages the drone's flight history and weather data. Furthermore, the optimization unit collaborates with other systems and departments to support efficient operation. For example, the optimization unit can adjust the drone's flight schedule based on weather data and propose the optimal operating method to extend battery life. This allows the optimization unit to operate the drone efficiently and effectively, improving the reliability and safety of the entire system.

[0074] The analysis unit analyzes social media information and calculates flight paths to avoid inappropriate routes. For example, the analysis unit collects social media information in real time and avoids inappropriate routes such as densely populated areas and disaster-stricken areas. Specifically, the analysis unit can change the drone's flight path based on social media information. For example, the analysis unit collects social media information in real time and calculates flight paths to avoid inappropriate routes such as densely populated areas and disaster-stricken areas. The analysis unit can also change the drone's flight path based on social media information and select a safe route. Furthermore, the analysis unit can optimize the drone's flight time and minimize battery consumption based on social media information. This allows the analysis unit to operate drones efficiently and safely. The analysis unit records this information in a central database and centrally manages drone operation history and social media information. Furthermore, the analysis unit collaborates with other systems and departments to support efficient operation. For example, the analysis unit can adjust the drone's operation schedule based on social media information and propose the optimal operation method to extend battery life. This allows the analysis unit to operate the drones efficiently and effectively, improving the reliability and safety of the entire system.

[0075] The control unit can monitor the drone's battery status in real time. For example, the control unit can monitor the drone's battery level in real time and record the number of battery charge cycles. The control unit can also monitor the battery temperature in real time and issue a warning if there is an abnormality. For example, if the drone's battery level drops, the control unit can instruct it to land at a drone port installed at the nearest base station. The control unit can also recommend battery replacement if the number of battery charge cycles exceeds a certain number. Furthermore, if the battery temperature is abnormally high, the control unit can temporarily suspend the drone's flight and allow it to cool down. This allows for charging at the appropriate time by monitoring the drone's battery status in real time. Real-time monitoring is performed based on criteria such as the frequency of monitoring updates and the delay time. Some or all of the above processes in the control unit may be performed using AI, or not. For example, the control unit can input the drone's battery level data into a generating AI and have the generating AI perform real-time monitoring.

[0076] The scheduling unit can create an optimal charging schedule based on the battery status managed by the management unit. For example, the scheduling unit determines the optimal charging timing by considering the drone's battery level and the number of charging cycles. The scheduling unit can also coordinate the charging schedules of multiple drones to achieve efficient charging. For example, if a drone's battery level is low, the scheduling unit can instruct it to land at the drone port installed at the nearest base station and begin charging. The scheduling unit can also coordinate the charging schedules to minimize waiting times when multiple drones require charging simultaneously. Furthermore, the scheduling unit can set appropriate charging cycles to extend the drone's battery life. This enables efficient drone operation by creating an optimal charging schedule. The optimal charging schedule is created based on criteria such as extending battery life and maximizing charging efficiency. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input drone battery status data into a generating AI and have the generating AI create an optimal charging schedule.

[0077] The charging unit can charge the drone based on a charging schedule created by the scheduling unit. For example, the charging unit automatically starts charging when the drone lands at a drone port installed at a base station. The charging unit can also notify the drone when charging is complete and instruct it to resume flight. For example, the charging unit automatically starts charging when the drone lands at a drone port installed at a base station, and notifies the drone when charging is complete and instructs it to resume flight. The charging unit can also monitor the battery temperature during charging and temporarily suspend charging if there is an abnormality. Furthermore, the charging unit can check the drone's battery level when charging is complete and perform additional charging if necessary. This enables efficient charging by charging the drone based on a charging schedule. Some or all of the above processes in the charging unit may be performed using AI, for example, or not using AI. For example, the charging unit can input the drone's charging schedule data into a generating AI and leave the execution of charging to the generating AI.

[0078] The optimization unit can analyze weather data and calculate the optimal flight path to avoid adverse weather conditions. For example, the optimization unit can collect weather data in real time and change the drone's flight path if adverse weather conditions such as strong winds or heavy rain are predicted. The optimization unit can also adjust the drone's flight altitude and speed based on the weather data. For example, the optimization unit can collect weather data in real time and change the drone's flight path to select a safe route if adverse weather conditions such as strong winds or heavy rain are predicted. The optimization unit can also optimize fuel consumption by adjusting the drone's flight altitude and speed based on the weather data. Furthermore, the optimization unit can optimize the drone's flight time based on the weather data to minimize battery consumption. In this way, by analyzing weather data, the optimal flight path to avoid adverse weather conditions can be provided. The optimal flight path is calculated based on criteria such as reducing flight time and ensuring safety. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input weather data into the generative AI and have the generative AI perform the calculation of the optimal flight path.

