system

Abstract

The system according to this embodiment aims to calculate the optimal launch window and orbit insertion parameters, taking into account weather conditions and rocket characteristics, and to plan the launch order and timing of multiple small satellites. [Solution] The system according to the embodiment comprises a weather forecasting unit, a characteristics analysis unit, an orbit calculation unit, and a planning unit. The weather forecasting unit predicts weather conditions. The characteristics analysis unit analyzes the characteristics of the rocket and payload based on the weather conditions predicted by the weather forecasting unit. The orbit calculation unit calculates the optimal launch window and orbit insertion parameters based on the characteristics analyzed by the characteristics analysis unit. The planning unit plans the launch order and timing of multiple small satellites based on the launch window and orbit insertion parameters calculated by the orbit calculation unit.

Description

Technical Field

[0001] The technology of this 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the calculation of the optimal launch window and orbit injection parameters considering meteorological conditions and rocket characteristics has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to calculate the optimal launch window and orbit injection parameters in consideration of meteorological conditions and rocket characteristics, and plan the launch order and timing of a plurality of small satellites.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a weather forecasting unit, a characteristics analysis unit, an orbit calculation unit, and a planning unit. The weather forecasting unit predicts weather conditions. The characteristics analysis unit analyzes the characteristics of the rocket and payload based on the weather conditions predicted by the weather forecasting unit. The orbit calculation unit calculates the optimal launch window and orbit insertion parameters based on the characteristics analyzed by the characteristics analysis unit. The planning unit plans the launch order and timing of multiple small satellites based on the launch window and orbit insertion parameters calculated by the orbit calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment can calculate the optimal launch window and orbit insertion parameters, taking into account weather conditions and rocket characteristics, and can plan the launch order and timing of multiple small satellites. [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 controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An orbit agent system according to an embodiment of the present invention is a system that uses AI to analyze weather conditions, rocket and payload characteristics, and proposes a launch window and orbit insertion parameters to maximize fuel efficiency and success rate. The orbit agent system analyzes weather conditions, rocket and payload characteristics, and proposes a launch window and orbit insertion parameters to maximize fuel efficiency and success rate. Specifically, first, a weather forecasting AI predicts weather conditions, and then an AI that analyzes rocket and payload characteristics analyzes those characteristics. Based on this information, an orbit calculation and optimization AI calculates the optimal launch window and orbit insertion parameters. Furthermore, it also plans the order and timing for efficiently launching a large number of small satellites. For example, the orbit agent system predicts weather conditions using a weather forecasting AI. Next, an AI that analyzes rocket and payload characteristics analyzes those characteristics. Based on this information, an orbit calculation and optimization AI calculates the optimal launch window and orbit insertion parameters. Furthermore, the orbit agent system also plans the order and timing for efficiently launching a large number of small satellites. As a result, the launch success rate improves to over 95%, fuel costs are reduced by an average of 20%, and the launch window increases by 20% annually. This allows the Orbit Agent system to improve launch success rates to over 95%, reduce fuel costs by an average of 20%, and increase the launch window by 20% annually.

[0029] The orbit agent system according to this embodiment comprises a weather forecasting unit, a characteristics analysis unit, an orbit calculation unit, and a planning unit. The weather forecasting unit predicts weather conditions. The weather forecasting unit predicts weather conditions using, for example, a weather forecasting AI. The weather forecasting unit can predict weather conditions such as temperature, humidity, wind speed, and precipitation using a weather forecasting AI. The characteristics analysis unit analyzes the characteristics of the rocket and payload. The characteristics analysis unit analyzes characteristics such as the weight, shape, material, and durability of the rocket and payload. The characteristics analysis unit can analyze the characteristics of the rocket and payload in detail and improve the success rate of launches. The orbit calculation unit calculates the optimal launch window and orbit insertion parameters. The orbit calculation unit calculates the optimal launch window considering, for example, weather conditions, orbit parameters, and the utilization status of the launch facility. The orbit calculation unit can calculate orbit insertion parameters such as orbital inclination, orbital altitude, and orbital period. The planning unit plans the launch order and timing of multiple small satellites. The planning department plans the launch sequence and timing of small satellites, such as communication satellites, observation satellites, and experimental satellites. The planning department can plan the sequence and timing for the efficient launch of multiple small satellites, thereby improving launch efficiency. As a result, the orbit agent system according to the embodiment can maximize fuel efficiency and success rate by analyzing weather conditions, rocket and payload characteristics, and proposing the optimal launch window and orbit insertion parameters.

[0030] The weather forecasting unit predicts weather conditions. For example, the weather forecasting unit uses weather forecasting AI to predict weather conditions. Using weather forecasting AI, the weather forecasting unit can predict weather conditions such as temperature, humidity, wind speed, and precipitation. Specifically, the weather forecasting AI receives past weather data and current weather observation data as input, analyzes this data, and predicts future weather conditions. The weather forecasting AI uses deep learning technology to learn complex weather patterns and make highly accurate predictions. For example, based on past weather data, the weather forecasting AI learns temperature fluctuation patterns and precipitation trends in a specific region, and uses this to predict future weather conditions. Furthermore, the weather forecasting AI can incorporate real-time updated weather observation data and make predictions based on the latest weather conditions. This allows the weather forecasting unit to provide weather information necessary for launch timing and orbit calculations, thereby improving the success rate of launches. In addition, based on the prediction results of the weather forecasting AI, the weather forecasting unit can perform a launch risk assessment and propose measures to ensure launch safety. For example, if severe weather such as strong winds or thunderstorms is predicted, the weather forecasting unit can propose a launch postponement or an alternative launch window. In this way, the weather forecasting unit can play a crucial role in maximizing the safety and success rate of the launch.

[0031] The characteristics analysis unit analyzes the characteristics of the rocket and payload. For example, it analyzes characteristics such as the weight, shape, material, and durability of the rocket and payload. Specifically, the characteristics analysis unit analyzes the weight distribution and structural strength of the rocket based on the rocket's design drawings and manufacturing data. This allows for the evaluation of the rocket's flight performance and durability, improving the launch success rate. The characteristics analysis unit also analyzes the characteristics of the payload in detail and evaluates its resistance to vibration and shock during launch. For example, based on the weight, shape, and material of the payload, it can simulate its resistance to acceleration and vibration during launch and set optimal launch conditions. Furthermore, the characteristics analysis unit can analyze the interaction between the rocket and the payload and optimize launch stability and flight path. For example, by considering the rocket's thrust and the payload's center of gravity, it can calculate the optimal launch angle and flight path, improving the launch success rate. In this way, the characteristics analysis unit can analyze the characteristics of the rocket and payload in detail and play a crucial role in maximizing the launch success rate.

[0032] The orbit calculation unit calculates the optimal launch window and orbit insertion parameters. The orbit calculation unit calculates the optimal launch window by considering factors such as weather conditions, orbital parameters, and launch facility utilization. Specifically, the orbit calculation unit evaluates the weather conditions at launch based on weather information provided by the weather forecasting unit and determines the optimal launch timing. Furthermore, the orbit calculation unit calculates the optimal orbit insertion parameters by considering orbital parameters such as rocket thrust, payload weight, orbital inclination, orbital altitude, and orbital period. For example, the orbit calculation unit can calculate the required fuel amount and thrust distribution based on the rocket thrust and payload weight, and set the optimal orbit insertion parameters. In addition, the orbit calculation unit determines the optimal launch window by considering launch facility utilization and coordinating with other launch plans. This allows the orbit calculation unit to provide the optimal launch window and orbit insertion parameters to maximize the launch success rate. Moreover, the orbit calculation unit can use simulation technology to examine multiple post-launch orbit insertion scenarios and select the most efficient and safe orbit insertion method. This allows the orbital calculation unit to play a crucial role in maximizing the launch success rate and fuel efficiency.

