system

Abstract

The system according to this embodiment aims to predict the location of space debris, correct the orbit of artificial satellites, and remove the debris. [Solution] The system according to the embodiment comprises an observation unit, a prediction unit, an avoidance unit, and a removal unit. The observation unit collects position data of space debris. The prediction unit analyzes the data collected by the observation unit and predicts the future position of the debris. The avoidance unit corrects the orbit of the satellite based on the prediction data obtained by the prediction unit. The removal unit captures and removes the debris based on the prediction data obtained by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, measures for preventing accidents caused by collisions between space debris and artificial satellites or falling objects to the earth have not been fully taken, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the position of space debris, correct the orbit of an artificial satellite, and remove the debris.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an observation unit, a prediction unit, an avoidance unit, and a removal unit. The observation unit collects position data of space debris. The prediction unit analyzes the data collected by the observation unit and predicts the future position of the debris. The avoidance unit corrects the orbit of the satellite based on the prediction data obtained by the prediction unit. The removal unit captures and removes the debris based on the prediction data obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the location of space debris, correct the orbit of a satellite, and remove the debris. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The space debris management system according to an embodiment of the present invention is a system that prevents collisions between space debris (space junk) and artificial satellites in outer space and efficiently removes the debris. This space debris management system uses multiple AI agents to observe and model the environment of space debris and improve prediction accuracy. This makes it possible to accurately grasp the location and movement of space debris. For example, an AI agent collects location data of the debris and performs simulations to predict the future location of the debris. Next, based on the obtained simulation data, an active artificial satellite autonomously takes evasive action. Specifically, the AI ​​analyzes the location of the debris and calculates the optimal evasive route. This allows the artificial satellite to avoid collision with the debris. For example, the AI ​​detects the approach of debris and issues instructions to the artificial satellite to correct its orbit. Furthermore, a recovery vehicle autonomously cleans up the debris. The recovery vehicle is controlled by AI and efficiently collects the debris. For example, the recovery vehicle identifies the location of the debris, extends its arm to capture the debris. The captured debris is released into the atmosphere and removed by burning. This system will improve the safety of outer space and stabilize the operation of artificial satellites. For example, collisions caused by space debris will decrease, and the lifespan of satellites will be extended. In addition, the environment of outer space will improve as debris removal progresses. In this way, the space debris management system can improve the safety of outer space and stabilize the operation of artificial satellites.

[0029] The space debris management system according to this embodiment comprises an observation unit, a prediction unit, an avoidance unit, and a removal unit. The observation unit collects location data of space debris. The observation unit uses, for example, radar and optical sensors to collect location data of debris. The observation unit can collect location data of debris in real time and store it in a database. The observation unit can, for example, measure the location of debris using radar and store that data in a database. The observation unit can also measure the location of debris using optical sensors and store that data in a database. The prediction unit analyzes the data collected by the observation unit and predicts the future location of the debris. The prediction unit predicts the future location of debris using, for example, a machine learning algorithm. The prediction unit uses a prediction model that takes location data of debris as input and outputs the future location. The prediction unit can also predict the future location of debris using numerical simulation. The avoidance unit corrects the satellite's orbit based on the prediction data obtained by the prediction unit. The avoidance unit, for example, analyzes the location data of the debris and calculates the optimal avoidance route. The avoidance unit uses the propulsion system to correct the satellite's orbit. The avoidance unit can also calculate the timing of orbital changes and issue instructions to the satellite. The removal unit captures and removes the debris based on the prediction data obtained by the prediction unit. The removal unit, for example, uses a robotic arm to capture the debris. After capturing the debris, the removal unit releases it into the atmosphere and burns it up. The removal unit can also use a laser to remove the debris. As a result, the space debris management system according to this embodiment can improve the safety of outer space by collecting and predicting the location data of space debris, correcting the orbits of artificial satellites, and removing the debris.

[0030] The observation unit collects location data of space debris. For example, the observation unit uses radar and optical sensors to collect debris location data. Specifically, radar identifies the location of debris by emitting electromagnetic waves and receiving the reflected waves. Radar can measure the location of debris over a wide area with high precision and is particularly effective in low Earth orbit. Optical sensors detect reflected light from debris to determine its location. Optical sensors can observe the shape and size of debris in detail and are particularly effective in geostationary orbit. By using these sensors in combination, the observation unit can collect debris location data in real time and store it in a database. For example, the observation unit can measure the location of debris using radar and store that data in the database. It can also measure the location of debris using optical sensors and store that data in the database. The observation unit centrally manages this data and can collaborate with other systems and departments as needed. For example, observed data can be stored on a cloud server and made accessible to the prediction and avoidance units. Furthermore, the observation unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the observation unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The prediction unit analyzes data collected by the observation unit to predict the future position of the debris. For example, the prediction unit uses machine learning algorithms to predict the future position of the debris. Specifically, the prediction unit uses a prediction model that takes debris position data as input and outputs the future position. The machine learning model learns from past data and understands the patterns of debris orbital variations, enabling it to predict the future position with high accuracy. The prediction unit can also predict the future position of the debris using numerical simulations. Numerical simulations predict the future position by solving the equations of motion of the debris and are particularly effective for debris with complex orbital variations. By combining these methods, the prediction unit can predict the future position of the debris with high accuracy, ensuring the safety of satellites and space stations. Furthermore, the prediction unit can continuously revise its prediction results based on real-time updated data, adapting to the latest situations. For example, if new debris location data is observed, the prediction unit immediately incorporates the new data and updates the prediction results. Furthermore, the prediction unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the prediction unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The avoidance unit corrects the satellite's orbit based on the prediction data obtained by the prediction unit. For example, the avoidance unit analyzes the location data of debris and calculates the optimal avoidance route. Specifically, the avoidance unit compares the satellite's current orbit with the predicted orbit of the debris and calculates the optimal avoidance route if there is a risk of collision. The avoidance unit uses the propulsion system to correct the satellite's orbit. The propulsion system controls the thrusters onboard the satellite and fine-tunes the orbit to avoid collisions with debris. For example, the avoidance unit corrects the satellite's orbit using the propulsion system. The avoidance unit can also calculate the timing of orbital changes and issue instructions to the satellite. For example, the avoidance unit calculates the timing of orbital changes and issues instructions to the satellite. By automating these operations, the avoidance unit can perform orbital corrections quickly and accurately. Furthermore, the avoidance unit can optimize overall operational efficiency by considering coordination with multiple satellites and space stations. For example, if multiple satellites perform orbital corrections simultaneously, the avoidance unit coordinates the orbital changes of each satellite to minimize the risk of collision. Furthermore, the avoidance unit works in conjunction with the ground control center to share information in real time, supporting rapid decision-making. This allows the avoidance unit to ensure the safety of satellites and space stations and improve the operational efficiency of space.