[0079] The analysis unit can analyze SNS information and calculate a flight path that avoids inappropriate routes such as densely populated areas and disaster-prone areas. For example, the analysis unit can collect SNS information in real time and avoid inappropriate routes such as densely populated areas and disaster-prone areas. The analysis unit can also change the drone's flight path based on SNS information. For example, the analysis unit can collect SNS information in real time and calculate a flight path that avoids inappropriate routes such as densely populated areas and disaster-prone areas. The analysis unit can also change the drone's flight path based on SNS information and select a safe route. Furthermore, the analysis unit can optimize the drone's flight time and minimize battery consumption based on SNS information. In this way, by analyzing SNS information, it is possible to provide an optimal flight path that avoids inappropriate routes. Inappropriate routes are determined based on criteria such as densely populated areas, disaster-prone areas, and no-fly zones. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input SNS information into the generation AI and have the generation AI perform the avoidance of inappropriate routes.

[0080] The control unit can estimate the drone's emotions and adjust the frequency of battery status monitoring based on the estimated emotions. For example, if the drone is stressed, the control unit will monitor the battery status more frequently and immediately notify of any abnormalities. If the drone is relaxed, the control unit can monitor the battery status at a normal frequency and periodically check the status. Furthermore, if the drone is in a hurry, the control unit can minimize battery status monitoring and allow it to concentrate on flight. This allows for more appropriate monitoring by adjusting the frequency of battery status monitoring based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0081] The management department can analyze the drone's past battery usage history and select the optimal monitoring method. For example, if the drone has frequently run out of battery in the past, the management department can apply a more rigorous monitoring method. Conversely, if the drone has shown stable battery usage in the past, the management department can apply a normal monitoring method. Furthermore, the management department can analyze battery consumption patterns under specific conditions from the drone's past battery usage history and select an appropriate monitoring method. In this way, the optimal monitoring method can be selected by analyzing past battery usage history. The optimal monitoring method is selected based on criteria such as monitoring frequency and monitoring items. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the drone's past battery usage history data into a generating AI and have the generating AI select the optimal monitoring method.

[0082] The management unit can filter battery status data based on the drone's current flight status and mission objectives. For example, the management unit can intensify battery status monitoring when the drone is performing a critical mission. Alternatively, it can monitor battery status at a normal frequency when the drone is on a regular patrol flight. Furthermore, if the drone is responding to an emergency, the management unit can minimize battery status monitoring and allow it to focus on flight. This allows for appropriate monitoring through filtering based on flight status and mission objectives. Filtering is performed based on criteria such as the type of flight status or the importance of the mission objectives. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input drone flight status data into a generating AI and have the generating AI perform the filtering.

[0083] The control unit can estimate the drone's emotions and prioritize battery status based on the estimated emotions. For example, if the drone is stressed, the control unit can set the battery status priority higher and respond immediately. If the drone is relaxed, the control unit can also set the battery status priority to the normal setting. Furthermore, if the drone is in a hurry, the control unit can set the battery status priority lower to allow it to concentrate on flight. This allows for a more appropriate response by prioritizing battery status based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0084] The management unit can prioritize monitoring relevant drones by considering their geographical location when monitoring battery status. For example, if a drone is in a densely populated area, the management unit can intensify battery status monitoring. Alternatively, if a drone is in a remote location, the management unit can monitor battery status at the normal frequency. Furthermore, if a drone is in a hazardous area, the management unit can minimize battery status monitoring and focus on flight. This allows for priority monitoring of relevant drones by considering geographical location. Geographical location information is obtained based on criteria such as GPS data or map information. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input the geographical location information of drones into a generating AI and have the generating AI perform priority monitoring of relevant drones.

[0085] The management department can analyze the drone's social media activity and obtain relevant battery information while monitoring battery status. For example, if the drone is frequently active on social media, the management department can intensify battery status monitoring. Conversely, if the drone is inactive on social media, the management department can monitor battery status at the normal frequency. Furthermore, the management department can analyze battery consumption patterns under specific conditions from the drone's social media activity and select an appropriate monitoring method. This allows for the acquisition of relevant battery information by analyzing social media activity. Social media activity is analyzed based on criteria such as post content and follower count. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input the drone's social media activity data into a generating AI and have the generating AI acquire relevant battery information.