[0033] The planning department plans the launch sequence and timing of multiple small satellites. For example, it plans the launch sequence and timing of small satellites such as communication satellites, observation satellites, and experimental satellites. Specifically, the planning department determines the optimal launch sequence and timing by considering the mission requirements and operational schedules of each small satellite. For example, a communication satellite needs to be launched at a specific time to provide communication services in a specific region. On the other hand, an observation satellite needs to be placed into a specific orbit for Earth observation and environmental monitoring. The planning department can optimize the launch sequence and timing of each small satellite by considering these requirements. Furthermore, the planning department creates an efficient launch schedule by coordinating with launch facility availability and other launch plans. This allows the planning department to plan the sequence and timing for the efficient launch of multiple small satellites, improving launch efficiency. The planning department can also create post-launch satellite operation plans and manage the operational schedules for achieving each satellite's mission. This allows the planning department to play a crucial role in maximizing launch efficiency and success rates.

[0034] The weather forecasting unit can predict weather conditions using weather forecasting AI. For example, the weather forecasting unit can use weather forecasting AI to predict weather conditions such as temperature, humidity, wind speed, and precipitation. The weather forecasting unit can use weather forecasting AI to collect weather data and predict weather conditions using machine learning models. For example, the weather forecasting unit can predict future weather conditions based on past weather data. The weather forecasting unit can use weather forecasting AI to monitor changes in weather conditions in real time and update the prediction results. This improves the accuracy of weather condition predictions by using weather forecasting AI. Weather forecasting AI is implemented using, for example, machine learning models, datasets, and prediction algorithms. Some or all of the above-described processes in the weather forecasting unit may be performed using, for example, generative AI, or without using generative AI. For example, the weather forecasting unit can input weather data into generative AI and have the generative AI perform weather condition predictions.

[0035] The characteristic analysis unit can analyze the characteristics of the rocket and payload. For example, the characteristic analysis unit analyzes characteristics such as the weight, shape, material, and durability of the rocket and payload. The characteristic analysis unit can analyze the characteristics of the rocket and payload in detail and improve the launch success rate. Based on the characteristics of the rocket and payload, the characteristic analysis unit can evaluate the launch risk and highlight characteristics that pose a high risk. For example, if the rocket is overweight, the characteristic analysis unit will highlight the risk. It can also highlight the risk if the payload is made of a fragile material. Based on the characteristics of the rocket and payload, the characteristic analysis unit can predict the launch success rate and recommend characteristics that increase the success rate. For example, if the rocket's shape is aerodynamically superior, the characteristic analysis unit will recommend that characteristic. It can also recommend if the payload has high durability. In this way, analyzing the characteristics of the rocket and payload improves the launch success rate. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the characteristic analysis unit can input characteristic data of the rocket and payload into the generating AI, and have the generating AI perform the characteristic analysis.

[0036] The orbital calculation unit can calculate the optimal launch window and orbital insertion parameters. The orbital calculation unit calculates the optimal launch window by considering, for example, weather conditions, orbital parameters, and the utilization status of the launch facility. The orbital calculation unit can calculate orbital insertion parameters such as orbital inclination, orbital altitude, and orbital period. Based on the optimal launch window and orbital insertion parameters, the orbital calculation unit can predict the success rate of the launch and propose a launch window and orbital insertion parameters that have a high success rate. For example, the orbital calculation unit can propose a launch window with favorable weather conditions. The orbital calculation unit can also propose orbital insertion parameters with an appropriate orbital inclination. Based on the optimal launch window and orbital insertion parameters, the orbital calculation unit can maximize fuel efficiency. For example, the orbital calculation unit can propose a launch window with low fuel consumption. The orbital calculation unit can also propose orbital insertion parameters that have high fuel efficiency. As a result, by calculating the optimal launch window and orbital insertion parameters, fuel efficiency and the success rate are improved. Some or all of the above processing in the orbital calculation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the orbit calculation unit can input data on weather conditions and orbital parameters into the generating AI, which can then perform calculations of the optimal launch window and orbital insertion parameters.

[0037] The planning unit can plan the launch sequence and timing of multiple small satellites. For example, the planning unit plans the launch sequence and timing of small satellites such as communication satellites, observation satellites, and experimental satellites. The planning unit can plan the sequence and timing for the efficient launch of multiple small satellites and improve launch efficiency. Based on the launch sequence and timing, the planning unit can predict the success rate of the launch and propose a sequence and timing with a high success rate. For example, the planning unit can propose a sequence in which communication satellites are launched first. The planning unit can also propose a timing in which observation satellites are launched later. Based on the launch sequence and timing, the planning unit can maximize fuel efficiency. For example, the planning unit can propose a sequence and timing that consumes less fuel. The planning unit can also propose a sequence and timing that is highly fuel-efficient. In this way, by planning the launch sequence and timing of multiple small satellites, launch efficiency is improved. Some or all of the above processing in the planning unit may be performed using, for example, generative AI, or without using generative AI. For example, the planning department can input data from small satellites into a generating AI and have the AI ​​execute a plan for the launch sequence and timing.

[0038] The weather forecasting unit can improve the accuracy of its forecasts by referring to past weather data. For example, the weather forecasting unit can refer to weather data from the past 10 years and analyze seasonal weather patterns. The weather forecasting unit can refer to weather data from past launches and identify weather conditions that have a high success rate. The weather forecasting unit can also calculate the probability of specific weather conditions occurring based on past weather data. In this way, the accuracy of weather forecasts is improved by referring to past weather data. Past weather data includes, but is not limited to, weather observation data, satellite data, and weather radar data. Some or all of the above processing in the weather forecasting unit may be performed using, for example, AI, or not using AI. For example, the weather forecasting unit can input past weather data into a generating AI and have the generating AI perform the task of improving the accuracy of weather forecasts.

[0039] The weather forecasting unit can perform a risk assessment for specific weather conditions and highlight high-risk conditions. For example, the weather forecasting unit can highlight high-risk weather conditions such as strong winds and thunderstorms. The weather forecasting unit can also display the probability of high-risk weather conditions occurring. The weather forecasting unit can also suggest countermeasures for high-risk weather conditions. This allows for attention to be drawn to high-risk weather conditions by highlighting them. Specific weather conditions include, but are not limited to, typhoons, heavy rain, and strong winds. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input weather data into a generating AI and have the generating AI perform a risk assessment.

[0040] The weather forecasting unit can improve the accuracy of its forecasts by considering geographical weather patterns. For example, the weather forecasting unit can analyze regional weather patterns to improve forecast accuracy. The weather forecasting unit can make weather forecasts for specific regions by considering geographical characteristics. The weather forecasting unit can also calculate the probability of specific weather conditions occurring based on geographical weather patterns. This improves the accuracy of weather forecasts by considering geographical weather patterns. Geographical weather patterns include, but are not limited to, regional weather characteristics and seasonal weather variations. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input geographical weather data into a generating AI and have the generating AI perform the task of improving forecast accuracy.

[0041] The weather forecasting unit can update weather forecast results in real time and provide the latest information. The weather forecasting unit can update weather forecasts based on real-time weather data, for example. The weather forecasting unit can display weather forecast results in real time. The weather forecasting unit can also improve the accuracy of weather forecasts based on real-time weather data. This allows the latest information to be provided by updating weather forecast results in real time. Methods for updating in real time include, but are not limited to, update intervals and data acquisition methods. Some or all of the above-described processes in the weather forecasting unit may be performed using, for example, AI, or without AI. For example, the weather forecasting unit can input real-time weather data into a generating AI and have the generating AI perform the update of the forecast results.

[0042] The characteristic analysis unit can improve the accuracy of its analysis by referring to historical data of the rocket and payload. For example, the characteristic analysis unit can analyze the characteristics of the rocket and payload by referring to past launch data. The characteristic analysis unit can improve the accuracy of the characteristic analysis based on past launch success rates. The characteristic analysis unit can also evaluate the impact of specific characteristics on success based on historical data. In this way, the accuracy of the characteristic analysis is improved by referring to historical data. Historical data includes, but is not limited to, launch history data and performance data. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, AI, or not using AI. For example, the characteristic analysis unit can input historical data into a generating AI and have the generating AI perform the improvement of the accuracy of the characteristic analysis.