[0033] The removal unit captures and removes debris based on prediction data obtained by the prediction unit. The removal unit captures debris using, for example, a robotic arm. Specifically, the removal unit controls the robotic arm to approach and capture the debris. The robotic arm is equipped with a high-precision control system and can flexibly adapt to the shape and size of the debris. After capturing the debris, the removal unit releases it into the atmosphere and burns it up. The debris burns up at high temperatures upon re-entering the atmosphere and completely disappears. The removal unit can also remove debris using lasers. Lasers irradiate the debris with high-power light, causing it to vaporize. By combining these methods, the removal unit can efficiently and effectively remove debris and improve space safety. Furthermore, the removal unit can improve work efficiency by automating the debris removal process. For example, the debris removal unit uses AI to recognize the location and shape of the debris and automatically selects the optimal removal method. Furthermore, the unit can plan the simultaneous removal of multiple debris objects and proceed with the work efficiently. This allows the debris removal unit to play a crucial role in ensuring the safety of outer space and realizing sustainable space use.

[0034] The removal unit can capture debris, release it into the atmosphere, and burn it up. For example, the removal unit can capture debris, release it into the atmosphere, and burn it up. The removal unit can capture debris, change its orbit, and release it into the atmosphere. Alternatively, the removal unit can capture debris, change its orbit, and release it into the atmosphere. Furthermore, the removal unit can capture debris and release it into the atmosphere using its propulsion system. This allows for efficient removal of captured debris by releasing it into the atmosphere and burning it up.

[0035] The observation unit can select the optimal observation method by referring to past observation data when collecting debris position data. For example, the observation unit can identify periods of active debris movement from past observation data and concentrate observations during those periods. The observation unit can analyze past observation data and select the most effective observation method. For example, the observation unit can optimize the timing and frequency of observations based on past observation data. This allows for the selection of the optimal observation method and improvement of observation accuracy by referring to past observation data. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0036] The observation unit can adjust the accuracy of its observations based on the size and shape of the debris when collecting location data. For example, the observation unit performs high-precision observations for large debris to collect detailed location data. For small debris, the observation unit reduces the accuracy of its observations to collect data over a wider area. For example, the observation unit adjusts the observation angle and distance according to the shape of the debris. By adjusting the accuracy of observations based on the size and shape of the debris, more accurate data can be collected. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0037] When collecting debris location data, the observation unit can prioritize the collection of highly relevant data by considering the geographical location information of the debris. For example, if debris is near a satellite, the observation unit will prioritize the collection of that data. If debris has a possibility of falling to Earth, the observation unit can prioritize the collection of that data. For example, if debris has a possibility of colliding with other debris, the observation unit will prioritize the collection of that data. In this way, by considering the geographical location information of the debris, highly relevant data can be prioritized. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0038] The observation unit can analyze the orbital information of debris and collect relevant data when collecting location data of debris. For example, the observation unit can analyze the orbit of debris and collect data to predict its future position. Based on the orbital information of debris, the observation unit can collect data to assess the risk of collision with other debris. For example, the observation unit can collect data to plan evasive actions for artificial satellites based on the orbital information of debris. In this way, by analyzing the orbital information of debris, relevant data can be collected and the accuracy of predictions can be improved. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0039] The prediction unit can optimize its prediction algorithm by referring to past prediction data when predicting the future location of debris. For example, the prediction unit can analyze past prediction data to improve the accuracy of the prediction algorithm. The prediction unit can adjust the parameters of the prediction algorithm based on past prediction data. For example, the prediction unit can identify areas for improvement in the prediction algorithm by referring to past prediction data. In this way, by referring to past prediction data, the prediction algorithm can be optimized and the accuracy of the prediction can be improved. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without using AI.

[0040] The prediction unit can adjust the accuracy of its predictions based on the debris's velocity and direction when predicting the future position of the debris. For example, if the debris is moving at a high speed, the prediction unit uses more detailed data to improve the accuracy of the prediction. If the debris's direction is likely to change, the prediction unit can update the data more frequently to improve the accuracy of the prediction. For example, the prediction unit adjusts the timing of the prediction depending on the debris's velocity and direction. By adjusting the accuracy of the prediction based on the debris's velocity and direction, it is possible to provide more accurate prediction results. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI.

[0041] The prediction unit can improve the accuracy of its predictions by considering the geographical location information of the debris when predicting its future position. For example, the prediction unit can improve its prediction accuracy if the debris is near a satellite. The prediction unit can improve its prediction accuracy if there is a possibility that the debris will fall to Earth. For example, the prediction unit can improve its prediction accuracy if there is a possibility that the debris will collide with other debris. In this way, the accuracy of the prediction can be improved by considering the geographical location information of the debris. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI.

[0042] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the debris when predicting the future location of the debris. For example, the prediction unit can improve the accuracy of its prediction algorithm by referring to relevant literature on the debris. The prediction unit can adjust the parameters of its prediction algorithm based on relevant literature on the debris. For example, the prediction unit can identify areas for improvement in its prediction algorithm by referring to relevant literature on the debris. In this way, the accuracy of predictions can be improved by referring to relevant literature on the debris. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI.

[0043] The avoidance unit can select the optimal avoidance method by referring to past avoidance data when correcting the orbit of an artificial satellite. For example, the avoidance unit analyzes past avoidance data and selects the optimal avoidance method. Based on past avoidance data, the avoidance unit can adjust the parameters of the avoidance action. For example, the avoidance unit identifies areas for improvement in the avoidance action by referring to past avoidance data. In this way, by referring to past avoidance data, the optimal avoidance method can be selected and the accuracy of the avoidance action can be improved. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without using AI.

