system

Abstract

The system according to this embodiment aims to quickly and effectively collect information and respond to disaster situations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an operation unit, and a mechanics unit. The collection unit collects information from the disaster site. The analysis unit analyzes the information collected by the collection unit and selects the optimal route. The operation unit remotely controls the robot based on the route selected by the analysis unit. The mechanics unit enables the robot, operated by the operation unit, to solve problems using powerful arms and cranes.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <9000025>In the conventional technology, there is a problem that information collection and response at a disaster site are not carried out quickly and effectively.

[0005] The system according to the embodiment aims to quickly and effectively perform information collection and response at a disaster site.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an operation unit, and a mechanics unit. The data collection unit collects information from the disaster site. The analysis unit analyzes the information collected by the data collection unit and selects the optimal route. The operation unit remotely controls the robot based on the route selected by the analysis unit. The mechanics unit enables the robot, operated by the operation unit, to solve problems using powerful arms and cranes. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and effectively collect information and respond to disaster sites. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The disaster relief robot system according to an embodiment of the present invention is a system that utilizes an AI agent to support rescue operations at disaster sites. This disaster relief robot system can approach disaster sites. Disaster sites such as those affected by fires, earthquakes, and landslides are often difficult for humans to approach, but this robot, controlled by an AI agent, can move safely to the site while avoiding obstacles. For example, when moving through rubble, the AI ​​agent analyzes the surrounding situation in real time and selects the optimal route. Furthermore, this disaster relief robot system can be operated remotely. Disaster sites are dangerous, so it is desirable to avoid human entry into the site. This robot can be operated remotely by an operator from a safe location. For example, the operator can operate the robot's arm to remove rubble or rescue victims while viewing camera footage. Moreover, this disaster relief robot system can solve problems with strength exceeding human capabilities. There are many situations at disaster sites that cannot be handled by human strength alone. This robot is equipped with a powerful arm and crane, and can lift heavy rubble or move large obstacles. For example, a robot's powerful arm can be useful in rescuing victims trapped under collapsed buildings during an earthquake. In this way, disaster relief robot systems utilize AI agents to support rescue operations at disaster sites. This allows government agencies conducting emergency and disaster response to carry out rescue operations more safely and efficiently. As a result, disaster relief robot systems enable information gathering, analysis, remote operation, and solving mechanical challenges at disaster sites.

[0029] The disaster relief robot system according to this embodiment comprises a collection unit, an analysis unit, an operation unit, and a mechanics unit. The collection unit collects information from the disaster site. The collection unit can collect, for example, video information, audio information, and sensor data from the disaster site. For example, the collection unit can collect video of the disaster site in real time using a camera. The collection unit can also collect audio information from the disaster site using a microphone. Furthermore, the collection unit can also collect environmental information such as temperature, humidity, and atmospheric pressure using various sensors. The analysis unit analyzes the information collected by the collection unit and selects the optimal route. For example, the analysis unit analyzes the collected video information to identify the location and size of obstacles. The analysis unit can also analyze the collected audio information to identify the location of victims. Furthermore, the analysis unit can analyze the collected sensor data to understand the environmental conditions of the disaster site. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can select the optimal route using an AI model that takes the information collected by the collection unit as input and outputs the optimal route. The control unit remotely operates the robot based on a route selected by the analysis unit. The control unit allows, for example, an operator to remotely control the robot. The control unit sends and receives data between the robot and the operator using, for example, communication technology. The control unit also allows the operator to operate the robot using an operating interface. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities. The mechanics unit allows the robot, operated by the control unit, to solve problems using powerful arms and cranes. The mechanics unit can, for example, use a powerful arm to lift heavy rubble. The mechanics unit can, for example, use a crane to move large obstacles.Furthermore, the mechanics department can optimize the coordinated movement of the arm and crane, enabling the efficient execution of complex tasks. Some or all of the processes described above in the mechanics department may be performed using AI, for example, or not. For example, the mechanics department can perform tasks efficiently using an AI model that optimizes the movement of the arm and crane. This enables the disaster relief robot system according to the embodiment to collect and analyze information, operate remotely, and solve mechanical problems at disaster sites.

[0030] The data collection unit collects information from disaster sites. For example, it can collect video, audio, and sensor data from disaster sites. Specifically, the unit uses high-resolution cameras to collect video footage of disaster sites in real time. This allows operators to gain a detailed understanding of the situation at the site. The unit can also collect audio information from disaster sites using high-sensitivity microphones. This allows for the detection of victims' voices and ambient sounds, which can be used to aid in rescue operations. Furthermore, the unit can collect environmental information such as temperature, humidity, and atmospheric pressure using various sensors. For example, temperature sensors are useful for identifying fires and heat sources, while humidity sensors help in understanding flood conditions. Atmospheric pressure sensors provide data for assessing the risk of building collapse. This sensor data is crucial for gaining a detailed understanding of the environmental conditions at disaster sites. The data collection unit centrally manages this data and transmits it to a central database in real time. This allows the analysis and operation units to quickly access the necessary information. Additionally, the data collection unit can collect information over a wide area using drones and robots. Drones collect aerial video and sensor data, while ground-based robots collect information in confined or dangerous locations. This allows the collection unit to grasp the overall picture of the disaster site and support efficient rescue operations.