[0086] The scheduling unit can estimate the drone's emotions and adjust how the charging schedule is presented based on the estimated emotions. For example, if the drone is stressed, the scheduling unit can provide a simple and easy-to-understand charging schedule. If the drone is relaxed, the scheduling unit can also provide a detailed charging schedule and suggest customizable options. Furthermore, if the drone is in a hurry, the scheduling unit can quickly display the charging schedule and allow charging to begin immediately. This allows for the provision of a more appropriate charging schedule by adjusting how the charging schedule is presented based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0087] The scheduling unit can adjust the level of detail in the charging schedule based on the importance of the battery when creating the schedule. For example, the scheduling unit can provide a detailed charging schedule to a drone performing an important mission. It can also provide a standard charging schedule to a drone performing a normal patrol flight. Furthermore, the scheduling unit can quickly display a charging schedule to a drone responding to an emergency, allowing charging to begin immediately. This allows for the provision of an appropriate charging schedule by adjusting the level of detail based on the importance of the battery. The importance of the battery is evaluated based on criteria such as battery level and mission importance. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the drone's battery information into a generating AI and have the generating AI perform the adjustment of the level of detail in the schedule.

[0088] The scheduling unit can apply different scheduling algorithms depending on the drone category when creating a charging schedule. For example, the scheduling unit can provide an efficient charging schedule for delivery drones. It can also provide a standard charging schedule for patrol drones. Furthermore, the scheduling unit can quickly display a charging schedule for emergency response drones, allowing them to start charging immediately. This allows for the provision of efficient charging schedules by applying different scheduling algorithms depending on the drone category. Drone categories are classified based on criteria such as application or size. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input drone category information into a generating AI and have the generating AI apply the scheduling algorithm.

[0089] The scheduling unit can estimate the drone's emotions and adjust the length of the charging schedule based on the estimated emotions. For example, if the drone is stressed, the scheduling unit can provide a short charging schedule to complete charging quickly. It can also provide a normal charging schedule if the drone is relaxed. Furthermore, if the drone is in a hurry, the scheduling unit can provide the shortest possible charging schedule to start charging immediately. This allows for a more appropriate charging schedule by adjusting the length of the charging schedule based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0090] The scheduling unit can determine the priority of charging schedules based on the battery submission date when creating a charging schedule. For example, the scheduling unit can provide priority charging schedules to batteries submitted early. It can also provide a normal charging schedule to batteries submitted at the normal time. Furthermore, the scheduling unit can quickly display a charging schedule to batteries submitted urgently, allowing charging to begin immediately. This allows for the provision of appropriate charging schedules by determining the priority of schedules based on the battery submission date. The battery submission date is determined based on criteria such as the start date of use or the replacement date. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input battery submission date data into a generating AI and have the generating AI perform the determination of schedule priorities.

[0091] The scheduling unit can adjust the order of charging schedules based on battery relevance when creating them. For example, the scheduling unit can prioritize charging schedules for drones performing critical missions. It can also provide a standard charging schedule for drones performing normal patrol flights. Furthermore, the scheduling unit can quickly display a charging schedule for drones responding to emergencies, allowing them to start charging immediately. This allows for the provision of efficient charging schedules by adjusting the order of schedules based on battery relevance. Battery relevance is evaluated based on criteria such as being on the same mission or the same model. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input battery relevance data into a generating AI and have the generating AI perform the adjustment of the schedule order.

[0092] The charging unit can estimate the drone's emotions and adjust the charging method based on the estimated emotions. For example, if the drone is stressed, the charging unit will select a method to complete charging quickly. If the drone is relaxed, the charging unit can also apply a normal charging method. Furthermore, if the drone is in a hurry, the charging unit can select a method to complete charging in the shortest possible time. This allows for more appropriate charging by adjusting the charging method based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the charging unit may be performed using AI or not. For example, the charging unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0093] The charging unit can analyze the drone's past charging history to select the optimal charging method during charging. For example, if the drone previously required rapid charging, the charging unit will apply a similar charging method. Alternatively, if the drone has previously demonstrated stable charging, the charging unit can apply a standard charging method. Furthermore, the charging unit can select the optimal charging method under specific conditions based on the drone's past charging history. This allows for the selection of the optimal charging method by analyzing past charging history. The optimal charging method is selected based on criteria such as charging speed and battery life extension. Some or all of the above-described processes in the charging unit may be performed using AI or not. For example, the charging unit can input the drone's past charging history data into a generating AI and have the generating AI select the optimal charging method.