[0044] The avoidance unit can adjust the accuracy of its avoidance based on the approaching speed and direction of the debris when correcting the satellite's orbit. For example, if the debris is approaching at a high speed, the avoidance unit uses detailed data to improve the accuracy of its avoidance. If the direction of the debris is likely to change, the avoidance unit can frequently update the data to improve the accuracy of its avoidance. For example, the avoidance unit adjusts the timing of its avoidance actions according to the approaching speed and direction of the debris. This allows for more accurate avoidance actions by adjusting the accuracy of the avoidance based on the approaching speed and direction of the debris. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without AI.

[0045] The avoidance unit can optimize avoidance actions by considering the geographical location information of debris when correcting the orbit of a satellite. For example, the avoidance unit may prioritize avoidance actions if debris is near the satellite. The avoidance unit may also prioritize avoidance actions if there is a possibility that the debris will fall to Earth. For example, the avoidance unit may also prioritize avoidance actions if there is a possibility that the debris will collide with other debris. By considering the geographical location information of the debris, the avoidance unit can optimize avoidance actions and reduce the risk of collision. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without using AI.

[0046] The avoidance unit can improve the accuracy of its avoidance actions by referring to relevant literature on debris when correcting the satellite's orbit. For example, the avoidance unit can improve the accuracy of its avoidance actions by referring to relevant literature on debris. The avoidance unit can adjust the parameters of its avoidance actions based on relevant literature on debris. For example, the avoidance unit can identify areas for improvement in its avoidance actions by referring to relevant literature on debris. In this way, the accuracy of avoidance actions can be improved by referring to relevant literature on debris. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without using AI.

[0047] The removal unit can select the optimal removal method by referring to past removal data when capturing and removing debris. For example, the removal unit analyzes past removal data and selects the optimal removal method. The removal unit can adjust the parameters of the removal action based on past removal data. For example, the removal unit identifies areas for improvement in the removal action by referring to past removal data. In this way, by referring to past removal data, the optimal removal method can be selected and the accuracy of the removal action can be improved. Some or all of the above processing in the removal unit may be performed using AI, for example, or without using AI.

[0048] The removal unit can adjust the accuracy of removal based on the size and shape of the debris when capturing and removing it. For example, for large debris, the removal unit performs high-precision removal and uses detailed positional data. For small debris, the removal unit reduces the removal accuracy and can collect data over a wider area. For example, the removal unit adjusts the removal method and timing according to the shape of the debris. This allows for more accurate removal actions by adjusting the removal accuracy based on the size and shape of the debris. Some or all of the above processing in the removal unit may be performed using AI, for example, or without AI.

[0049] The removal unit can optimize its removal actions by considering the geographical location of the debris when capturing and removing it. For example, the removal unit may prioritize the removal of debris if it is near a satellite. The removal unit may also prioritize the removal of debris if there is a possibility of it falling to Earth. For example, the removal unit may also prioritize the removal of debris if there is a possibility of it colliding with other debris. By considering the geographical location of the debris, the removal actions can be optimized and the debris can be removed efficiently. Some or all of the above processing in the removal unit may be performed using AI, for example, or without using AI.

[0050] The removal unit can improve the accuracy of its removal actions by referring to relevant literature on the debris when capturing and removing it. For example, the removal unit can improve the accuracy of its removal actions by referring to relevant literature on the debris. The removal unit can adjust the parameters of its removal actions based on relevant literature on the debris. For example, the removal unit can identify areas for improvement in its removal actions by referring to relevant literature on the debris. In this way, the accuracy of the removal actions can be improved by referring to relevant literature on the debris. Some or all of the above processing in the removal unit may be performed using AI, for example, or without using AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The observation unit can adjust its observation method based on the material of the debris when collecting location data. For example, the observation unit can use radar to perform high-precision observations of metallic debris. For example, the observation unit can use optical sensors to perform observations of plastic debris. The observation unit adjusts the observation frequency and intensity according to the material of the debris. By adjusting the observation method based on the material of the debris, more accurate data can be collected.

[0053] The prediction unit can adjust its prediction algorithm based on the shape of the debris when predicting its future position. For example, for spherical debris, the prediction unit uses a prediction model that takes air resistance into account. For elongated debris, the prediction unit can use a prediction model that takes rotational motion into account. The prediction unit adjusts the prediction parameters according to the shape of the debris, for example. By adjusting the prediction algorithm based on the shape of the debris, it is possible to provide more accurate prediction results.

[0054] The avoidance unit can adjust the precision of its avoidance actions based on the mass of the debris when correcting the satellite's orbit. For example, for large-mass debris, the avoidance unit uses detailed data to increase the precision of its avoidance actions. For small-mass debris, the avoidance unit can reduce the precision of its avoidance actions and collect data over a wider area. For example, the avoidance unit adjusts the timing of its avoidance actions according to the mass of the debris. This allows for more accurate avoidance actions by adjusting the precision of the avoidance actions based on the mass of the debris.

[0055] The removal unit can adjust the removal method based on the temperature of the debris when capturing and removing it. For example, for high-temperature debris, the removal unit can use a cooling system to lower its temperature before capturing it. For low-temperature debris, the removal unit can capture it while maintaining its temperature. For example, the removal unit adjusts the timing and method of removal according to the temperature of the debris. By adjusting the removal method based on the temperature of the debris, debris can be removed more safely and efficiently.

[0056] The observation unit can adjust its observation method based on the electromagnetic reflection characteristics of the debris when collecting location data. For example, the observation unit can use radar to perform high-precision observations of highly reflective debris. For low-reflective debris, the observation unit can use optical sensors. The observation unit adjusts the observation frequency and intensity according to the electromagnetic reflection characteristics of the debris. By adjusting the observation method based on the electromagnetic reflection characteristics of the debris, more accurate data can be collected.