[0031] The analysis unit analyzes the information collected by the collection unit and selects the optimal route. For example, the analysis unit analyzes collected video information to identify the location and size of obstacles. Specifically, it utilizes AI-based image recognition technology to automatically detect obstacles in the video. This allows operators to quickly select the optimal route to avoid obstacles. The analysis unit can also analyze collected audio information to identify the location of victims. Using speech recognition technology, it detects the voices of victims and sounds calling for help and identifies their location. Furthermore, the analysis unit can analyze collected sensor data to understand the environmental conditions at the disaster site. For example, it can analyze temperature sensor data to identify the location of a fire, analyze humidity sensor data to understand the progression of flooding, and analyze barometric pressure sensor data to assess the risk of building collapse. This allows the analysis unit to gain a detailed understanding of the disaster site and provide information for selecting the optimal route. In addition, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past disaster data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The control unit remotely operates the robot based on the route selected by the analysis unit. For example, the control unit allows an operator to remotely control the robot. Specifically, the control unit uses communication technology to send and receive data between the robot and the operator. This allows the operator to remotely control the robot while understanding the situation at the disaster site in real time. The control unit also allows the operator to operate the robot using an operating interface. The operating interface features an intuitive touch panel and joystick, allowing the operator to easily control the robot. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, it can utilize an AI-based anomaly detection algorithm to automatically perform corrective actions if an abnormality is detected in the robot's movement. This allows the control unit to improve the robot's safety and reliability. The control unit can also control multiple robots simultaneously, supporting efficient work at disaster sites. For example, multiple robots working together can quickly collect information over a wide area and remove obstacles. This allows the control unit to effectively support rescue operations at disaster sites, contributing to the rescue of victims and mitigating disaster damage.

[0033] The Mechanics Department uses robots operated by the control unit to solve problems using powerful arms and cranes. For example, the Mechanics Department can lift heavy debris using its powerful arms. Specifically, the arms are equipped with high-precision motors and sensors, allowing them to accurately lift and move heavy objects. The Mechanics Department can also move large obstacles using its cranes. The cranes are equipped with long arms and powerful winches, allowing them to safely move objects from high places or distances. Furthermore, the Mechanics Department can optimize the coordinated movements of the arms and cranes to efficiently perform complex tasks. For example, it utilizes AI-based motion optimization algorithms to adjust the movements of the arms and cranes in real time. This allows the Mechanics Department to perform complex tasks quickly and accurately. The Mechanics Department is equipped with multiple tools and attachments to address various challenges in disaster sites. For example, the arms can be fitted with grippers for grasping debris and cutters for cutting. The cranes can be fitted with hooks and buckets for lifting heavy objects. This allows the Mechanics Department to adapt to various situations and perform tasks efficiently. Furthermore, the mechanics unit can monitor the progress of the work in real time and adjust its operation as needed. This allows the mechanics unit to work safely and efficiently at disaster sites and support rescue operations.

[0034] The data collection unit can collect information about obstacles at a disaster site. For example, the data collection unit collects information such as the location, size, and material of the obstacles. For example, the data collection unit can collect images of obstacles using a camera and identify their location and size using image analysis technology. The data collection unit can also detect the material of obstacles using sensors. For example, the data collection unit can measure the distance to obstacles using a laser sensor and acquire location information. Furthermore, the data collection unit can detect the shape of obstacles using an ultrasonic sensor. By collecting information about obstacles at a disaster site, the accuracy of the robot's motion plan can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired by a camera into a generating AI and have the generating AI identify the location and size of obstacles.

[0035] The analysis unit can analyze the collected information and select the optimal route to avoid obstacles. For example, the analysis unit can analyze collected video information to identify the location and size of obstacles. For example, the analysis unit can analyze collected audio information to identify the location of victims. Furthermore, the analysis unit can analyze collected sensor data to understand the environmental conditions of the disaster site. For example, the analysis unit can analyze collected temperature information to assess the risk of fire. The analysis unit can also analyze collected humidity information to assess the risk of flooding. Furthermore, the analysis unit can analyze the concentration of collected harmful gases to assess the risk of chemical substances. This enables the robot to move safely by selecting the optimal route to avoid obstacles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select the optimal route using an AI model that takes information collected by the collection unit as input and outputs the optimal route.