[0043] The characteristic analysis unit can perform a risk assessment based on the characteristics of the rocket and payload, and can highlight high-risk characteristics. For example, the characteristic analysis unit can highlight high-risk characteristics to draw attention to them. The characteristic analysis unit can evaluate the impact of high-risk characteristics on the success rate. The characteristic analysis unit can also propose countermeasures for high-risk characteristics. This allows for drawing attention to high-risk characteristics by highlighting them. Risk assessment includes, but is not limited to, methods for quantifying risk and evaluation criteria. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, AI, or not using AI. For example, the characteristic analysis unit can input rocket and payload characteristic data into a generating AI and have the generating AI perform a risk assessment.

[0044] The characteristic analysis unit can improve the accuracy of its analysis by referring to data from the rocket and payload manufacturers. For example, the characteristic analysis unit can improve the accuracy of its characteristic analysis by referring to data provided by the rocket and payload manufacturers. The characteristic analysis unit can perform a detailed analysis of the characteristics based on the manufacturer's data. The characteristic analysis unit can also evaluate the reliability of the characteristics by referring to the manufacturer's data. This improves the accuracy of the characteristic analysis by referring to the manufacturer's data. Manufacturer data includes, but is not limited to, technical data and quality control data provided by the manufacturer. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, AI, or not using AI. For example, the characteristic analysis unit can input manufacturer data into a generating AI and have the generating AI perform the improvement of the accuracy of the characteristic analysis.

[0045] The characteristic analysis unit can update the results of characteristic analysis in real time and provide the latest information. The characteristic analysis unit can update the results of characteristic analysis based on real-time data, for example. The characteristic analysis unit can display the results of characteristic analysis in real time. The characteristic analysis unit can also improve the accuracy of characteristic analysis based on real-time data. This allows for the provision of the latest information by updating the results of characteristic analysis in real time. Methods for updating in real time include, but are not limited to, the update interval and data acquisition method. Some or all of the above-described processes in the characteristic analysis unit may be performed using, for example, AI, or without AI. For example, the characteristic analysis unit can input real-time data into a generating AI and have the generating AI perform the update of the characteristic analysis results.

[0046] The orbit calculation unit can improve the accuracy of its calculations by referring to past launch data. For example, the orbit calculation unit can improve the accuracy of its orbit calculations by referring to past launch data. The orbit calculation unit can improve the accuracy of its orbit calculations based on past launch success rates. The orbit calculation unit can also evaluate the impact of specific orbit parameters on success based on past data. This improves the accuracy of orbit calculations by referring to past launch data. Past launch data includes, but is not limited to, launch history data and performance data. Some or all of the above processing in the orbit calculation unit may be performed using, for example, AI, or not using AI. For example, the orbit calculation unit can input past launch data into a generating AI and have the generating AI perform the calculation accuracy improvement.

[0047] The orbital calculation unit can perform a risk assessment for specific orbital parameters and highlight high-risk parameters. For example, the orbital calculation unit can highlight high-risk orbital parameters to draw attention to them. The orbital calculation unit can assess the impact of high-risk orbital parameters on the success rate. The orbital calculation unit can also propose countermeasures for high-risk orbital parameters. This allows for drawing attention to high-risk orbital parameters by highlighting them. Risk assessment includes, but is not limited to, methods for quantifying risk and evaluation criteria. Some or all of the above processing in the orbital calculation unit may be performed using, for example, AI, or not using AI. For example, the orbital calculation unit can input orbital parameter data into a generating AI and have the generating AI perform the risk assessment.

[0048] The orbital calculation unit can improve the accuracy of calculations by considering geographical orbital patterns. For example, the orbital calculation unit can analyze regional orbital patterns to improve calculation accuracy. The orbital calculation unit can perform orbital calculations for specific regions by considering geographical characteristics. The orbital calculation unit can also calculate the probability of specific orbital parameters occurring based on geographical orbital patterns. This improves the accuracy of orbital calculations by considering geographical orbital patterns. Geographical orbital patterns include, but are not limited to, regional orbital characteristics and seasonal orbital variations. Some or all of the above processing in the orbital calculation unit may be performed using, for example, AI, or not using AI. For example, the orbital calculation unit can input geographical orbital data into a generating AI and have the generating AI perform the calculation accuracy improvement.

[0049] The orbit calculation unit can update the results of orbit calculations in real time and provide the latest information. The orbit calculation unit can update the results of orbit calculations based on real-time data, for example. The orbit calculation unit can display the results of orbit calculations in real time. The orbit calculation unit can also improve the accuracy of orbit calculations based on real-time data. This allows the latest information to be provided by updating the results of orbit calculations in real time. Methods for updating in real time include, but are not limited to, the update interval and data acquisition method. Some or all of the above-described processes in the orbit calculation unit may be performed using, for example, AI, or not using AI. For example, the orbit calculation unit can input real-time data into a generating AI and have the generating AI perform the update of the calculation results.

[0050] The planning department can improve the accuracy of its plans by referring to past launch data. For example, the planning department can improve the accuracy of its launch sequence and timing plans by referring to past launch data. The planning department can improve the accuracy of its plans based on past launch success rates. The planning department can also evaluate the impact of specific launch sequences and timings on success based on past data. This improves the accuracy of the plans by referring to past launch data. Past launch data includes, but is not limited to, launch history data and performance data. Some or all of the above processes in the planning department may be performed using, for example, AI, or not using AI. For example, the planning department can input past launch data into a generating AI and have the generating AI perform the improvement of the plan's accuracy.

[0051] The planning department can perform a risk assessment for specific launch sequences and timings and highlight high-risk sequences and timings. For example, the planning department can highlight high-risk launch sequences and timings to draw attention to them. The planning department can assess the impact of high-risk sequences and timings on the success rate. The planning department can also propose countermeasures for high-risk sequences and timings. This allows for drawing attention to high-risk launch sequences and timings by highlighting them. Risk assessment includes, but is not limited to, methods for quantifying risk and evaluation criteria. Some or all of the above processes in the planning department may be performed using, for example, AI, or not using AI. For example, the planning department can input launch sequence and timing data into a generating AI and have the generating AI perform a risk assessment.

[0052] The planning department can improve the accuracy of its plans by considering geographical launch patterns. For example, the planning department can analyze regional launch patterns to improve the accuracy of its plans. The planning department can create launch plans for specific regions by considering geographical characteristics. The planning department can also evaluate the impact of specific launch sequences and timings on success based on geographical launch patterns. This improves the accuracy of the plans by considering geographical launch patterns. Geographical launch patterns include, but are not limited to, regional launch characteristics and seasonal launch variations. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input geographical launch data into a generating AI and have the generating AI perform the task of improving the accuracy of the plans.

[0053] The planning department can update the launch sequence and timing results in real time and provide the latest information. The planning department can update the launch sequence and timing results based on real-time data, for example. The planning department can display the launch sequence and timing results in real time. The planning department can also improve the accuracy of the launch plan based on real-time data. This allows the department to provide the latest information by updating the launch sequence and timing results in real time. Methods for updating in real time include, but are not limited to, update intervals and data acquisition methods. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input real-time data into a generating AI and have the generating AI perform the update of the results.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The weather forecasting unit can not only predict weather conditions but also propose appropriate actions to users based on the forecast results. For example, if bad weather is predicted, the weather forecasting unit can propose a postponement of the launch. It can also propose safety measures for the launch depending on the predicted weather conditions. Furthermore, based on the forecast results, the weather forecasting unit can propose the optimal timing for the launch. In this way, the weather forecasting unit can improve the success rate of launches by not only predicting weather conditions but also proposing actions based on the forecast results.

[0056] The characteristics analysis unit not only analyzes the characteristics of rockets and payloads, but can also provide improvement suggestions to users based on the analysis results. For example, if the rocket is too heavy, the characteristics analysis unit can make specific suggestions for weight reduction. It can also suggest material changes to improve durability if the payload material is fragile. Furthermore, if the rocket and payload shapes are not aerodynamically efficient, the characteristics analysis unit can suggest shape optimization. In this way, the characteristics analysis unit can improve the launch success rate not only by analyzing characteristics but also by providing improvement suggestions.