[0057] The observation unit can adjust its observation method based on the radiation level of the debris when collecting location data. For example, for debris with high radiation levels, the observation unit can perform remote observation to ensure safety. For debris with low radiation levels, the observation unit can perform close-range observation to collect detailed data. For example, the observation unit adjusts the observation distance and method according to the radiation level of the debris. By adjusting the observation method based on the radiation level of the debris, safer and more accurate data can be collected.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The observation unit collects space debris location data. The observation unit collects debris location data using, for example, radar and optical sensors, and stores it in a database in real time. Step 2: The prediction unit analyzes the data collected by the observation unit and predicts the future location of the debris. The prediction unit predicts the future location of the debris using, for example, machine learning algorithms or numerical simulations. Step 3: The avoidance unit corrects the satellite's orbit based on the prediction data obtained by the prediction unit. For example, the avoidance unit analyzes the location data of the debris, calculates the optimal avoidance route, and corrects the satellite's orbit using the propulsion system. Step 4: The removal unit captures and removes the debris based on the prediction data obtained by the prediction unit. The removal unit, for example, uses robotic arms or lasers to capture the debris and release it into the atmosphere to burn it up.

[0060] (Example of form 2) The space debris management system according to an embodiment of the present invention is a system that prevents collisions between space debris (space junk) and artificial satellites in outer space and efficiently removes the debris. This space debris management system uses multiple AI agents to observe and model the environment of space debris and improve prediction accuracy. This makes it possible to accurately grasp the location and movement of space debris. For example, an AI agent collects location data of the debris and performs simulations to predict the future location of the debris. Next, based on the obtained simulation data, an active artificial satellite autonomously takes evasive action. Specifically, the AI ​​analyzes the location of the debris and calculates the optimal evasive route. This allows the artificial satellite to avoid collision with the debris. For example, the AI ​​detects the approach of debris and issues instructions to the artificial satellite to correct its orbit. Furthermore, a recovery vehicle autonomously cleans up the debris. The recovery vehicle is controlled by AI and efficiently collects the debris. For example, the recovery vehicle identifies the location of the debris, extends its arm to capture the debris. The captured debris is released into the atmosphere and removed by burning. This system will improve the safety of outer space and stabilize the operation of artificial satellites. For example, collisions caused by space debris will decrease, and the lifespan of satellites will be extended. In addition, the environment of outer space will improve as debris removal progresses. In this way, the space debris management system can improve the safety of outer space and stabilize the operation of artificial satellites.

[0061] The space debris management system according to this embodiment comprises an observation unit, a prediction unit, an avoidance unit, and a removal unit. The observation unit collects location data of space debris. The observation unit uses, for example, radar and optical sensors to collect location data of debris. The observation unit can collect location data of debris in real time and store it in a database. The observation unit can, for example, measure the location of debris using radar and store that data in a database. The observation unit can also measure the location of debris using optical sensors and store that data in a database. The prediction unit analyzes the data collected by the observation unit and predicts the future location of the debris. The prediction unit predicts the future location of debris using, for example, a machine learning algorithm. The prediction unit uses a prediction model that takes location data of debris as input and outputs the future location. The prediction unit can also predict the future location of debris using numerical simulation. The avoidance unit corrects the satellite's orbit based on the prediction data obtained by the prediction unit. The avoidance unit, for example, analyzes the location data of the debris and calculates the optimal avoidance route. The avoidance unit uses the propulsion system to correct the satellite's orbit. The avoidance unit can also calculate the timing of orbital changes and issue instructions to the satellite. The removal unit captures and removes the debris based on the prediction data obtained by the prediction unit. The removal unit, for example, uses a robotic arm to capture the debris. After capturing the debris, the removal unit releases it into the atmosphere and burns it up. The removal unit can also use a laser to remove the debris. As a result, the space debris management system according to this embodiment can improve the safety of outer space by collecting and predicting the location data of space debris, correcting the orbits of artificial satellites, and removing the debris.

[0062] The observation unit collects location data of space debris. For example, the observation unit uses radar and optical sensors to collect debris location data. Specifically, radar identifies the location of debris by emitting electromagnetic waves and receiving the reflected waves. Radar can measure the location of debris over a wide area with high precision and is particularly effective in low Earth orbit. Optical sensors detect reflected light from debris to determine its location. Optical sensors can observe the shape and size of debris in detail and are particularly effective in geostationary orbit. By using these sensors in combination, the observation unit can collect debris location data in real time and store it in a database. For example, the observation unit can measure the location of debris using radar and store that data in the database. It can also measure the location of debris using optical sensors and store that data in the database. The observation unit centrally manages this data and can collaborate with other systems and departments as needed. For example, observed data can be stored on a cloud server and made accessible to the prediction and avoidance units. Furthermore, the observation unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the observation unit to collect data efficiently and effectively, improving the overall system performance.

[0063] The prediction unit analyzes data collected by the observation unit to predict the future position of the debris. For example, the prediction unit uses machine learning algorithms to predict the future position of the debris. Specifically, the prediction unit uses a prediction model that takes debris position data as input and outputs the future position. The machine learning model learns from past data and understands the patterns of debris orbital variations, enabling it to predict the future position with high accuracy. The prediction unit can also predict the future position of the debris using numerical simulations. Numerical simulations predict the future position by solving the equations of motion of the debris and are particularly effective for debris with complex orbital variations. By combining these methods, the prediction unit can predict the future position of the debris with high accuracy, ensuring the safety of satellites and space stations. Furthermore, the prediction unit can continuously revise its prediction results based on real-time updated data, adapting to the latest situations. For example, if new debris location data is observed, the prediction unit immediately incorporates the new data and updates the prediction results. Furthermore, the prediction unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the prediction unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0064] The avoidance unit corrects the satellite's orbit based on the prediction data obtained by the prediction unit. For example, the avoidance unit analyzes the location data of debris and calculates the optimal avoidance route. Specifically, the avoidance unit compares the satellite's current orbit with the predicted orbit of the debris and calculates the optimal avoidance route if there is a risk of collision. The avoidance unit uses the propulsion system to correct the satellite's orbit. The propulsion system controls the thrusters onboard the satellite and fine-tunes the orbit to avoid collisions with debris. For example, the avoidance unit corrects the satellite's orbit using the propulsion system. The avoidance unit can also calculate the timing of orbital changes and issue instructions to the satellite. For example, the avoidance unit calculates the timing of orbital changes and issues instructions to the satellite. By automating these operations, the avoidance unit can perform orbital corrections quickly and accurately. Furthermore, the avoidance unit can optimize overall operational efficiency by considering coordination with multiple satellites and space stations. For example, if multiple satellites perform orbital corrections simultaneously, the avoidance unit coordinates the orbital changes of each satellite to minimize the risk of collision. Furthermore, the avoidance unit works in conjunction with the ground control center to share information in real time, supporting rapid decision-making. This allows the avoidance unit to ensure the safety of satellites and space stations and improve the operational efficiency of space.