[0036] The control unit allows an operator to remotely control the robot. The control unit sends and receives data between the robot and the operator, for example, using communication technology. The control unit also allows the operator to operate the robot using, for example, an operating interface. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities. This allows the operator to remotely control the robot from a safe location. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities.

[0037] The Mechanics Department can lift heavy debris using powerful arms and cranes. For example, the Mechanics Department can lift heavy debris using powerful arms. The Mechanics Department can also move large obstacles using cranes. Furthermore, the Mechanics Department can optimize the coordinated movements of arms and cranes to efficiently perform complex tasks. For example, the Mechanics Department can optimize the movement of arms to efficiently lift heavy debris. The Mechanics Department can also optimize the movement of cranes to efficiently move large obstacles. Furthermore, the Mechanics Department can optimize the coordinated movements of arms and cranes to efficiently perform complex tasks. This makes it possible to rescue victims by lifting heavy debris. Some or all of the above processes in the Mechanics Department may be performed using AI, for example, or not. For example, the Mechanics Department can perform tasks efficiently using AI models that optimize the movements of arms and cranes.

[0038] The data collection unit can collect environmental information such as temperature and humidity at the disaster site and reflect it in the robot's movements. For example, the data collection unit can monitor the temperature at the disaster site in real time and adjust the robot's operating speed. The data collection unit can also measure the humidity at the disaster site and take protective measures for the robot's electronic equipment. Furthermore, the data collection unit can detect the concentration of harmful gases at the disaster site and change the robot's operating route. In this way, the robot's movements are optimized by collecting environmental information and reflecting it in its movements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data acquired by sensors into a generating AI and have the generating AI perform the optimization of the robot's movements.

[0039] The data collection unit can update the collected information in real time and adjust the robot's movements based on the latest situation. For example, if the situation at the disaster site changes, the data collection unit can collect new information and update the robot's movement plan. For example, if the location information of disaster victims changes, the data collection unit can also update the information in real time and recalculate the robot's movement route. Furthermore, if the environmental information at the disaster site changes, the data collection unit can collect the latest information and adjust the robot's movements. This allows for situation-appropriate responses by adjusting the robot's movements based on the latest information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the real-time updated information into a generating AI and have the generating AI perform adjustments to the robot's movements.

[0040] The collection unit can collect audio information from disaster sites and use it to pinpoint the location of victims. For example, the collection unit can detect the screams of victims and pinpoint their location. The collection unit can also analyze the cries for help of victims and determine the direction of the sound source. Furthermore, the collection unit can amplify the faint voices of victims and pinpoint their location. This allows for rapid rescue by collecting audio information and pinpointing the location of victims. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input audio data acquired by a microphone into a generating AI and have the generating AI perform the task of pinpointing the location of victims.

[0041] The data collection unit can transmit the collected information to the cloud and share it with other rescue teams. For example, the data collection unit can transmit the collected location information of disaster victims to the cloud and share it with other rescue teams. The data collection unit can also transmit the collected environmental information of the disaster site to the cloud and share it with other rescue teams. Furthermore, the data collection unit can transmit the collected information on obstacles at the disaster site to the cloud and share it with other rescue teams. This improves the efficiency of rescue operations by transmitting the collected information to the cloud and sharing it with other rescue teams. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model to transmit the collected information to the cloud and share it with other rescue teams.

[0042] The analysis unit can analyze the collected information and assess the risk level of the disaster site. For example, the analysis unit can analyze the collected temperature information to assess the risk of fire. For example, the analysis unit can also analyze the collected humidity information to assess the risk of flooding. Furthermore, the analysis unit can analyze the collected concentration of harmful gases to assess the risk level of chemical substances. This allows for appropriate responses by assessing the risk level of the disaster site. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that evaluates risk levels with the collected information as input to assess the risk level of the disaster site.

[0043] The analysis unit can automatically generate a robot motion plan based on the analyzed information. For example, the analysis unit can automatically generate the optimal motion route based on the collected information. For example, the analysis unit can also automatically generate a motion plan that avoids obstacles based on the collected information. Furthermore, the analysis unit can also automatically generate a rescue plan for disaster victims based on the collected information. This enables a rapid response by automatically generating a motion plan based on the analyzed information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can automatically generate a robot motion plan using an AI model that takes collected information as input and automatically generates motion plans.

[0044] The analysis unit can analyze the collected information and create a 3D map of the disaster site's terrain. For example, the analysis unit can analyze the collected terrain information and generate a 3D map of the disaster site. The analysis unit can also analyze the collected obstacle information and reflect it in the 3D map. Furthermore, the analysis unit can analyze the collected location information of disaster victims and display it on the 3D map. This allows for an accurate understanding of the situation at the site by creating a 3D map of the disaster site's terrain. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the collected information as input and performs 3D mapping to create a 3D map of the disaster site's terrain.