[0094] The charging unit can customize the charging method based on the drone's current flight status during charging. For example, if the drone is performing an important mission, the charging unit can select a method to complete charging quickly. It can also apply a standard charging method if the drone is performing a normal patrol flight. Furthermore, if the drone is responding to an emergency, the charging unit can select a method to complete charging in the shortest possible time. This allows for efficient charging by customizing the charging method based on the current flight status. The charging method is customized based on criteria such as fast charging or wireless charging. Some or all of the above processing in the charging unit may be performed using AI or not. For example, the charging unit can input drone flight status data into a generating AI and have the generating AI perform the customization of the charging method.

[0095] The charging unit can estimate the drone's emotions and determine charging priorities based on the estimated emotions. For example, if the drone is stressed, the charging unit can set a higher charging priority and respond immediately. The charging unit can also return the charging priority to the normal setting if the drone is relaxed. Furthermore, if the drone is in a hurry, the charging unit can set a lower charging priority to allow it to concentrate on flight. This allows for a more appropriate response by determining charging priorities based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the charging unit may be performed using AI or not. For example, the charging unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0096] The charging unit can select the optimal charging method by considering the drone's geographical location information during charging. For example, if the drone is in a densely populated area, the charging unit can select a method that completes charging quickly. The charging unit can also apply a standard charging method if the drone is in a remote location. Furthermore, if the drone is in a hazardous area, the charging unit can select a method that completes charging in the shortest possible time. This allows for the selection of the optimal charging method by considering geographical location information. Geographical location information is obtained based on criteria such as GPS data or map information. Some or all of the above processing in the charging unit may be performed using AI, or not. For example, the charging unit can input the drone's geographical location information into a generating AI and have the generating AI select the optimal charging method.

[0097] The charging unit can analyze the drone's social media activity during charging and propose a charging method. For example, if the drone is frequently active on social media, the charging unit can propose a method to complete charging quickly. If the drone is inactive on social media, the charging unit can also propose a standard charging method. Furthermore, the charging unit can propose the optimal charging method under specific conditions based on the drone's social media activity. This allows for the proposal of the optimal charging method by analyzing social media activity. Social media activity is analyzed based on criteria such as post content and follower count. Some or all of the above processing in the charging unit may be performed using AI or not. For example, the charging unit can input the drone's social media activity data into a generating AI and have the generating AI propose a charging method.

[0098] The optimization unit can estimate the drone's emotions and adjust the route optimization method based on the estimated emotions. For example, if the drone is stressed, the optimization unit will optimize the route by prioritizing the shortest route. If the drone is relaxed, the optimization unit can also apply the normal route optimization method. Furthermore, if the drone is in a hurry, the optimization unit can optimize the route to reach the destination in the shortest time. This allows for the provision of a more appropriate route by adjusting the route optimization method based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the drone's sensor information into a generative AI and have the generative AI perform emotion estimation.

[0099] The optimization unit can predict the optimal route by referring to past weather data during route optimization. For example, the optimization unit can predict the optimal route for a specific season or time of day from past weather data. The optimization unit can also predict routes to avoid bad weather based on past weather data. Furthermore, the optimization unit can analyze past weather data and predict the optimal route under specific weather conditions. In this way, the optimal route can be predicted by referring to past weather data. Past weather data is obtained based on criteria such as past wind speed, precipitation, and temperature. Some or all of the above processing in the optimization unit is performed using a generation AI. For example, the optimization unit can input past weather data into the generation AI and have the generation AI perform the prediction of the optimal route.

[0100] The optimization unit can apply different optimization algorithms to each drone category during route optimization. For example, the optimization unit can apply an efficient route optimization algorithm to delivery drones. It can also apply a standard route optimization algorithm to patrol drones. Furthermore, it can apply a route optimization algorithm that allows emergency response drones to reach their destination quickly. This enables efficient route optimization by applying different optimization algorithms to each drone category. Drone categories are classified based on criteria such as application or size. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input drone category information into the generative AI and have the generative AI execute the application of the optimization algorithm.

[0101] The optimization unit can estimate the drone's emotions and determine the priority of route optimization based on the estimated emotions. For example, if the drone is stressed, the optimization unit can set a higher priority for route optimization and respond immediately. The optimization unit can also set the priority of route optimization to a normal setting if the drone is relaxed. Furthermore, if the drone is in a hurry, the optimization unit can set a lower priority for route optimization to allow it to concentrate on flight. This allows for a more appropriate response by determining the priority of route optimization based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the drone's sensor information into the generative AI and have the generative AI perform emotion estimation.