[0065] The removal unit captures and removes debris based on prediction data obtained by the prediction unit. The removal unit captures debris using, for example, a robotic arm. Specifically, the removal unit controls the robotic arm to approach and capture the debris. The robotic arm is equipped with a high-precision control system and can flexibly adapt to the shape and size of the debris. After capturing the debris, the removal unit releases it into the atmosphere and burns it up. The debris burns up at high temperatures upon re-entering the atmosphere and completely disappears. The removal unit can also remove debris using lasers. Lasers irradiate the debris with high-power light, causing it to vaporize. By combining these methods, the removal unit can efficiently and effectively remove debris and improve space safety. Furthermore, the removal unit can improve work efficiency by automating the debris removal process. For example, the debris removal unit uses AI to recognize the location and shape of the debris and automatically selects the optimal removal method. Furthermore, the unit can plan the simultaneous removal of multiple debris objects and proceed with the work efficiently. This allows the debris removal unit to play a crucial role in ensuring the safety of outer space and realizing sustainable space use.

[0066] The removal unit can capture debris, release it into the atmosphere, and burn it up. For example, the removal unit can capture debris, release it into the atmosphere, and burn it up. The removal unit can capture debris, change its orbit, and release it into the atmosphere. Alternatively, the removal unit can capture debris, change its orbit, and release it into the atmosphere. Furthermore, the removal unit can capture debris and release it into the atmosphere using its propulsion system. This allows for efficient removal of captured debris by releasing it into the atmosphere and burning it up.

[0067] The observation unit can estimate the user's emotions when collecting debris location data and adjust the timing of observations based on the estimated user emotions. For example, if the user is stressed, the observation unit can increase the frequency of observations and collect data in real time. If the user is relaxed, the observation unit can decrease the frequency of observations and collect data periodically. If the user is in a hurry, for example, the observation unit can shorten the timing of observations and collect data quickly. This allows for data collection at a more appropriate time by adjusting the timing of observations 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 observation unit may be performed using AI, for example, or without AI.

[0068] The observation unit can select the optimal observation method by referring to past observation data when collecting debris position data. For example, the observation unit can identify periods of active debris movement from past observation data and concentrate observations during those periods. The observation unit can analyze past observation data and select the most effective observation method. For example, the observation unit can optimize the timing and frequency of observations based on past observation data. This allows for the selection of the optimal observation method and improvement of observation accuracy by referring to past observation data. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0069] The observation unit can adjust the accuracy of its observations based on the size and shape of the debris when collecting location data. For example, the observation unit performs high-precision observations for large debris to collect detailed location data. For small debris, the observation unit reduces the accuracy of its observations to collect data over a wider area. For example, the observation unit adjusts the observation angle and distance according to the shape of the debris. By adjusting the accuracy of observations based on the size and shape of the debris, more accurate data can be collected. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0070] The observation unit can estimate the user's emotions when collecting debris location data and determine observation priorities based on the estimated user emotions. For example, if the user is stressed, the observation unit will prioritize observing important debris. If the user is relaxed, the observation unit can perform a general observation and collect data. For example, if the user is in a hurry, the observation unit will prioritize observing the most dangerous debris. This ensures that important debris is observed by prioritizing observations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the observation unit may be performed using AI or not using AI.

[0071] When collecting debris location data, the observation unit can prioritize the collection of highly relevant data by considering the geographical location information of the debris. For example, if debris is near a satellite, the observation unit will prioritize the collection of that data. If debris has a possibility of falling to Earth, the observation unit can prioritize the collection of that data. For example, if debris has a possibility of colliding with other debris, the observation unit will prioritize the collection of that data. In this way, by considering the geographical location information of the debris, highly relevant data can be prioritized. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0072] The observation unit can analyze the orbital information of debris and collect relevant data when collecting location data of debris. For example, the observation unit can analyze the orbit of debris and collect data to predict its future position. Based on the orbital information of debris, the observation unit can collect data to assess the risk of collision with other debris. For example, the observation unit can collect data to plan evasive actions for artificial satellites based on the orbital information of debris. In this way, by analyzing the orbital information of debris, relevant data can be collected and the accuracy of predictions can be improved. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI.

[0073] The prediction unit can estimate the user's emotions when predicting the future location of debris and adjust the way the prediction is presented based on the estimated user emotions. For example, if the user is tense, the prediction unit can provide a simple and easy-to-understand prediction result. If the user is relaxed, the prediction unit can provide a detailed prediction result. If the user is in a hurry, the prediction unit can provide a concise prediction result. By adjusting the way the prediction is presented according to the user's emotions, more appropriate prediction results can be provided. 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 prediction unit may be performed using AI, for example, or without AI.

[0074] The prediction unit can optimize its prediction algorithm by referring to past prediction data when predicting the future location of debris. For example, the prediction unit can analyze past prediction data to improve the accuracy of the prediction algorithm. The prediction unit can adjust the parameters of the prediction algorithm based on past prediction data. For example, the prediction unit can identify areas for improvement in the prediction algorithm by referring to past prediction data. In this way, by referring to past prediction data, the prediction algorithm can be optimized and the accuracy of the prediction can be improved. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without using AI.

[0075] The prediction unit can adjust the accuracy of its predictions based on the debris's velocity and direction when predicting the future position of the debris. For example, if the debris is moving at a high speed, the prediction unit uses more detailed data to improve the accuracy of the prediction. If the debris's direction is likely to change, the prediction unit can update the data more frequently to improve the accuracy of the prediction. For example, the prediction unit adjusts the timing of the prediction depending on the debris's velocity and direction. By adjusting the accuracy of the prediction based on the debris's velocity and direction, it is possible to provide more accurate prediction results. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI.

[0076] The prediction unit can estimate the user's emotions when predicting the future location of debris and determine prediction priorities based on the estimated user emotions. For example, if the user is stressed, the prediction unit will prioritize predictions of important debris. If the user is relaxed, the prediction unit can make an overall prediction and provide data. For example, if the user is in a hurry, the prediction unit will prioritize predictions of the most dangerous debris. This allows for prioritizing predictions of important debris by determining prediction priorities 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 prediction unit may be performed using AI, for example, or not using AI.