[0057] The orbit calculation unit not only calculates the optimal launch window and orbital insertion parameters, but can also propose the optimal launch scenario to the user based on the calculation results. For example, the orbit calculation unit can present multiple launch window and orbital insertion parameter scenarios and explain the advantages and disadvantages of each scenario. Furthermore, the orbit calculation unit can provide guidelines for selecting the optimal scenario. In addition, the orbit calculation unit can compare the success rate and fuel efficiency of each scenario. In this way, the orbit calculation unit can support user decision-making not only by presenting calculation results, but also by proposing the optimal launch scenario.

[0058] The planning department can not only plan the launch sequence and timing of multiple small satellites, but also propose the optimal launch strategy to users based on the planning results. For example, the planning department can present multiple launch strategies and explain the advantages and disadvantages of each strategy. Furthermore, the planning department can provide guidelines for selecting the optimal strategy. In addition, the planning department can compare the success rate and fuel efficiency of each strategy. This allows the planning department to support user decision-making not only by presenting planning results, but also by proposing the optimal launch strategy.

[0059] The weather forecasting unit can not only predict weather conditions but also propose appropriate actions to users based on the forecast results. For example, if bad weather is predicted, the weather forecasting unit can propose a postponement of the launch. It can also propose safety measures for the launch depending on the predicted weather conditions. Furthermore, based on the forecast results, the weather forecasting unit can propose the optimal timing for the launch. In this way, the weather forecasting unit can improve the success rate of launches by not only predicting weather conditions but also proposing actions based on the forecast results.

[0060] The characteristics analysis unit not only analyzes the characteristics of rockets and payloads, but can also provide improvement suggestions to users based on the analysis results. For example, if the rocket is too heavy, the characteristics analysis unit can make specific suggestions for weight reduction. It can also suggest material changes to improve durability if the payload material is fragile. Furthermore, if the rocket and payload shapes are not aerodynamically efficient, the characteristics analysis unit can suggest shape optimization. In this way, the characteristics analysis unit can improve the launch success rate not only by analyzing characteristics but also by providing improvement suggestions.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The weather forecasting unit predicts weather conditions. The weather forecasting unit predicts weather conditions such as temperature, humidity, wind speed, and precipitation using, for example, weather forecasting AI. Step 2: The characteristics analysis unit analyzes the characteristics of the rocket and payload based on the weather conditions predicted by the weather forecasting unit. The characteristics analysis unit analyzes in detail the characteristics of the rocket and payload, such as weight, shape, material, and durability, to improve the success rate of the launch. Step 3: The orbit calculation unit calculates the optimal launch window and orbit insertion parameters based on the characteristics analyzed by the characteristic analysis unit. The orbit calculation unit calculates the optimal launch window and orbit insertion parameters such as orbital inclination, orbital altitude, and orbital period, taking into consideration, for example, weather conditions, orbital parameters, and the utilization status of the launch facility. Step 4: The Planning Department plans the launch sequence and timing of multiple small satellites based on the launch window and orbit insertion parameters calculated by the Orbit Calculation Department. The Planning Department plans the launch sequence and timing of small satellites such as communication satellites, observation satellites, and experimental satellites, and plans the sequence and timing to launch multiple small satellites efficiently.

[0063] (Example of form 2) An orbit agent system according to an embodiment of the present invention is a system that uses AI to analyze weather conditions, rocket and payload characteristics, and proposes a launch window and orbit insertion parameters to maximize fuel efficiency and success rate. The orbit agent system analyzes weather conditions, rocket and payload characteristics, and proposes a launch window and orbit insertion parameters to maximize fuel efficiency and success rate. Specifically, first, a weather forecasting AI predicts weather conditions, and then an AI that analyzes rocket and payload characteristics analyzes those characteristics. Based on this information, an orbit calculation and optimization AI calculates the optimal launch window and orbit insertion parameters. Furthermore, it also plans the order and timing for efficiently launching a large number of small satellites. For example, the orbit agent system predicts weather conditions using a weather forecasting AI. Next, an AI that analyzes rocket and payload characteristics analyzes those characteristics. Based on this information, an orbit calculation and optimization AI calculates the optimal launch window and orbit insertion parameters. Furthermore, the orbit agent system also plans the order and timing for efficiently launching a large number of small satellites. As a result, the launch success rate improves to over 95%, fuel costs are reduced by an average of 20%, and the launch window increases by 20% annually. This allows the Orbit Agent system to improve launch success rates to over 95%, reduce fuel costs by an average of 20%, and increase the launch window by 20% annually.

[0064] The orbit agent system according to this embodiment comprises a weather forecasting unit, a characteristics analysis unit, an orbit calculation unit, and a planning unit. The weather forecasting unit predicts weather conditions. The weather forecasting unit predicts weather conditions using, for example, a weather forecasting AI. The weather forecasting unit can predict weather conditions such as temperature, humidity, wind speed, and precipitation using a weather forecasting AI. The characteristics analysis unit analyzes the characteristics of the rocket and payload. The characteristics analysis unit analyzes characteristics such as the weight, shape, material, and durability of the rocket and payload. The characteristics analysis unit can analyze the characteristics of the rocket and payload in detail and improve the success rate of launches. The orbit calculation unit calculates the optimal launch window and orbit insertion parameters. The orbit calculation unit calculates the optimal launch window considering, for example, weather conditions, orbit parameters, and the utilization status of the launch facility. The orbit calculation unit can calculate orbit insertion parameters such as orbital inclination, orbital altitude, and orbital period. The planning unit plans the launch order and timing of multiple small satellites. The planning department plans the launch sequence and timing of small satellites, such as communication satellites, observation satellites, and experimental satellites. The planning department can plan the sequence and timing for the efficient launch of multiple small satellites, thereby improving launch efficiency. As a result, the orbit agent system according to the embodiment can maximize fuel efficiency and success rate by analyzing weather conditions, rocket and payload characteristics, and proposing the optimal launch window and orbit insertion parameters.

[0065] The weather forecasting unit predicts weather conditions. For example, the weather forecasting unit uses weather forecasting AI to predict weather conditions. Using weather forecasting AI, the weather forecasting unit can predict weather conditions such as temperature, humidity, wind speed, and precipitation. Specifically, the weather forecasting AI receives past weather data and current weather observation data as input, analyzes this data, and predicts future weather conditions. The weather forecasting AI uses deep learning technology to learn complex weather patterns and make highly accurate predictions. For example, based on past weather data, the weather forecasting AI learns temperature fluctuation patterns and precipitation trends in a specific region, and uses this to predict future weather conditions. Furthermore, the weather forecasting AI can incorporate real-time updated weather observation data and make predictions based on the latest weather conditions. This allows the weather forecasting unit to provide weather information necessary for launch timing and orbit calculations, thereby improving the success rate of launches. In addition, based on the prediction results of the weather forecasting AI, the weather forecasting unit can perform a launch risk assessment and propose measures to ensure launch safety. For example, if severe weather such as strong winds or thunderstorms is predicted, the weather forecasting unit can propose a launch postponement or an alternative launch window. In this way, the weather forecasting unit can play a crucial role in maximizing the safety and success rate of the launch.

[0066] The characteristics analysis unit analyzes the characteristics of the rocket and payload. For example, it analyzes characteristics such as the weight, shape, material, and durability of the rocket and payload. Specifically, the characteristics analysis unit analyzes the weight distribution and structural strength of the rocket based on the rocket's design drawings and manufacturing data. This allows for the evaluation of the rocket's flight performance and durability, improving the launch success rate. The characteristics analysis unit also analyzes the characteristics of the payload in detail and evaluates its resistance to vibration and shock during launch. For example, based on the weight, shape, and material of the payload, it can simulate its resistance to acceleration and vibration during launch and set optimal launch conditions. Furthermore, the characteristics analysis unit can analyze the interaction between the rocket and the payload and optimize launch stability and flight path. For example, by considering the rocket's thrust and the payload's center of gravity, it can calculate the optimal launch angle and flight path, improving the launch success rate. In this way, the characteristics analysis unit can analyze the characteristics of the rocket and payload in detail and play a crucial role in maximizing the launch success rate.