[0045] The analysis unit can plan coordinated operations with other rescue robots based on the analyzed information. For example, the analysis unit can plan coordinated operations with other rescue robots based on the collected information. For example, the analysis unit can also plan the division of roles among rescue robots based on the collected information. Furthermore, the analysis unit can adjust the timing of the rescue robots' movements based on the collected information. This enables efficient rescue operations by planning coordinated operations with other rescue robots. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use collected information as input and an AI model to plan coordinated operations with other rescue robots.

[0046] The control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. The control unit can also record robot movement logs and identify the cause of any abnormalities. Furthermore, the control unit can monitor the robot's movements and notify the operator if any abnormalities occur. This enables stable operation by monitoring the robot's movements in real time and automatically correcting any abnormalities. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities.

[0047] The control unit can record the robot's motion logs and save them for later analysis. For example, the control unit can record the robot's motion logs and save them for later analysis. The control unit can also save the robot's motion logs to the cloud and share them with other rescue teams. Furthermore, the control unit can analyze the robot's motion logs and use the results for future rescue operations. This means that by recording and later analyzing the robot's motion logs, the results can be used for future rescue operations. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can analyze the robot's motion logs using an AI model that records and saves the robot's motion logs for later analysis.

[0048] The control unit can simulate the robot's movements and verify the optimal operating procedures in advance. For example, the control unit can simulate the robot's movements and verify the optimal operating procedures in advance. The control unit can also simulate the robot's movements and consider countermeasures in case of abnormalities. Furthermore, the control unit can simulate the robot's movements and optimize the operator's operating procedures. This enables efficient rescue operations by simulating the robot's movements and verifying the optimal operating procedures in advance. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can simulate the robot's movements using an AI model that simulates the robot's movements and verifies the optimal operating procedures in advance.

[0049] The control unit can share the robot's movements with other rescue teams and cooperate in rescue operations. For example, the control unit can share the robot's movement information with other rescue teams and cooperate in rescue operations. The control unit can also share the robot's movement plan with other rescue teams and coordinate rescue operations. Furthermore, the control unit can share the robot's movement log with other rescue teams and utilize it for future rescue operations. This improves the efficiency of rescue operations by sharing the robot's movements with other rescue teams and cooperating in rescue operations. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can use an AI model to share the robot's movement information with other rescue teams and cooperate in rescue operations.

[0050] The mechanics department can optimize the movements of robot arms and cranes to perform tasks efficiently. For example, the mechanics department can optimize the movement of a robot arm to efficiently lift heavy rubble. It can also optimize the movement of a robot crane to efficiently move large obstacles. Furthermore, the mechanics department can optimize the coordinated movement of the robot arm and crane to efficiently perform complex tasks. This makes efficient work possible by optimizing the movements of robot arms and cranes. Some or all of the above-described processes in the mechanics department may be performed using AI, for example, or without AI. For example, the mechanics department can perform tasks efficiently using an AI model that optimizes the movements of arms and cranes.

[0051] The Mechanics Department can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the Mechanics Department can monitor the robot's movements in real time and automatically correct any abnormalities. The Mechanics Department can also record robot movement logs and identify the cause of any abnormalities. Furthermore, the Mechanics Department can monitor the robot's movements and notify the operator if any abnormalities occur. This enables stable operation by monitoring the robot's movements in real time and automatically correcting any abnormalities. Some or all of the above processes in the Mechanics Department may be performed using AI, for example, or without AI. For example, the Mechanics Department can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities.

[0052] The Mechanics Department can simulate the movements of robot arms and cranes and verify the optimal operating procedures in advance. For example, the Mechanics Department can simulate the movements of a robot arm and verify the optimal operating procedures in advance. The Mechanics Department can also simulate the movements of a robot crane and verify the optimal operating procedures in advance. Furthermore, the Mechanics Department can simulate the coordinated movements of a robot arm and crane and verify the optimal operating procedures in advance. This enables efficient work by simulating the movements of robot arms and cranes and verifying the optimal operating procedures in advance. Some or all of the above-described processes in the Mechanics Department may be performed using AI, for example, or without AI. For example, the Mechanics Department can simulate robot movements using an AI model that simulates the movements of arms and cranes and verifies the optimal operating procedures in advance.

[0053] The Mechanics Department can share robot movements with other rescue teams and cooperate in rescue operations. For example, the Mechanics Department can share robot movement information with other rescue teams and cooperate in rescue operations. The Mechanics Department can also share robot movement plans with other rescue teams and coordinate rescue operations. Furthermore, the Mechanics Department can share robot movement logs with other rescue teams and utilize them for future rescue operations. This improves the efficiency of rescue operations by sharing robot movements with other rescue teams and cooperating in rescue operations. Some or all of the above processes in the Mechanics Department may be performed using AI, for example, or not. For example, the Mechanics Department can use an AI model to share robot movement information with other rescue teams and cooperate in rescue operations.