[0077] The prediction unit can improve the accuracy of its predictions by considering the geographical location information of the debris when predicting its future position. For example, the prediction unit can improve its prediction accuracy if the debris is near a satellite. The prediction unit can improve its prediction accuracy if there is a possibility that the debris will fall to Earth. For example, the prediction unit can improve its prediction accuracy if there is a possibility that the debris will collide with other debris. In this way, the accuracy of the prediction can be improved by considering the geographical location information of the debris. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI.

[0078] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the debris when predicting the future location of the debris. For example, the prediction unit can improve the accuracy of its prediction algorithm by referring to relevant literature on the debris. The prediction unit can adjust the parameters of its prediction algorithm based on relevant literature on the debris. For example, the prediction unit can identify areas for improvement in its prediction algorithm by referring to relevant literature on the debris. In this way, the accuracy of predictions can be improved by referring to relevant literature on the debris. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI.

[0079] The avoidance unit can estimate the user's emotions when correcting the satellite's orbit and adjust the timing of avoidance actions based on the estimated user emotions. For example, if the user is tense, the avoidance unit can speed up the timing of avoidance actions. If the user is relaxed, the avoidance unit can delay the timing of avoidance actions. For example, if the user is in a hurry, the avoidance unit can perform avoidance actions quickly. In this way, by adjusting the timing of avoidance actions according to the user's emotions, avoidance actions can be performed at a more appropriate time. 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 avoidance unit may be performed using AI, for example, or without using AI.

[0080] The avoidance unit can select the optimal avoidance method by referring to past avoidance data when correcting the orbit of an artificial satellite. For example, the avoidance unit analyzes past avoidance data and selects the optimal avoidance method. Based on past avoidance data, the avoidance unit can adjust the parameters of the avoidance action. For example, the avoidance unit identifies areas for improvement in the avoidance action by referring to past avoidance data. In this way, by referring to past avoidance data, the optimal avoidance method can be selected and the accuracy of the avoidance action can be improved. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without using AI.

[0081] The avoidance unit can adjust the accuracy of its avoidance based on the approaching speed and direction of the debris when correcting the satellite's orbit. For example, if the debris is approaching at a high speed, the avoidance unit uses detailed data to improve the accuracy of its avoidance. If the direction of the debris is likely to change, the avoidance unit can frequently update the data to improve the accuracy of its avoidance. For example, the avoidance unit adjusts the timing of its avoidance actions according to the approaching speed and direction of the debris. This allows for more accurate avoidance actions by adjusting the accuracy of the avoidance based on the approaching speed and direction of the debris. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without AI.

[0082] The avoidance unit can estimate the user's emotions when correcting the satellite's orbit and determine the priority of avoidance actions based on the estimated user emotions. For example, if the user is tense, the avoidance unit will prioritize important avoidance actions. If the user is relaxed, the avoidance unit can perform general avoidance actions. For example, if the user is in a hurry, the avoidance unit will prioritize the most dangerous avoidance actions. This allows for prioritizing important avoidance actions by determining the priority of avoidance actions 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 avoidance unit may be performed using AI, for example, or without AI.

[0083] The avoidance unit can optimize avoidance actions by considering the geographical location information of debris when correcting the orbit of a satellite. For example, the avoidance unit may prioritize avoidance actions if debris is near the satellite. The avoidance unit may also prioritize avoidance actions if there is a possibility that the debris will fall to Earth. For example, the avoidance unit may also prioritize avoidance actions if there is a possibility that the debris will collide with other debris. By considering the geographical location information of the debris, the avoidance unit can optimize avoidance actions and reduce the risk of collision. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without using AI.

[0084] The avoidance unit can improve the accuracy of its avoidance actions by referring to relevant literature on debris when correcting the satellite's orbit. For example, the avoidance unit can improve the accuracy of its avoidance actions by referring to relevant literature on debris. The avoidance unit can adjust the parameters of its avoidance actions based on relevant literature on debris. For example, the avoidance unit can identify areas for improvement in its avoidance actions by referring to relevant literature on debris. In this way, the accuracy of avoidance actions can be improved by referring to relevant literature on debris. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without using AI.

[0085] The removal unit can estimate the user's emotions when capturing and removing debris, and adjust the timing of the removal action based on the estimated user emotions. For example, if the user is tense, the removal unit can speed up the removal action. If the user is relaxed, the removal unit can delay the removal action. If the user is in a hurry, the removal unit can perform the removal action quickly. In this way, by adjusting the timing of the removal action according to the user's emotions, debris can be removed at a more appropriate time. 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 removal unit may be performed using AI, for example, or without using AI.

[0086] The removal unit can select the optimal removal method by referring to past removal data when capturing and removing debris. For example, the removal unit analyzes past removal data and selects the optimal removal method. The removal unit can adjust the parameters of the removal action based on past removal data. For example, the removal unit identifies areas for improvement in the removal action by referring to past removal data. In this way, by referring to past removal data, the optimal removal method can be selected and the accuracy of the removal action can be improved. Some or all of the above processing in the removal unit may be performed using AI, for example, or without using AI.

[0087] The removal unit can adjust the accuracy of removal based on the size and shape of the debris when capturing and removing it. For example, for large debris, the removal unit performs high-precision removal and uses detailed positional data. For small debris, the removal unit reduces the removal accuracy and can collect data over a wider area. For example, the removal unit adjusts the removal method and timing according to the shape of the debris. This allows for more accurate removal actions by adjusting the removal accuracy based on the size and shape of the debris. Some or all of the above processing in the removal unit may be performed using AI, for example, or without AI.

[0088] The removal unit can estimate the user's emotions when capturing and removing debris, and determine the priority of removal actions based on the estimated user emotions. For example, if the user is tense, the removal unit will prioritize the removal of critical debris. If the user is relaxed, the removal unit can perform overall removal actions and collect data. For example, if the user is in a hurry, the removal unit will prioritize the removal of the most dangerous debris. This ensures that critical debris is prioritized by determining the priority of removal actions 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 removal unit may be performed using AI, for example, or not using AI.