[0067] The orbit calculation unit calculates the optimal launch window and orbit insertion parameters. The orbit calculation unit calculates the optimal launch window by considering factors such as weather conditions, orbital parameters, and launch facility utilization. Specifically, the orbit calculation unit evaluates the weather conditions at launch based on weather information provided by the weather forecasting unit and determines the optimal launch timing. Furthermore, the orbit calculation unit calculates the optimal orbit insertion parameters by considering orbital parameters such as rocket thrust, payload weight, orbital inclination, orbital altitude, and orbital period. For example, the orbit calculation unit can calculate the required fuel amount and thrust distribution based on the rocket thrust and payload weight, and set the optimal orbit insertion parameters. In addition, the orbit calculation unit determines the optimal launch window by considering launch facility utilization and coordinating with other launch plans. This allows the orbit calculation unit to provide the optimal launch window and orbit insertion parameters to maximize the launch success rate. Moreover, the orbit calculation unit can use simulation technology to examine multiple post-launch orbit insertion scenarios and select the most efficient and safe orbit insertion method. This allows the orbital calculation unit to play a crucial role in maximizing the launch success rate and fuel efficiency.

[0068] The planning department plans the launch sequence and timing of multiple small satellites. For example, it plans the launch sequence and timing of small satellites such as communication satellites, observation satellites, and experimental satellites. Specifically, the planning department determines the optimal launch sequence and timing by considering the mission requirements and operational schedules of each small satellite. For example, a communication satellite needs to be launched at a specific time to provide communication services in a specific region. On the other hand, an observation satellite needs to be placed into a specific orbit for Earth observation and environmental monitoring. The planning department can optimize the launch sequence and timing of each small satellite by considering these requirements. Furthermore, the planning department creates an efficient launch schedule by coordinating with launch facility availability and other launch plans. This allows the planning department to plan the sequence and timing for the efficient launch of multiple small satellites, improving launch efficiency. The planning department can also create post-launch satellite operation plans and manage the operational schedules for achieving each satellite's mission. This allows the planning department to play a crucial role in maximizing launch efficiency and success rates.

[0069] The weather forecasting unit can predict weather conditions using weather forecasting AI. For example, the weather forecasting unit can use weather forecasting AI to predict weather conditions such as temperature, humidity, wind speed, and precipitation. The weather forecasting unit can use weather forecasting AI to collect weather data and predict weather conditions using machine learning models. For example, the weather forecasting unit can predict future weather conditions based on past weather data. The weather forecasting unit can use weather forecasting AI to monitor changes in weather conditions in real time and update the prediction results. This improves the accuracy of weather condition predictions by using weather forecasting AI. Weather forecasting AI is implemented using, for example, machine learning models, datasets, and prediction algorithms. Some or all of the above-described processes in the weather forecasting unit may be performed using, for example, generative AI, or without using generative AI. For example, the weather forecasting unit can input weather data into generative AI and have the generative AI perform weather condition predictions.

[0070] The characteristic analysis unit can analyze the characteristics of the rocket and payload. For example, the characteristic analysis unit analyzes characteristics such as the weight, shape, material, and durability of the rocket and payload. The characteristic analysis unit can analyze the characteristics of the rocket and payload in detail and improve the launch success rate. Based on the characteristics of the rocket and payload, the characteristic analysis unit can evaluate the launch risk and highlight characteristics that pose a high risk. For example, if the rocket is overweight, the characteristic analysis unit will highlight the risk. It can also highlight the risk if the payload is made of a fragile material. Based on the characteristics of the rocket and payload, the characteristic analysis unit can predict the launch success rate and recommend characteristics that increase the success rate. For example, if the rocket's shape is aerodynamically superior, the characteristic analysis unit will recommend that characteristic. It can also recommend if the payload has high durability. In this way, analyzing the characteristics of the rocket and payload improves the launch success rate. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the characteristic analysis unit can input characteristic data of the rocket and payload into the generating AI, and have the generating AI perform the characteristic analysis.

[0071] The orbital calculation unit can calculate the optimal launch window and orbital insertion parameters. The orbital calculation unit calculates the optimal launch window by considering, for example, weather conditions, orbital parameters, and the utilization status of the launch facility. The orbital calculation unit can calculate orbital insertion parameters such as orbital inclination, orbital altitude, and orbital period. Based on the optimal launch window and orbital insertion parameters, the orbital calculation unit can predict the success rate of the launch and propose a launch window and orbital insertion parameters that have a high success rate. For example, the orbital calculation unit can propose a launch window with favorable weather conditions. The orbital calculation unit can also propose orbital insertion parameters with an appropriate orbital inclination. Based on the optimal launch window and orbital insertion parameters, the orbital calculation unit can maximize fuel efficiency. For example, the orbital calculation unit can propose a launch window with low fuel consumption. The orbital calculation unit can also propose orbital insertion parameters that have high fuel efficiency. As a result, by calculating the optimal launch window and orbital insertion parameters, fuel efficiency and the success rate are improved. Some or all of the above processing in the orbital calculation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the orbit calculation unit can input data on weather conditions and orbital parameters into the generating AI, which can then perform calculations of the optimal launch window and orbital insertion parameters.

[0072] The planning unit can plan the launch sequence and timing of multiple small satellites. For example, the planning unit plans the launch sequence and timing of small satellites such as communication satellites, observation satellites, and experimental satellites. The planning unit can plan the sequence and timing for the efficient launch of multiple small satellites and improve launch efficiency. Based on the launch sequence and timing, the planning unit can predict the success rate of the launch and propose a sequence and timing with a high success rate. For example, the planning unit can propose a sequence in which communication satellites are launched first. The planning unit can also propose a timing in which observation satellites are launched later. Based on the launch sequence and timing, the planning unit can maximize fuel efficiency. For example, the planning unit can propose a sequence and timing that consumes less fuel. The planning unit can also propose a sequence and timing that is highly fuel-efficient. In this way, by planning the launch sequence and timing of multiple small satellites, launch efficiency is improved. Some or all of the above processing in the planning unit may be performed using, for example, generative AI, or without using generative AI. For example, the planning department can input data from small satellites into a generating AI and have the AI ​​execute a plan for the launch sequence and timing.

[0073] The weather forecasting unit can estimate the user's emotions and adjust the display method of the weather forecast based on the estimated emotions. For example, if the user is stressed, the weather forecasting unit can provide a simple and easy-to-read display method. If the user is relaxed, the weather forecasting unit can provide a display method that includes detailed information. If the user is in a hurry, the weather forecasting unit can also provide a display method that gets straight to the point. By adjusting the display method of the weather forecast according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or not using AI. For example, the weather forecasting unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The weather forecasting unit can improve the accuracy of its forecasts by referring to past weather data. For example, the weather forecasting unit can refer to weather data from the past 10 years and analyze seasonal weather patterns. The weather forecasting unit can refer to weather data from past launches and identify weather conditions that have a high success rate. The weather forecasting unit can also calculate the probability of specific weather conditions occurring based on past weather data. In this way, the accuracy of weather forecasts is improved by referring to past weather data. Past weather data includes, but is not limited to, weather observation data, satellite data, and weather radar data. Some or all of the above processing in the weather forecasting unit may be performed using, for example, AI, or not using AI. For example, the weather forecasting unit can input past weather data into a generating AI and have the generating AI perform the task of improving the accuracy of weather forecasts.

[0075] The weather forecasting unit can perform a risk assessment for specific weather conditions and highlight high-risk conditions. For example, the weather forecasting unit can highlight high-risk weather conditions such as strong winds and thunderstorms. The weather forecasting unit can also display the probability of high-risk weather conditions occurring. The weather forecasting unit can also suggest countermeasures for high-risk weather conditions. This allows for attention to be drawn to high-risk weather conditions by highlighting them. Specific weather conditions include, but are not limited to, typhoons, heavy rain, and strong winds. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input weather data into a generating AI and have the generating AI perform a risk assessment.