[0102] The optimization unit can analyze changes in optimization based on the drone's flight timing during route optimization. For example, if the drone flies during a specific season, the optimization unit can analyze the optimal route for that season. It can also analyze the optimal route for a specific time of day if the drone flies during that time. Furthermore, the optimization unit can analyze the optimal route by referring to past data based on the drone's flight timing. This allows for the provision of more appropriate routes by analyzing changes in optimization based on flight timing. Flight timing is determined based on criteria such as season and time of day. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input drone flight timing data into the generative AI and have the generative AI perform the analysis of changes in optimization.

[0103] The optimization unit can analyze the optimization process by referring to relevant market data for drones during route optimization. For example, if a drone is used in the delivery market, the optimization unit can analyze the optimal route by referring to that market data. It can also analyze the optimal route if the drone is used in the patrol market, and if the drone is used in the emergency response market. This allows the optimization unit to provide the optimal route by referring to relevant market data. Relevant market data is acquired based on criteria such as demand forecasting data and competitor information. Some or all of the above processing in the optimization unit is performed using a generative AI. For example, the optimization unit can input relevant market data for drones into the generative AI and have the generative AI perform the optimization analysis.

[0104] The analysis unit can estimate the drone's emotions and adjust the analysis method based on the estimated emotions. For example, if the drone is stressed, the analysis unit can select a method to complete the analysis quickly. If the drone is relaxed, the analysis unit can also apply the normal analysis method. Furthermore, if the drone is in a hurry, the analysis unit can select a method to complete the analysis in the shortest possible time. This allows for more appropriate analysis by adjusting the analysis method based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and movement patterns. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the drone's sensor information into the generative AI and have the generative AI perform emotion estimation.

[0105] The analysis unit can select the optimal analysis method by referring to past SNS data during analysis. For example, the analysis unit can select the optimal analysis method under specific conditions from past SNS data. The analysis unit can also select a method to complete the analysis quickly based on past SNS data. Furthermore, the analysis unit can analyze past SNS data and select the optimal analysis method under specific conditions. This allows the optimal analysis method to be selected by referring to past SNS data. Past SNS data is acquired based on criteria such as past post content and follower count. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input past SNS data into the generation AI and have the generation AI select the optimal analysis method.

[0106] The analysis unit can apply different analysis algorithms to each drone category during analysis. For example, it can apply an efficient analysis algorithm to delivery drones. It can also apply a standard analysis algorithm to patrol drones. Furthermore, it can apply an algorithm that completes the analysis quickly to emergency response drones. This allows for efficient analysis by applying different analysis algorithms to each drone category. Drone categories are classified based on criteria such as application or size. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input drone category information into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0107] The analysis unit can estimate the drone's emotions and determine the priority of analysis based on the estimated emotions. For example, if the drone is stressed, the analysis unit can set a high priority and respond immediately. The analysis unit can also return to the normal priority if the drone is relaxed. Furthermore, if the drone is in a hurry, the analysis unit can set a low priority to allow it to concentrate on flight. This allows for a more appropriate response by determining the priority of analysis based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the drone's sensor information into the generative AI and have the generative AI perform emotion estimation.

[0108] The analysis unit can analyze changes in the analysis based on the timing of SNS posts during the analysis process. For example, the analysis unit can analyze changes in the analysis during specific time periods based on the timing of SNS posts. The analysis unit can also select a method to complete the analysis quickly based on the timing of SNS posts. Furthermore, the analysis unit can analyze the timing of SNS posts and select the optimal analysis method under specific conditions. This enables more appropriate analysis by analyzing changes in the analysis based on the timing of SNS posts. The timing of SNS posts is determined based on criteria such as posting frequency and time of day. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input SNS posting timing data into the generation AI and have the generation AI perform the analysis of changes in the analysis.

[0109] The analysis unit can perform analysis by referring to relevant market data for social networking services (SNS). For example, the analysis unit can refer to relevant market data for SNS and select the optimal analysis method under specific conditions. The analysis unit can also select a method to complete the analysis quickly based on the relevant market data for SNS. Furthermore, the analysis unit can analyze the relevant market data for SNS and select the optimal analysis method under specific conditions. This enables optimal analysis by referring to relevant market data. The relevant market data is acquired based on criteria such as demand forecast data and competitor information. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input relevant market data for SNS into the generating AI and leave the execution of the analysis to the generating AI.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] To further improve energy efficiency, drone network systems can also be equipped with drone ports featuring solar panels. For example, solar panels can be installed on the roof of a drone port to generate electricity using sunlight during the day. This generated electricity can then be stored in batteries and used to charge drones at night or on cloudy days. Furthermore, the installation of solar panels can reduce the operating costs of drone ports and lessen the environmental impact. In this way, drone network systems can achieve sustainable energy use and improve energy efficiency.