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

[0055] Disaster relief robot systems can also be equipped with a health monitoring unit to monitor the health status of disaster victims. This unit can, for example, measure vital signs such as heart rate, blood pressure, and body temperature in real time. This allows for understanding the health status of victims and prioritizing the rescue of those with the highest levels of urgency. For instance, it can identify victims with abnormally high heart rates and rescue them quickly. It can also detect victims with low body temperature and take action to avoid the risk of hypothermia. Furthermore, it can identify victims with abnormally low blood pressure and take action to avoid the risk of shock. By monitoring the health status of disaster victims, more effective rescue operations become possible.

[0056] Disaster relief robot systems can also be equipped with a tracking unit that tracks the location of disaster victims in real time. The tracking unit, for example, tracks the location of disaster victims in real time using GPS or beacons. This allows for accurate location tracking and rapid rescue. For example, even if a disaster victim is moving, the tracking unit updates their location in real time, providing the rescue team with the latest location information. Furthermore, even if a disaster victim is inside a building, the tracking unit can pinpoint their location and provide information to the rescue team. The tracking unit can also transmit the disaster victim's location information to the cloud and share it with other rescue teams. This real-time tracking of disaster victims' locations enables rapid and efficient rescue operations.

[0057] Disaster relief robot systems can also be equipped with a health support unit that monitors the health status of disaster victims and conducts rescue operations based on the estimated health status. The health support unit can, for example, measure vital signs such as heart rate, blood pressure, and body temperature of disaster victims in real time and respond quickly if an abnormality is detected. For example, it can identify disaster victims with abnormally high heart rates and rescue them quickly. It can also detect disaster victims with low body temperature and take action to avoid the risk of hypothermia. Furthermore, it can identify disaster victims with abnormally low blood pressure and take action to avoid the risk of shock. This enables monitoring of the health status of disaster victims and allows for rapid and appropriate rescue operations.

[0058] Disaster relief robot systems can also be equipped with a cloud sharing unit that transmits the location information of disaster victims to the cloud and shares it with other rescue teams. For example, the cloud sharing unit can transmit the location information of disaster victims to the cloud in real time and share it with other rescue teams. This allows multiple rescue teams to cooperate and rescue disaster victims quickly. For instance, even if a disaster victim is on the move, the cloud sharing unit updates their location information in real time, providing other rescue teams with the latest location information. Furthermore, even if a disaster victim is inside a building, the cloud sharing unit can pinpoint their location and provide that information to other rescue teams. In addition, the cloud sharing unit can share information such as the health status and emotional state of disaster victims. This enables more efficient rescue operations by transmitting the location information of disaster victims to the cloud and sharing it with other rescue teams.

[0059] The disaster relief robot system can also be equipped with a 3D mapping unit that maps the location information of disaster victims in 3D. The 3D mapping unit can, for example, display the location information of disaster victims on a 3D map, providing visual information to rescue teams. This allows for accurate identification of disaster victims' locations and rapid rescue. For example, even if a disaster victim is inside a building, the 3D mapping unit can display their location on the 3D map and provide information to the rescue team. Furthermore, even if a disaster victim is moving, the 3D mapping unit can update their location in real time and display it on the 3D map. In addition, the 3D mapping unit can display not only the location information of disaster victims but also the location information of obstacles on the 3D map. This enables more efficient rescue operations by 3D mapping the location information of disaster victims.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects information from the disaster site. The collection unit can collect, for example, video information, audio information, and sensor data from the disaster site. The collection unit can also collect video from the disaster site in real time using a camera and collect audio information from the disaster site using a microphone. Furthermore, it can collect environmental information such as temperature, humidity, and atmospheric pressure using various sensors. Step 2: The analysis unit analyzes the information collected by the collection unit and selects the optimal route. The analysis unit analyzes the collected video information to identify the location and size of obstacles. It can also analyze the collected audio information to identify the location of victims. Furthermore, it can analyze the collected sensor data to understand the environmental conditions of the disaster site. Some or all of the processing in the analysis unit may be performed using AI, or it may be performed without using AI. Step 3: The control unit remotely operates the robot based on the route selected by the analysis unit. The control unit allows the operator to remotely control the robot and transmits and receives data between the robot and the operator using communication technology. The operator can also operate the robot using the control interface. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. Some or all of the processing in the control unit may be performed using AI or not. Step 4: The mechanics department uses a robot operated by the control unit to solve problems using powerful arms and cranes. The mechanics department can lift heavy rubble using its powerful arms and move large obstacles using its cranes. Furthermore, it can optimize the coordinated movements of the arms and cranes to efficiently perform complex tasks. Some or all of the processing in the mechanics department may or may not be performed using AI.