[0076] The weather forecasting unit can estimate the user's emotions and determine the priority of weather forecasts based on the estimated emotions. For example, if the user is stressed, the weather forecasting unit will prioritize displaying important weather information. If the user is relaxed, the weather forecasting unit can provide detailed weather information. If the user is in a hurry, the weather forecasting unit can also provide concise weather information. This allows important information to be displayed preferentially by prioritizing weather forecasts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The weather forecasting unit can improve the accuracy of its forecasts by considering geographical weather patterns. For example, the weather forecasting unit can analyze regional weather patterns to improve forecast accuracy. The weather forecasting unit can make weather forecasts for specific regions by considering geographical characteristics. The weather forecasting unit can also calculate the probability of specific weather conditions occurring based on geographical weather patterns. This improves the accuracy of weather forecasts by considering geographical weather patterns. Geographical weather patterns include, but are not limited to, regional weather characteristics and seasonal weather variations. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input geographical weather data into a generating AI and have the generating AI perform the task of improving forecast accuracy.

[0078] The weather forecasting unit can update weather forecast results in real time and provide the latest information. The weather forecasting unit can update weather forecasts based on real-time weather data, for example. The weather forecasting unit can display weather forecast results in real time. The weather forecasting unit can also improve the accuracy of weather forecasts based on real-time weather data. This allows the latest information to be provided by updating weather forecast results in real time. Methods for updating in real time include, but are not limited to, update intervals and data acquisition methods. Some or all of the above-described processes in the weather forecasting unit may be performed using, for example, AI, or without AI. For example, the weather forecasting unit can input real-time weather data into a generating AI and have the generating AI perform the update of the forecast results.

[0079] The trait analysis unit can estimate the user's emotions and adjust the display method of the trait analysis based on the estimated user emotions. For example, if the user is stressed, the trait analysis unit can provide a simple and highly visible display method. If the user is relaxed, the trait analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the trait analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the trait analysis according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the trait analysis unit may be performed using AI, for example, or without using AI. For example, the trait analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The characteristic analysis unit can improve the accuracy of its analysis by referring to historical data of the rocket and payload. For example, the characteristic analysis unit can analyze the characteristics of the rocket and payload by referring to past launch data. The characteristic analysis unit can improve the accuracy of the characteristic analysis based on past launch success rates. The characteristic analysis unit can also evaluate the impact of specific characteristics on success based on historical data. In this way, the accuracy of the characteristic analysis is improved by referring to historical data. Historical data includes, but is not limited to, launch history data and performance data. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, AI, or not using AI. For example, the characteristic analysis unit can input historical data into a generating AI and have the generating AI perform the improvement of the accuracy of the characteristic analysis.

[0081] The characteristic analysis unit can perform a risk assessment based on the characteristics of the rocket and payload, and can highlight high-risk characteristics. For example, the characteristic analysis unit can highlight high-risk characteristics to draw attention to them. The characteristic analysis unit can evaluate the impact of high-risk characteristics on the success rate. The characteristic analysis unit can also propose countermeasures for high-risk characteristics. This allows for drawing attention to high-risk characteristics by highlighting them. Risk assessment includes, but is not limited to, methods for quantifying risk and evaluation criteria. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, AI, or not using AI. For example, the characteristic analysis unit can input rocket and payload characteristic data into a generating AI and have the generating AI perform a risk assessment.

[0082] The trait analysis unit can estimate the user's emotions and determine the priority of trait analysis based on the estimated user emotions. For example, if the user is stressed, the trait analysis unit will prioritize displaying important traits. If the user is relaxed, the trait analysis unit can provide detailed trait information. If the user is in a hurry, the trait analysis unit can also provide concise trait information. This allows important information to be displayed preferentially by prioritizing trait analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the trait analysis unit may be performed using AI or not using AI. For example, the trait analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The characteristic analysis unit can improve the accuracy of its analysis by referring to data from the rocket and payload manufacturers. For example, the characteristic analysis unit can improve the accuracy of its characteristic analysis by referring to data provided by the rocket and payload manufacturers. The characteristic analysis unit can perform a detailed analysis of the characteristics based on the manufacturer's data. The characteristic analysis unit can also evaluate the reliability of the characteristics by referring to the manufacturer's data. This improves the accuracy of the characteristic analysis by referring to the manufacturer's data. Manufacturer data includes, but is not limited to, technical data and quality control data provided by the manufacturer. Some or all of the above processing in the characteristic analysis unit may be performed using, for example, AI, or not using AI. For example, the characteristic analysis unit can input manufacturer data into a generating AI and have the generating AI perform the improvement of the accuracy of the characteristic analysis.

[0084] The characteristic analysis unit can update the results of characteristic analysis in real time and provide the latest information. The characteristic analysis unit can update the results of characteristic analysis based on real-time data, for example. The characteristic analysis unit can display the results of characteristic analysis in real time. The characteristic analysis unit can also improve the accuracy of characteristic analysis based on real-time data. This allows for the provision of the latest information by updating the results of characteristic analysis in real time. Methods for updating in real time include, but are not limited to, the update interval and data acquisition method. Some or all of the above-described processes in the characteristic analysis unit may be performed using, for example, AI, or without AI. For example, the characteristic analysis unit can input real-time data into a generating AI and have the generating AI perform the update of the characteristic analysis results.

[0085] The trajectory calculation unit can estimate the user's emotions and adjust the display method of the trajectory calculation based on the estimated user emotions. For example, if the user is stressed, the trajectory calculation unit can provide a simple and highly visible display method. If the user is relaxed, the trajectory calculation unit can provide a display method that includes detailed information. If the user is in a hurry, the trajectory calculation unit can also provide a display method that gets straight to the point. By adjusting the display method of the trajectory calculation according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the trajectory calculation unit may be performed using AI, for example, or not using AI. For example, the trajectory calculation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The orbit calculation unit can improve the accuracy of its calculations by referring to past launch data. For example, the orbit calculation unit can improve the accuracy of its orbit calculations by referring to past launch data. The orbit calculation unit can improve the accuracy of its orbit calculations based on past launch success rates. The orbit calculation unit can also evaluate the impact of specific orbit parameters on success based on past data. This improves the accuracy of orbit calculations by referring to past launch data. Past launch data includes, but is not limited to, launch history data and performance data. Some or all of the above processing in the orbit calculation unit may be performed using, for example, AI, or not using AI. For example, the orbit calculation unit can input past launch data into a generating AI and have the generating AI perform the calculation accuracy improvement.

[0087] The orbital calculation unit can perform a risk assessment for specific orbital parameters and highlight high-risk parameters. For example, the orbital calculation unit can highlight high-risk orbital parameters to draw attention to them. The orbital calculation unit can assess the impact of high-risk orbital parameters on the success rate. The orbital calculation unit can also propose countermeasures for high-risk orbital parameters. This allows for drawing attention to high-risk orbital parameters by highlighting them. Risk assessment includes, but is not limited to, methods for quantifying risk and evaluation criteria. Some or all of the above processing in the orbital calculation unit may be performed using, for example, AI, or not using AI. For example, the orbital calculation unit can input orbital parameter data into a generating AI and have the generating AI perform the risk assessment.

[0088] The trajectory calculation unit can estimate the user's emotions and determine the priority of trajectory calculations based on the estimated user emotions. For example, if the user is stressed, the trajectory calculation unit will prioritize displaying important trajectory parameters. If the user is relaxed, the trajectory calculation unit can provide detailed trajectory information. If the user is in a hurry, the trajectory calculation unit can also provide concise trajectory information. This allows important information to be displayed preferentially by prioritizing trajectory calculations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the trajectory calculation unit may be performed using AI, for example, or not using AI. For example, the trajectory calculation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The orbital calculation unit can improve the accuracy of calculations by considering geographical orbital patterns. For example, the orbital calculation unit can analyze regional orbital patterns to improve calculation accuracy. The orbital calculation unit can perform orbital calculations for specific regions by considering geographical characteristics. The orbital calculation unit can also calculate the probability of specific orbital parameters occurring based on geographical orbital patterns. This improves the accuracy of orbital calculations by considering geographical orbital patterns. Geographical orbital patterns include, but are not limited to, regional orbital characteristics and seasonal orbital variations. Some or all of the above processing in the orbital calculation unit may be performed using, for example, AI, or not using AI. For example, the orbital calculation unit can input geographical orbital data into a generating AI and have the generating AI perform the calculation accuracy improvement.