[0112] The drone network system can also incorporate a drone battery replacement function. For example, an automatic battery replacement device can be installed at the drone port to automatically replace the battery when the drone lands. The replaced battery can then be charged at the drone port, ready for the next use. Furthermore, the battery replacement device can monitor the battery status in real time and automatically discard degraded batteries. This extends the drone's flight time and prevents problems caused by battery degradation.

[0113] The drone network system can also incorporate wireless charging technology to supply energy to drones during flight. For example, wireless charging stations can be installed around drone ports to supply energy to drones while they are in flight. These wireless charging stations can also monitor the drones' battery levels in real time and supply energy as needed. Furthermore, by utilizing wireless charging technology, drone flight time can be extended and the frequency of battery replacements reduced. This allows the drone network system to achieve more efficient energy supply and reduce drone operating costs.

[0114] Drone network systems can also build mesh networks for real-time communication during drone flight. For example, drones can communicate with each other, sharing flight routes and battery status. Furthermore, using a mesh network allows drones to maintain stable communication even when they are far from a base station. The mesh network can also share information to help drones avoid obstacles during flight, ensuring safer operation. This allows drone network systems to provide more advanced communication capabilities and improve the operational efficiency of drones.

[0115] The drone network system can also be equipped with sensors to collect environmental data in real time while the drones are in flight. For example, temperature and humidity sensors can be mounted on the drones to collect environmental data during flight. The collected data can then be analyzed by data analysis equipment installed at the drone port and used for weather forecasting and environmental monitoring. Furthermore, the drone's flight route can be optimized based on the environmental data, enabling more efficient flight. In this way, the drone network system can provide more advanced services through the collection and analysis of environmental data.

[0116] The drone network system can also estimate the drone's emotions and adjust its flight path based on those emotions. For example, if a drone is stressed, it can change its flight path and choose a safer route. If the drone is relaxed, it can maintain its normal flight path. Furthermore, if the drone is in a hurry, it can choose the shortest route to reach its destination quickly. This allows for more appropriate flight by adjusting the flight path based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns.

[0117] The drone network system can also estimate the drone's emotions and adjust the charging schedule based on those emotions. For example, if the drone is stressed, the charging schedule can be prioritized for faster charging. If the drone is relaxed, the normal charging schedule can be applied. Furthermore, if the drone is in a hurry, a schedule can be set to complete charging in the shortest possible time. This allows for more appropriate charging by adjusting the charging schedule based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns.

[0118] The drone network system can also estimate the drone's emotions and adjust the battery replacement timing based on those emotions. For example, if the drone is stressed, battery replacement will be prioritized to ensure flight safety. If the drone is relaxed, the normal replacement timing can be applied. Furthermore, if the drone is in a hurry, the system can set the battery replacement timing to the shortest possible time. This allows for more appropriate battery management by adjusting the battery replacement timing based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns.

[0119] The drone network system can also estimate the drone's emotions and adjust its flight altitude based on those emotions. For example, if the drone is stressed, it can lower its flight altitude to ensure stable flight. If the drone is relaxed, it can maintain its normal flight altitude. Furthermore, if the drone is in a hurry, it can select the optimal flight altitude to reach its destination quickly. This allows for more appropriate flight by adjusting the flight altitude based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns.