[0089] The removal unit can optimize its removal actions by considering the geographical location of the debris when capturing and removing it. For example, the removal unit may prioritize the removal of debris if it is near a satellite. The removal unit may also prioritize the removal of debris if there is a possibility of it falling to Earth. For example, the removal unit may also prioritize the removal of debris if there is a possibility of it colliding with other debris. By considering the geographical location of the debris, the removal actions can be optimized and the debris can be removed efficiently. Some or all of the above processing in the removal unit may be performed using AI, for example, or without using AI.

[0090] The removal unit can improve the accuracy of its removal actions by referring to relevant literature on the debris when capturing and removing it. For example, the removal unit can improve the accuracy of its removal actions by referring to relevant literature on the debris. The removal unit can adjust the parameters of its removal actions based on relevant literature on the debris. For example, the removal unit can identify areas for improvement in its removal actions by referring to relevant literature on the debris. In this way, the accuracy of the removal actions can be improved by referring to relevant literature on the debris. Some or all of the above processing in the removal unit may be performed using AI, for example, or without using AI.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The observation unit can adjust its observation method based on the material of the debris when collecting location data. For example, the observation unit can use radar to perform high-precision observations of metallic debris. For example, the observation unit can use optical sensors to perform observations of plastic debris. The observation unit adjusts the observation frequency and intensity according to the material of the debris. By adjusting the observation method based on the material of the debris, more accurate data can be collected.

[0093] The prediction unit can adjust its prediction algorithm based on the shape of the debris when predicting its future position. For example, for spherical debris, the prediction unit uses a prediction model that takes air resistance into account. For elongated debris, the prediction unit can use a prediction model that takes rotational motion into account. The prediction unit adjusts the prediction parameters according to the shape of the debris, for example. By adjusting the prediction algorithm based on the shape of the debris, it is possible to provide more accurate prediction results.

[0094] The avoidance unit can adjust the precision of its avoidance actions based on the mass of the debris when correcting the satellite's orbit. For example, for large-mass debris, the avoidance unit uses detailed data to increase the precision of its avoidance actions. For small-mass debris, the avoidance unit can reduce the precision of its avoidance actions and collect data over a wider area. For example, the avoidance unit adjusts the timing of its avoidance actions according to the mass of the debris. This allows for more accurate avoidance actions by adjusting the precision of the avoidance actions based on the mass of the debris.

[0095] The removal unit can adjust the removal method based on the temperature of the debris when capturing and removing it. For example, for high-temperature debris, the removal unit can use a cooling system to lower its temperature before capturing it. For low-temperature debris, the removal unit can capture it while maintaining its temperature. For example, the removal unit adjusts the timing and method of removal according to the temperature of the debris. By adjusting the removal method based on the temperature of the debris, debris can be removed more safely and efficiently.

[0096] The observation unit can adjust its observation method based on the electromagnetic reflection characteristics of the debris when collecting location data. For example, the observation unit can use radar to perform high-precision observations of highly reflective debris. For low-reflective debris, the observation unit can use optical sensors. The observation unit adjusts the observation frequency and intensity according to the electromagnetic reflection characteristics of the debris. By adjusting the observation method based on the electromagnetic reflection characteristics of the debris, more accurate data can be collected.

[0097] The observation unit can estimate the user's emotions when collecting debris location data and adjust the accuracy of the observations based on the estimated emotions. For example, if the user is stressed, the observation unit will increase the accuracy of the observations and collect more detailed data. If the user is relaxed, the observation unit will decrease the accuracy of the observations and collect data over a wider area. For example, if the user is in a hurry, the observation unit will adjust the accuracy of the observations to collect data quickly. In this way, by adjusting the accuracy of the observations according to the user's emotions, more appropriate data can be collected.

[0098] The prediction unit can estimate the user's emotions when predicting the future location of debris and adjust the frequency of predictions based on the estimated user emotions. For example, if the user is stressed, the prediction unit can increase the frequency of predictions and provide prediction results in real time. If the user is relaxed, the prediction unit can decrease the frequency of predictions and provide prediction results periodically. For example, if the user is in a hurry, the prediction unit can adjust the frequency of predictions to provide prediction results quickly. In this way, by adjusting the frequency of predictions according to the user's emotions, more appropriate prediction results can be provided.

[0099] The avoidance unit can estimate the user's emotions when correcting the satellite's orbit and adjust its avoidance actions based on those emotions. For example, if the user is tense, the avoidance unit will perform simple and rapid avoidance actions. If the user is relaxed, the avoidance unit can perform more detailed avoidance actions. For example, if the user is in a hurry, the avoidance unit will adjust its methods to perform rapid avoidance actions. This allows for more appropriate avoidance actions by adjusting the avoidance actions according to the user's emotions.

[0100] The removal unit can estimate the user's emotions when capturing and removing debris, and adjust its removal actions based on those emotions. For example, if the user is tense, the removal unit can perform quick and decisive removal actions. If the user is relaxed, the removal unit can perform detailed removal actions. For example, if the user is in a hurry, the removal unit can adjust its method to perform quick removal actions. This allows for more appropriate removal actions by adjusting the removal method according to the user's emotions.