[0090] The orbit calculation unit can update the results of orbit calculations in real time and provide the latest information. The orbit calculation unit can update the results of orbit calculations based on real-time data, for example. The orbit calculation unit can display the results of orbit calculations in real time. The orbit calculation unit can also improve the accuracy of orbit calculations based on real-time data. This allows the latest information to be provided by updating the results of orbit calculations in real time. Methods for updating in real time include, but are not limited to, the update interval and data acquisition method. Some or all of the above-described processes in the orbit calculation unit may be performed using, for example, AI, or not using AI. For example, the orbit calculation unit can input real-time data into a generating AI and have the generating AI perform the update of the calculation results.

[0091] The planning unit can estimate the user's emotions and adjust the display method of the launch order and timing based on the estimated user emotions. For example, if the user is stressed, the planning unit can provide a simple and highly visible display method. If the user is relaxed, the planning unit can provide a display method that includes detailed information. If the user is in a hurry, the planning unit can also provide a concise display method. By adjusting the display method of the launch order and timing according to the user's emotions, a user-friendly display can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The planning department can improve the accuracy of its plans by referring to past launch data. For example, the planning department can improve the accuracy of its launch sequence and timing plans by referring to past launch data. The planning department can improve the accuracy of its plans based on past launch success rates. The planning department can also evaluate the impact of specific launch sequences and timings on success based on past data. This improves the accuracy of the plans by referring to past launch data. Past launch data includes, but is not limited to, launch history data and performance data. Some or all of the above processes in the planning department may be performed using, for example, AI, or not using AI. For example, the planning department can input past launch data into a generating AI and have the generating AI perform the improvement of the plan's accuracy.

[0093] The planning department can perform a risk assessment for specific launch sequences and timings and highlight high-risk sequences and timings. For example, the planning department can highlight high-risk launch sequences and timings to draw attention to them. The planning department can assess the impact of high-risk sequences and timings on the success rate. The planning department can also propose countermeasures for high-risk sequences and timings. This allows for drawing attention to high-risk launch sequences and timings by highlighting them. Risk assessment includes, but is not limited to, methods for quantifying risk and evaluation criteria. Some or all of the above processes in the planning department may be performed using, for example, AI, or not using AI. For example, the planning department can input launch sequence and timing data into a generating AI and have the generating AI perform a risk assessment.

[0094] The planning unit can estimate the user's emotions and determine the priority of launch order and timing based on the estimated emotions. For example, if the user is stressed, the planning unit will prioritize displaying important launch orders and timings. If the user is relaxed, the planning unit can provide detailed launch information. If the user is in a hurry, the planning unit can also provide concise launch information. This allows important information to be displayed preferentially by prioritizing launch order and timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not using AI. For example, the planning unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The planning department can improve the accuracy of its plans by considering geographical launch patterns. For example, the planning department can analyze regional launch patterns to improve the accuracy of its plans. The planning department can create launch plans for specific regions by considering geographical characteristics. The planning department can also evaluate the impact of specific launch sequences and timings on success based on geographical launch patterns. This improves the accuracy of the plans by considering geographical launch patterns. Geographical launch patterns include, but are not limited to, regional launch characteristics and seasonal launch variations. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input geographical launch data into a generating AI and have the generating AI perform the task of improving the accuracy of the plans.

[0096] The planning department can update the launch sequence and timing results in real time and provide the latest information. The planning department can update the launch sequence and timing results based on real-time data, for example. The planning department can display the launch sequence and timing results in real time. The planning department can also improve the accuracy of the launch plan based on real-time data. This allows the department to provide the latest information by updating the launch sequence and timing results in real time. Methods for updating in real time include, but are not limited to, update intervals and data acquisition methods. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input real-time data into a generating AI and have the generating AI perform the update of the results.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The weather forecasting unit can not only predict weather conditions but also propose appropriate actions to users based on the forecast results. For example, if bad weather is predicted, the weather forecasting unit can propose a postponement of the launch. It can also propose safety measures for the launch depending on the predicted weather conditions. Furthermore, based on the forecast results, the weather forecasting unit can propose the optimal timing for the launch. In this way, the weather forecasting unit can improve the success rate of launches by not only predicting weather conditions but also proposing actions based on the forecast results.

[0099] The characteristics analysis unit not only analyzes the characteristics of rockets and payloads, but can also provide improvement suggestions to users based on the analysis results. For example, if the rocket is too heavy, the characteristics analysis unit can make specific suggestions for weight reduction. It can also suggest material changes to improve durability if the payload material is fragile. Furthermore, if the rocket and payload shapes are not aerodynamically efficient, the characteristics analysis unit can suggest shape optimization. In this way, the characteristics analysis unit can improve the launch success rate not only by analyzing characteristics but also by providing improvement suggestions.

[0100] The orbit calculation unit not only calculates the optimal launch window and orbital insertion parameters, but can also propose the optimal launch scenario to the user based on the calculation results. For example, the orbit calculation unit can present multiple launch window and orbital insertion parameter scenarios and explain the advantages and disadvantages of each scenario. Furthermore, the orbit calculation unit can provide guidelines for selecting the optimal scenario. In addition, the orbit calculation unit can compare the success rate and fuel efficiency of each scenario. In this way, the orbit calculation unit can support user decision-making not only by presenting calculation results, but also by proposing the optimal launch scenario.

[0101] The planning department can not only plan the launch sequence and timing of multiple small satellites, but also propose the optimal launch strategy to users based on the planning results. For example, the planning department can present multiple launch strategies and explain the advantages and disadvantages of each strategy. Furthermore, the planning department can provide guidelines for selecting the optimal strategy. In addition, the planning department can compare the success rate and fuel efficiency of each strategy. This allows the planning department to support user decision-making not only by presenting planning results, but also by proposing the optimal launch strategy.

[0102] The weather forecasting unit can estimate the user's emotions and adjust the way weather forecasts are notified based on those emotions. For example, if the user is stressed, the weather forecasting unit can make the notification concise and provide only essential information. If the user is relaxed, the unit can provide a notification that includes detailed weather information. Furthermore, if the user is in a hurry, the unit can provide a short, to-the-point notification. In this way, the weather forecasting unit can provide the most optimal information to the user by adjusting the notification method according to the user's emotions.

[0103] The trait analysis unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the trait analysis unit can make the notification concise and provide only essential information. If the user is relaxed, the trait analysis unit can provide a notification that includes detailed trait information. Furthermore, if the user is in a hurry, the trait analysis unit can provide a short, to-the-point notification. In this way, the trait analysis unit can provide the most optimal information to the user by adjusting the notification method according to the user's emotions.

[0104] The trajectory calculation unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, the trajectory calculation unit can make the notification concise and provide only essential information. If the user is relaxed, the trajectory calculation unit can provide a notification that includes detailed trajectory information. Furthermore, if the user is in a hurry, the trajectory calculation unit can provide a short, to-the-point notification. In this way, the trajectory calculation unit can provide the most optimal information to the user by adjusting the notification method according to the user's emotions.

[0105] The planning department can estimate the user's emotions and adjust the way launch plans are communicated based on those emotions. For example, if the user is stressed, the planning department can make the notification concise and provide only essential information. If the user is relaxed, the planning department can provide a notification that includes detailed launch information. Furthermore, if the user is in a hurry, the planning department can provide a short, to-the-point notification. In this way, the planning department can provide the most optimal information to the user by adjusting the notification method according to the user's emotions.

[0106] The weather forecasting unit can not only predict weather conditions but also propose appropriate actions to users based on the forecast results. For example, if bad weather is predicted, the weather forecasting unit can propose a postponement of the launch. It can also propose safety measures for the launch depending on the predicted weather conditions. Furthermore, based on the forecast results, the weather forecasting unit can propose the optimal timing for the launch. In this way, the weather forecasting unit can improve the success rate of launches by not only predicting weather conditions but also proposing actions based on the forecast results.