[0062] (Example of form 2) The disaster relief robot system according to an embodiment of the present invention is a system that utilizes an AI agent to support rescue operations at disaster sites. This disaster relief robot system can approach disaster sites. Disaster sites such as those affected by fires, earthquakes, and landslides are often difficult for humans to approach, but this robot, controlled by an AI agent, can move safely to the site while avoiding obstacles. For example, when moving through rubble, the AI ​​agent analyzes the surrounding situation in real time and selects the optimal route. Furthermore, this disaster relief robot system can be operated remotely. Disaster sites are dangerous, so it is desirable to avoid human entry into the site. This robot can be operated remotely by an operator from a safe location. For example, the operator can operate the robot's arm to remove rubble or rescue victims while viewing camera footage. Moreover, this disaster relief robot system can solve problems with strength exceeding human capabilities. There are many situations at disaster sites that cannot be handled by human strength alone. This robot is equipped with a powerful arm and crane, and can lift heavy rubble or move large obstacles. For example, a robot's powerful arm can be useful in rescuing victims trapped under collapsed buildings during an earthquake. In this way, disaster relief robot systems utilize AI agents to support rescue operations at disaster sites. This allows government agencies conducting emergency and disaster response to carry out rescue operations more safely and efficiently. As a result, disaster relief robot systems enable information gathering, analysis, remote operation, and solving mechanical challenges at disaster sites.

[0063] The disaster relief robot system according to this embodiment comprises a collection unit, an analysis unit, an operation unit, and a mechanics unit. The collection unit collects information from the disaster site. The collection unit can collect, for example, video information, audio information, and sensor data from the disaster site. For example, the collection unit can collect video of the disaster site in real time using a camera. The collection unit can also collect audio information from the disaster site using a microphone. Furthermore, the collection unit can also collect environmental information such as temperature, humidity, and atmospheric pressure using various sensors. The analysis unit analyzes the information collected by the collection unit and selects the optimal route. For example, the analysis unit analyzes the collected video information to identify the location and size of obstacles. The analysis unit can also analyze the collected audio information to identify the location of victims. Furthermore, the analysis unit can analyze the collected sensor data to understand the environmental conditions of the disaster site. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can select the optimal route using an AI model that takes the information collected by the collection unit as input and outputs the optimal route. The control unit remotely operates the robot based on a route selected by the analysis unit. The control unit allows, for example, an operator to remotely control the robot. The control unit sends and receives data between the robot and the operator using, for example, communication technology. The control unit also allows the operator to operate the robot using an operating interface. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities. The mechanics unit allows the robot, operated by the control unit, to solve problems using powerful arms and cranes. The mechanics unit can, for example, use a powerful arm to lift heavy rubble. The mechanics unit can, for example, use a crane to move large obstacles.Furthermore, the mechanics department can optimize the coordinated movement of the arm and crane, enabling the efficient execution of complex tasks. Some or all of the processes described above in the mechanics department may be performed using AI, for example, or not. For example, the mechanics department can perform tasks efficiently using an AI model that optimizes the movement of the arm and crane. This enables the disaster relief robot system according to the embodiment to collect and analyze information, operate remotely, and solve mechanical problems at disaster sites.

[0064] The data collection unit collects information from disaster sites. For example, it can collect video, audio, and sensor data from disaster sites. Specifically, the unit uses high-resolution cameras to collect video footage of disaster sites in real time. This allows operators to gain a detailed understanding of the situation at the site. The unit can also collect audio information from disaster sites using high-sensitivity microphones. This allows for the detection of victims' voices and ambient sounds, which can be used to aid in rescue operations. Furthermore, the unit can collect environmental information such as temperature, humidity, and atmospheric pressure using various sensors. For example, temperature sensors are useful for identifying fires and heat sources, while humidity sensors help in understanding flood conditions. Atmospheric pressure sensors provide data for assessing the risk of building collapse. This sensor data is crucial for gaining a detailed understanding of the environmental conditions at disaster sites. The data collection unit centrally manages this data and transmits it to a central database in real time. This allows the analysis and operation units to quickly access the necessary information. Additionally, the data collection unit can collect information over a wide area using drones and robots. Drones collect aerial video and sensor data, while ground-based robots collect information in confined or dangerous locations. This allows the collection unit to grasp the overall picture of the disaster site and support efficient rescue operations.