[0101] The observation unit can adjust its observation method based on the radiation level of the debris when collecting location data. For example, for debris with high radiation levels, the observation unit can perform remote observation to ensure safety. For debris with low radiation levels, the observation unit can perform close-range observation to collect detailed data. For example, the observation unit adjusts the observation distance and method according to the radiation level of the debris. By adjusting the observation method based on the radiation level of the debris, safer and more accurate data can be collected.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The observation unit collects space debris location data. The observation unit collects debris location data using, for example, radar and optical sensors, and stores it in a database in real time. Step 2: The prediction unit analyzes the data collected by the observation unit and predicts the future location of the debris. The prediction unit predicts the future location of the debris using, for example, machine learning algorithms or numerical simulations. Step 3: The avoidance unit corrects the satellite's orbit based on the prediction data obtained by the prediction unit. For example, the avoidance unit analyzes the location data of the debris, calculates the optimal avoidance route, and corrects the satellite's orbit using the propulsion system. Step 4: The removal unit captures and removes the debris based on the prediction data obtained by the prediction unit. The removal unit, for example, uses robotic arms or lasers to capture the debris and release it into the atmosphere to burn it up.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the observation unit, prediction unit, avoidance unit, and removal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the observation unit collects debris location data using the camera 42 and radar of the smart device 14 and stores it in a database by the control unit 46A. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts the future position of the debris using a machine learning algorithm. The avoidance unit is implemented, for example, by the control unit 46A of the smart device 14 and analyzes the debris location data to correct the satellite's orbit. The removal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses a robotic arm to capture the debris and release it into the atmosphere to burn it up. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the observation unit, prediction unit, avoidance unit, and removal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the observation unit collects debris location data using the camera 42 and radar of the smart glasses 214 and stores it in a database by the control unit 46A. The prediction unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and predicts the future position of the debris using a machine learning algorithm. The avoidance unit is implemented, for example, in the control unit 46A of the smart glasses 214 and analyzes the debris location data to correct the satellite's orbit. The removal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and uses a robotic arm to capture the debris and release it into the atmosphere to burn it up. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the observation unit, prediction unit, avoidance unit, and removal unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the observation unit collects debris position data using the camera 42 and radar of the headset terminal 314 and stores it in a database by the control unit 46A. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts the future position of the debris using a machine learning algorithm. The avoidance unit is implemented, for example, by the control unit 46A of the headset terminal 314 and analyzes the debris position data to correct the satellite's orbit. The removal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses a robotic arm to capture the debris and release it into the atmosphere to burn it up. 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.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the observation unit, prediction unit, avoidance unit, and removal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the observation unit collects debris position data using the camera 42 and radar of the robot 414 and stores it in a database by the control unit 46A. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts the future position of the debris using a machine learning algorithm. The avoidance unit is implemented, for example, by the control unit 46A of the robot 414 and analyzes the debris position data to correct the satellite's orbit. The removal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses a robotic arm to capture the debris and release it into the atmosphere to burn it up. 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] (Note 1) An observation unit that collects location data of space debris, A prediction unit analyzes the data collected by the observation unit and predicts the future position of the debris, An avoidance unit that corrects the orbit of the artificial satellite based on the prediction data obtained by the prediction unit, The system includes a removal unit that captures and removes debris based on prediction data obtained by the prediction unit. A system characterized by the following features. (Note 2) The removal section is, The captured debris is released into the atmosphere and burned up. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned observation unit is When collecting debris location data, the system estimates the user's emotions and adjusts the timing of observations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned observation unit is When collecting debris location data, the optimal observation method is selected by referring to past observation data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned observation unit is When collecting debris location data, the accuracy of the observations is adjusted based on the size and shape of the debris. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned observation unit is When collecting debris location data, the system estimates user sentiment and determines observation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned observation unit is When collecting debris location data, the geographical location information of the debris is taken into consideration, and the collection of highly relevant data is prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned observation unit is When collecting debris location data, the debris's orbital information is analyzed and related data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The prediction unit, When predicting the future location of debris, the system estimates user sentiment and adjusts the representation of the prediction based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The prediction unit, When predicting the future location of debris, we optimize the prediction algorithm by referring to historical prediction data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The prediction unit, When predicting the future location of debris, the accuracy of the prediction is adjusted based on the debris's velocity and direction. The system described in Appendix 1, characterized by the features described herein. (Note 12) The prediction unit, When predicting the future location of debris, the system estimates user sentiment and prioritizes predictions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, When predicting the future location of debris, consider the debris's geographical location to improve the accuracy of the prediction. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, When predicting the future location of debris, we improve the accuracy of predictions by referring to relevant literature on debris. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned avoidance section is When correcting the satellite's orbit, the system estimates the user's emotions and adjusts the timing of avoidance actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned avoidance section is When correcting the orbit of a satellite, the optimal avoidance method is selected by referring to past avoidance data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned avoidance section is When correcting a satellite's orbit, the accuracy of the avoidance is adjusted based on the approaching speed and direction of the debris. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned avoidance section is When correcting the satellite's orbit, the system estimates the user's emotions and determines the priority of avoidance actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned avoidance section is When correcting a satellite's orbit, evasive action is optimized by considering the geographical location of the debris. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned avoidance section is When correcting satellite orbits, we refer to relevant literature on debris to improve the accuracy of evasive maneuvers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The removal section is, When capturing and removing debris, the system estimates the user's emotions and adjusts the timing of the removal action based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The removal section is, When capturing and removing debris, the optimal removal method is selected by referring to past removal data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The removal section is, When capturing and removing debris, the precision of the removal is adjusted based on the size and shape of the debris. The system described in Appendix 1, characterized by the features described herein. (Note 24) The removal section is, When capturing and removing debris, the system estimates the user's emotions and determines the priority of removal actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The removal section is, When capturing and removing debris, the removal action is optimized by considering the geographical location of the debris. The system described in Appendix 1, characterized by the features described herein. (Note 26) The removal section is, When capturing and removing debris, we refer to relevant literature on debris to improve the accuracy of removal actions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0176] 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. An observation unit that collects location data of space debris, A prediction unit analyzes the data collected by the observation unit and predicts the future position of the debris, An avoidance unit that corrects the orbit of the artificial satellite based on the prediction data obtained by the prediction unit, The system includes a removal unit that captures and removes debris based on prediction data obtained by the prediction unit. A system characterized by the following features.

2. The removal section is, The captured debris is released into the atmosphere and burned up. The system according to feature 1.

3. The aforementioned observation unit is When collecting debris location data, the system estimates the user's emotions and adjusts the timing of observations based on the estimated emotions. The system according to feature 1.

4. The aforementioned observation unit is When collecting debris location data, the optimal observation method is selected by referring to past observation data. The system according to feature 1.

5. The aforementioned observation unit is When collecting debris location data, the accuracy of the observations is adjusted based on the size and shape of the debris. The system according to feature 1.

6. The aforementioned observation unit is When collecting debris location data, the system estimates user sentiment and determines observation priorities based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned observation unit is When collecting debris location data, the geographical location information of the debris is taken into consideration, and the collection of highly relevant data is prioritized. The system according to feature 1.

8. The aforementioned observation unit is When collecting debris location data, the debris's orbital information is analyzed and related data is collected. The system according to feature 1.

9. The prediction unit, When predicting the future location of debris, the system estimates user sentiment and adjusts the representation of the prediction based on that estimated sentiment. The system according to feature 1.

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