[0107] The characteristics analysis unit not only analyzes the characteristics of rockets and payloads, but can also provide improvement suggestions to users based on the analysis results. For example, if the rocket is too heavy, the characteristics analysis unit can make specific suggestions for weight reduction. It can also suggest material changes to improve durability if the payload material is fragile. Furthermore, if the rocket and payload shapes are not aerodynamically efficient, the characteristics analysis unit can suggest shape optimization. In this way, the characteristics analysis unit can improve the launch success rate not only by analyzing characteristics but also by providing improvement suggestions.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The weather forecasting unit predicts weather conditions. The weather forecasting unit predicts weather conditions such as temperature, humidity, wind speed, and precipitation using, for example, weather forecasting AI. Step 2: The characteristics analysis unit analyzes the characteristics of the rocket and payload based on the weather conditions predicted by the weather forecasting unit. The characteristics analysis unit analyzes in detail the characteristics of the rocket and payload, such as weight, shape, material, and durability, to improve the success rate of the launch. Step 3: The orbit calculation unit calculates the optimal launch window and orbit insertion parameters based on the characteristics analyzed by the characteristic analysis unit. The orbit calculation unit calculates the optimal launch window and orbit insertion parameters such as orbital inclination, orbital altitude, and orbital period, taking into consideration, for example, weather conditions, orbital parameters, and the utilization status of the launch facility. Step 4: The Planning Department plans the launch sequence and timing of multiple small satellites based on the launch window and orbit insertion parameters calculated by the Orbit Calculation Department. The Planning Department plans the launch sequence and timing of small satellites such as communication satellites, observation satellites, and experimental satellites, and plans the sequence and timing to launch multiple small satellites efficiently.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the weather forecasting unit, characteristic analysis unit, orbit calculation unit, and planning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the weather forecasting unit is implemented by the control unit 46A of the smart device 14 and predicts weather conditions using a weather forecasting AI. The characteristic analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the rocket and payload. The orbit calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the optimal launch window and orbit insertion parameters. The planning unit is implemented by the control unit 46A of the smart device 14 and plans the launch order and timing of multiple small satellites. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the weather forecasting unit, characteristic analysis unit, orbit calculation unit, and planning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the weather forecasting unit is implemented by the control unit 46A of the smart glasses 214 and predicts weather conditions using weather forecasting AI. The characteristic analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the rocket and payload. The orbit calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and calculates the optimal launch window and orbit insertion parameters. The planning unit is implemented, for example, by the control unit 46A of the smart glasses 214 and plans the launch order and timing of multiple small satellites. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

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

[0133] The 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.

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

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

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

[0137] Figure 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.

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

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

[0140] In the 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.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0144] The data processing system 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.

[0145] Each of the multiple elements described above, including the weather forecasting unit, characteristic analysis unit, orbit calculation unit, and planning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the weather forecasting unit is implemented by the control unit 46A of the headset terminal 314 and predicts weather conditions using a weather forecasting AI. The characteristic analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the rocket and payload. The orbit calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the optimal launch window and orbit insertion parameters. The planning unit is implemented by the control unit 46A of the headset terminal 314 and plans the launch order and timing of multiple small satellites. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

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

[0149] The 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.

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

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the weather forecasting unit, characteristic analysis unit, orbit calculation unit, and planning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the weather forecasting unit is implemented by the control unit 46A of the robot 414 and predicts weather conditions using a weather forecasting AI. The characteristic analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the rocket and payload. The orbit calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the optimal launch window and orbit insertion parameters. The planning unit is implemented by, for example, the control unit 46A of the robot 414 and plans the launch order and timing of multiple small satellites. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The weather forecasting department predicts weather conditions, A characteristic analysis unit analyzes the characteristics of the rocket and payload based on the weather conditions predicted by the weather forecasting unit, An orbital calculation unit calculates the optimal launch window and orbital insertion parameters based on the characteristics analyzed by the characteristic analysis unit, The system includes a planning unit that plans the launch order and timing of multiple small satellites based on the launch window and orbit insertion parameters calculated by the orbit calculation unit. A system characterized by the following features. (Note 2) The aforementioned weather forecasting unit, Predicting weather conditions using weather forecasting AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The characteristic analysis unit is, Analyze the characteristics of the rocket and payload. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned orbital calculation unit, Calculate the optimal launch window and orbit insertion parameters. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned planning department, Plan the launch sequence and timing of multiple small satellites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned weather forecasting unit, The system estimates the user's emotions and adjusts how weather forecasts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned weather forecasting unit, By referring to past weather data, we can improve the accuracy of our forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned weather forecasting unit, It performs a risk assessment for specific weather conditions and highlights high-risk conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned weather forecasting unit, The system estimates user sentiment and prioritizes weather forecasts based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned weather forecasting unit, Improving forecast accuracy by considering geographical weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned weather forecasting unit, We update weather forecast results in real time and provide the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The characteristic analysis unit is, It estimates the user's emotions and adjusts how the characteristic analysis is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The characteristic analysis unit is, Referencing historical data on rockets and payloads improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The characteristic analysis unit is, A risk assessment is performed based on the characteristics of the rocket and payload, and high-risk characteristics are highlighted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The characteristic analysis unit is, We estimate the user's emotions and determine the priority of characteristic analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The characteristic analysis unit is, Improve the accuracy of the analysis by referencing data from the rocket and payload manufacturers. The system described in Appendix 1, characterized by the features described herein. (Note 17) The characteristic analysis unit is, We update the results of the characteristic analysis in real time and provide the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned orbital calculation unit, It estimates the user's emotions and adjusts the display method of trajectory calculations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned orbital calculation unit, Improve the accuracy of calculations by referring to past launch data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned orbital calculation unit, This tool performs a risk assessment for specific orbital parameters and highlights the parameters with high risk. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned orbital calculation unit, The system estimates the user's emotions and determines the priority of trajectory calculations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned orbital calculation unit, Improve calculation accuracy by considering geographical orbital patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned orbital calculation unit, We update the results of orbital calculations in real time and provide the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, The system estimates user emotions and adjusts the display method for launch order and timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning department, By referring to past launch data, we can improve the accuracy of our plans. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned planning department, The system performs a risk assessment for specific launch sequences and timings, and highlights sequences and timings with high risk. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned planning department, It estimates user sentiment and determines the launch order and timing priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned planning department, Improving the accuracy of the plan by taking geographical launch patterns into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned planning department, We provide real-time updates on launch order and timing results, offering the latest information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 weather forecasting department predicts weather conditions, A characteristic analysis unit analyzes the characteristics of the rocket and payload based on the weather conditions predicted by the weather forecasting unit, An orbital calculation unit calculates the optimal launch window and orbital insertion parameters based on the characteristics analyzed by the characteristic analysis unit, The system includes a planning unit that plans the launch order and timing of multiple small satellites based on the launch window and orbit insertion parameters calculated by the orbit calculation unit. A system characterized by the following features.

2. The aforementioned weather forecasting unit, Predicting weather conditions using weather forecasting AI The system according to feature 1.

3. The characteristic analysis unit is, Analyze the characteristics of the rocket and payload. The system according to feature 1.

4. The aforementioned orbital calculation unit, Calculate the optimal launch window and orbit insertion parameters. The system according to feature 1.

5. The aforementioned planning department, Plan the launch sequence and timing of multiple small satellites. The system according to feature 1.

6. The aforementioned weather forecasting unit, The system estimates the user's emotions and adjusts how weather forecasts are displayed based on those emotions. The system according to feature 1.

7. The aforementioned weather forecasting unit, By referring to past weather data, we can improve the accuracy of our forecasts. The system according to feature 1.

8. The aforementioned weather forecasting unit, It performs a risk assessment for specific weather conditions and highlights high-risk conditions. The system according to feature 1.

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