[0065] The analysis unit analyzes the information collected by the collection unit and selects the optimal route. For example, the analysis unit analyzes collected video information to identify the location and size of obstacles. Specifically, it utilizes AI-based image recognition technology to automatically detect obstacles in the video. This allows operators to quickly select the optimal route to avoid obstacles. The analysis unit can also analyze collected audio information to identify the location of victims. Using speech recognition technology, it detects the voices of victims and sounds calling for help and identifies their location. Furthermore, the analysis unit can analyze collected sensor data to understand the environmental conditions at the disaster site. For example, it can analyze temperature sensor data to identify the location of a fire, analyze humidity sensor data to understand the progression of flooding, and analyze barometric pressure sensor data to assess the risk of building collapse. This allows the analysis unit to gain a detailed understanding of the disaster site and provide information for selecting the optimal route. In addition, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past disaster data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The control unit remotely operates the robot based on the route selected by the analysis unit. For example, the control unit allows an operator to remotely control the robot. Specifically, the control unit uses communication technology to send and receive data between the robot and the operator. This allows the operator to remotely control the robot while understanding the situation at the disaster site in real time. The control unit also allows the operator to operate the robot using an operating interface. The operating interface features an intuitive touch panel and joystick, allowing the operator to easily control the robot. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, it can utilize an AI-based anomaly detection algorithm to automatically perform corrective actions if an abnormality is detected in the robot's movement. This allows the control unit to improve the robot's safety and reliability. The control unit can also control multiple robots simultaneously, supporting efficient work at disaster sites. For example, multiple robots working together can quickly collect information over a wide area and remove obstacles. This allows the control unit to effectively support rescue operations at disaster sites, contributing to the rescue of victims and mitigating disaster damage.

[0067] The Mechanics Department uses robots operated by the control unit to solve problems using powerful arms and cranes. For example, the Mechanics Department can lift heavy debris using its powerful arms. Specifically, the arms are equipped with high-precision motors and sensors, allowing them to accurately lift and move heavy objects. The Mechanics Department can also move large obstacles using its cranes. The cranes are equipped with long arms and powerful winches, allowing them to safely move objects from high places or distances. Furthermore, the Mechanics Department can optimize the coordinated movements of the arms and cranes to efficiently perform complex tasks. For example, it utilizes AI-based motion optimization algorithms to adjust the movements of the arms and cranes in real time. This allows the Mechanics Department to perform complex tasks quickly and accurately. The Mechanics Department is equipped with multiple tools and attachments to address various challenges in disaster sites. For example, the arms can be fitted with grippers for grasping debris and cutters for cutting. The cranes can be fitted with hooks and buckets for lifting heavy objects. This allows the Mechanics Department to adapt to various situations and perform tasks efficiently. Furthermore, the mechanics unit can monitor the progress of the work in real time and adjust its operation as needed. This allows the mechanics unit to work safely and efficiently at disaster sites and support rescue operations.

[0068] The data collection unit can collect information about obstacles at a disaster site. For example, the data collection unit collects information such as the location, size, and material of the obstacles. For example, the data collection unit can collect images of obstacles using a camera and identify their location and size using image analysis technology. The data collection unit can also detect the material of obstacles using sensors. For example, the data collection unit can measure the distance to obstacles using a laser sensor and acquire location information. Furthermore, the data collection unit can detect the shape of obstacles using an ultrasonic sensor. By collecting information about obstacles at a disaster site, the accuracy of the robot's motion plan can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired by a camera into a generating AI and have the generating AI identify the location and size of obstacles.

[0069] The analysis unit can analyze the collected information and select the optimal route to avoid obstacles. For example, the analysis unit can analyze collected video information to identify the location and size of obstacles. For example, the analysis unit can analyze collected audio information to identify the location of victims. Furthermore, the analysis unit can analyze collected sensor data to understand the environmental conditions of the disaster site. For example, the analysis unit can analyze collected temperature information to assess the risk of fire. The analysis unit can also analyze collected humidity information to assess the risk of flooding. Furthermore, the analysis unit can analyze the concentration of collected harmful gases to assess the risk of chemical substances. This enables the robot to move safely by selecting the optimal route to avoid obstacles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select the optimal route using an AI model that takes information collected by the collection unit as input and outputs the optimal route.

[0070] The control unit allows an operator to remotely control the robot. The control unit sends and receives data between the robot and the operator, for example, using communication technology. The control unit also allows the operator to operate the robot using, for example, an operating interface. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities. This allows the operator to remotely control the robot from a safe location. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities.

[0071] The Mechanics Department can lift heavy debris using powerful arms and cranes. For example, the Mechanics Department can lift heavy debris using powerful arms. The Mechanics Department can also move large obstacles using cranes. Furthermore, the Mechanics Department can optimize the coordinated movements of arms and cranes to efficiently perform complex tasks. For example, the Mechanics Department can optimize the movement of arms to efficiently lift heavy debris. The Mechanics Department can also optimize the movement of cranes to efficiently move large obstacles. Furthermore, the Mechanics Department can optimize the coordinated movements of arms and cranes to efficiently perform complex tasks. This makes it possible to rescue victims by lifting heavy debris. Some or all of the above processes in the Mechanics Department may be performed using AI, for example, or not. For example, the Mechanics Department can perform tasks efficiently using AI models that optimize the movements of arms and cranes.