[0120] The drone network system can also estimate the drone's emotions and adjust its flight speed based on those emotions. For example, if the drone is stressed, it can set a lower flight speed to ensure stable flight. If the drone is relaxed, it can maintain its normal flight speed. Furthermore, if the drone is in a hurry, it can select the optimal flight speed to reach its destination quickly. This allows for more appropriate flight by adjusting the flight speed based on the drone's emotions. The drone's emotions are estimated based on criteria such as sensor information and behavioral patterns.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The management team manages the drone's battery status. Specifically, they monitor the battery level, record the number of charge cycles, and monitor the battery temperature, issuing warnings if any abnormalities are detected. If the battery level drops, they instruct the drone to land at the nearest base station's drone port, and if the number of charge cycles exceeds a certain number, they recommend replacing the battery. Furthermore, if the battery temperature is abnormally high, they temporarily suspend the drone's flight and allow it to cool down. Step 2: The scheduling unit creates a charging schedule based on the battery status managed by the management unit. Specifically, it determines the optimal charging timing considering the remaining battery level and the number of charging cycles, and adjusts the charging schedules for multiple drones. It also makes adjustments to minimize waiting time for charging and sets appropriate charging cycles to extend battery life. Step 3: The charging unit charges the drone based on the charging schedule created by the scheduling unit. Specifically, it automatically starts charging when the drone lands at the drone port installed at the base station, and notifies the drone when charging is complete, instructing it to resume flight. It can also monitor the battery temperature during charging and temporarily suspend charging if there is an abnormality. Furthermore, it can check the battery level when charging is complete and perform additional charging if necessary. Step 4: The optimization unit analyzes weather data and calculates the optimal flight path to avoid adverse weather conditions. Specifically, it collects weather data in real time and changes the drone's flight path if adverse weather conditions such as strong winds or heavy rain are predicted. It can also adjust flight altitude and speed based on weather data to optimize fuel efficiency. Furthermore, it can optimize flight time based on weather data to minimize battery consumption. Step 5: The analysis unit analyzes SNS information and calculates a flight path to avoid inappropriate routes. Specifically, it collects SNS information in real time and avoids inappropriate routes such as densely populated areas and disaster-prone areas. It can also change the flight path based on SNS information to select a safe route. Furthermore, it can optimize flight time based on SNS information to minimize battery consumption.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the management unit, scheduling unit, charging unit, optimization unit, and analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the management unit monitors the drone's battery status using the control unit 46A of the smart device 14 and records the number of battery charging cycles using the specific processing unit 290 of the data processing unit 12. The scheduling unit creates a charging schedule using, for example, the specific processing unit 290 of the data processing unit 12 and determines the charging timing using the control unit 46A of the smart device 14. The charging unit starts charging the drone using, for example, the control unit 46A of the smart device 14 and provides notification when charging is complete. The optimization unit analyzes weather data using, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the drone's flight path. The analysis unit analyzes SNS information using, for example, the specific processing unit 290 of the data processing unit 12 and calculates a flight path to avoid inappropriate routes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the management unit, scheduling unit, charging unit, optimization unit, and analysis unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit monitors the drone's battery status using the control unit 46A of the smart glasses 214 and records the number of battery charging cycles using the specific processing unit 290 of the data processing unit 12. The scheduling unit creates a charging schedule using the specific processing unit 290 of the data processing unit 12 and determines the charging timing using the control unit 46A of the smart glasses 214. The charging unit starts charging the drone using the control unit 46A of the smart glasses 214 and notifies when charging is complete. The optimization unit analyzes weather data using the specific processing unit 290 of the data processing unit 12 and optimizes the drone's flight path. The analysis unit analyzes SNS information using the specific processing unit 290 of the data processing unit 12 and calculates a flight path to avoid inappropriate routes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the management unit, scheduling unit, charging unit, optimization unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit monitors the drone's battery status using the control unit 46A of the headset terminal 314 and records the number of battery charging cycles using the specific processing unit 290 of the data processing unit 12. The scheduling unit creates a charging schedule using, for example, the specific processing unit 290 of the data processing unit 12 and determines the charging timing using the control unit 46A of the headset terminal 314. The charging unit starts charging the drone using, for example, the control unit 46A of the headset terminal 314 and notifies when charging is complete. The optimization unit analyzes weather data using, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the drone's flight path. The analysis unit analyzes SNS information using, for example, the specific processing unit 290 of the data processing unit 12 and calculates a flight path to avoid inappropriate routes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the management unit, scheduling unit, charging unit, optimization unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the management unit monitors the drone's battery status using the control unit 46A of the robot 414 and records the number of battery charging cycles using the specific processing unit 290 of the data processing unit 12. The scheduling unit creates a charging schedule using the specific processing unit 290 of the data processing unit 12 and determines the charging timing using the control unit 46A of the robot 414. The charging unit starts charging the drone using the control unit 46A of the robot 414 and notifies when charging is complete. The optimization unit analyzes weather data using the specific processing unit 290 of the data processing unit 12 and optimizes the drone's flight path. The analysis unit analyzes SNS information using the specific processing unit 290 of the data processing unit 12 and calculates a flight path to avoid inappropriate routes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The management department manages the battery status of the drone, A scheduling unit creates a charging schedule based on the battery status managed by the aforementioned management unit, A charging unit that charges the drone based on the charging schedule created by the aforementioned scheduling unit, An optimization unit that analyzes weather data and optimizes the shipping route, It includes an analysis unit that analyzes SNS information and avoids inappropriate routes. A system characterized by the following features. (Note 2) The aforementioned management department, Monitor the drone's battery status in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned scheduling unit is Based on the battery status managed by the aforementioned control unit, an optimal charging schedule is created. The system described in Appendix 1, characterized by the features described herein. (Note 4) The charging unit is The drone is charged based on the charging schedule created by the aforementioned scheduling unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, Analyze weather data and calculate the optimal route to avoid bad weather. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, By analyzing information from social media, the system calculates a route that avoids inappropriate paths such as densely populated areas and disaster-prone regions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, The system estimates the drone's emotions and adjusts the frequency of battery status monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, Analyze the drone's past battery usage history to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, When monitoring battery status, filtering is performed based on the drone's current flight status and mission objectives. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, It estimates the drone's emotions and prioritizes battery status based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, When monitoring battery status, the system prioritizes monitoring of relevant drones by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, When monitoring battery status, the drone's social media activity is analyzed to obtain relevant battery information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned scheduling unit is The system estimates the drone's emotions and adjusts how the charging schedule is expressed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned scheduling unit is When creating a charging schedule, adjust the level of detail in the schedule based on the importance of the battery. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned scheduling unit is When creating a charging schedule, different scheduling algorithms are applied depending on the drone category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned scheduling unit is It estimates the drone's emotions and adjusts the length of the charging schedule based on the estimated emotions of the drone. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned scheduling unit is When creating a charging schedule, prioritize the schedule based on when the batteries are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned scheduling unit is When creating a charging schedule, adjust the order of the schedules based on battery relationships. The system described in Appendix 1, characterized by the features described herein. (Note 19) The charging unit is It estimates the drone's emotions and adjusts the charging method based on the estimated emotions of the drone. The system described in Appendix 1, characterized by the features described herein. (Note 20) The charging unit is During charging, the drone's past charging history is analyzed to select the optimal charging method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The charging unit is During charging, the charging method is customized based on the drone's current flight status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The charging unit is It estimates the drone's emotions and determines charging priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The charging unit is During charging, the optimal charging method is selected by considering the drone's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The charging unit is During charging, the system analyzes the drone's social media activity and suggests charging methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, The system estimates the drone's emotions and adjusts the flight path optimization method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, When optimizing a shipping route, historical weather data is used to predict the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, When optimizing flight paths, different optimization algorithms are applied for each drone category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, The system estimates the drone's emotions and determines priority for route optimization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, When optimizing flight paths, analyze how the optimization changes based on the timing of the drone's flight. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, When optimizing flight paths, we analyze optimization by referring to relevant market data for drones. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, We estimate the drone's emotions and adjust the analysis method based on the estimated emotions of the drone. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit, During analysis, past SNS data is referenced to select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each drone category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit, The system estimates the drone's emotions and determines the analysis priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, During the analysis, we analyze changes in the analysis based on the timing of SNS posts. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, During the analysis, we will refer to relevant market data from social media. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The management department manages the battery status of the drone, A scheduling unit creates a charging schedule based on the battery status managed by the aforementioned management unit, A charging unit that charges the drone based on the charging schedule created by the aforementioned scheduling unit, An optimization unit that analyzes weather data and optimizes the shipping route, It includes an analysis unit that analyzes SNS information and avoids inappropriate routes. A system characterized by the following features.

2. The aforementioned management department, Monitor the drone's battery status in real time. The system according to feature 1.

3. The aforementioned scheduling unit is Based on the battery status managed by the aforementioned control unit, an optimal charging schedule is created. The system according to feature 1.

4. The charging unit is The drone is charged based on the charging schedule created by the aforementioned scheduling unit. The system according to feature 1.

5. The optimization unit, Analyze weather data and calculate the optimal route to avoid bad weather. The system according to feature 1.

6. The aforementioned analysis unit, By analyzing information from social media, the system calculates a route that avoids inappropriate paths such as densely populated areas and disaster-prone regions. The system according to feature 1.

7. The aforementioned management department, The system estimates the drone's emotions and adjusts the frequency of battery status monitoring based on the estimated emotions. The system according to feature 1.

8. The aforementioned management department, Analyze the drone's past battery usage history to select the optimal monitoring method. The system according to feature 1.

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