[0072] The data collection unit can estimate the emotions of victims and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit can analyze the facial expressions and tone of voice of victims and prioritize the collection of location information of victims who are feeling fear or anxiety. For example, the data collection unit can monitor the heart rate and respiratory rate of victims and prioritize the collection of information of victims whose lives are at high risk. Furthermore, the data collection unit can analyze the behavioral patterns of victims and prioritize the collection of information of victims who are in a state of panic. In this way, important information can be collected preferentially by determining the priority of information based on the emotions of victims. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0073] The data collection unit can collect environmental information such as temperature and humidity at the disaster site and reflect it in the robot's movements. For example, the data collection unit can monitor the temperature at the disaster site in real time and adjust the robot's operating speed. The data collection unit can also measure the humidity at the disaster site and take protective measures for the robot's electronic equipment. Furthermore, the data collection unit can detect the concentration of harmful gases at the disaster site and change the robot's operating route. In this way, the robot's movements are optimized by collecting environmental information and reflecting it in its movements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data acquired by sensors into a generating AI and have the generating AI perform the optimization of the robot's movements.

[0074] The data collection unit can update the collected information in real time and adjust the robot's movements based on the latest situation. For example, if the situation at the disaster site changes, the data collection unit can collect new information and update the robot's movement plan. For example, if the location information of disaster victims changes, the data collection unit can also update the information in real time and recalculate the robot's movement route. Furthermore, if the environmental information at the disaster site changes, the data collection unit can collect the latest information and adjust the robot's movements. This allows for situation-appropriate responses by adjusting the robot's movements based on the latest information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the real-time updated information into a generating AI and have the generating AI perform adjustments to the robot's movements.

[0075] The data collection unit can estimate the emotions of disaster victims and select the types of information to collect based on the estimated emotions. For example, the data collection unit can analyze the facial expressions of disaster victims and prioritize the collection of location information for those who appear to be feeling fear. For example, the data collection unit can analyze the tone of voice of disaster victims and prioritize the collection of health information for those who appear to be feeling anxious. Furthermore, the data collection unit can analyze the behavioral patterns of disaster victims and prioritize the collection of information about the surrounding environment for those who appear to be in a state of panic. This allows for the efficient collection of necessary information by selecting the types of information based on the emotions of the disaster victims. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of disaster victims captured by a camera into a generative AI and have the generative AI perform the estimation of the disaster victims' emotions.

[0076] The collection unit can collect audio information from disaster sites and use it to pinpoint the location of victims. For example, the collection unit can detect the screams of victims and pinpoint their location. The collection unit can also analyze the cries for help of victims and determine the direction of the sound source. Furthermore, the collection unit can amplify the faint voices of victims and pinpoint their location. This allows for rapid rescue by collecting audio information and pinpointing the location of victims. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input audio data acquired by a microphone into a generating AI and have the generating AI perform the task of pinpointing the location of victims.

[0077] The data collection unit can transmit the collected information to the cloud and share it with other rescue teams. For example, the data collection unit can transmit the collected location information of disaster victims to the cloud and share it with other rescue teams. The data collection unit can also transmit the collected environmental information of the disaster site to the cloud and share it with other rescue teams. Furthermore, the data collection unit can transmit the collected information on obstacles at the disaster site to the cloud and share it with other rescue teams. This improves the efficiency of rescue operations by transmitting the collected information to the cloud and sharing it with other rescue teams. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model to transmit the collected information to the cloud and share it with other rescue teams.

[0078] The analysis unit can estimate the emotions of the victims and adjust the display method of the analysis results based on the estimated emotions of the victims. For example, if the emotions of the victims are unstable, the analysis unit can display the analysis results simply to reduce the visual burden. For example, if the emotions of the victims are stable, the analysis unit can also display detailed analysis results to provide information. Furthermore, if the emotions of the victims are tense, the analysis unit can display the analysis results in calm colors to provide a sense of security. In this way, by adjusting the display method of the analysis results based on the emotions of the victims, appropriate information can be provided to the victims. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the victims taken by a camera into the generative AI and have the generative AI perform the estimation of the victims' emotions.

[0079] The analysis unit can analyze the collected information and assess the risk level of the disaster site. For example, the analysis unit can analyze the collected temperature information to assess the risk of fire. For example, the analysis unit can also analyze the collected humidity information to assess the risk of flooding. Furthermore, the analysis unit can analyze the collected concentration of harmful gases to assess the risk level of chemical substances. This allows for appropriate responses by assessing the risk level of the disaster site. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that evaluates risk levels with the collected information as input to assess the risk level of the disaster site.

[0080] The analysis unit can automatically generate a robot motion plan based on the analyzed information. For example, the analysis unit can automatically generate the optimal motion route based on the collected information. For example, the analysis unit can also automatically generate a motion plan that avoids obstacles based on the collected information. Furthermore, the analysis unit can also automatically generate a rescue plan for disaster victims based on the collected information. This enables a rapid response by automatically generating a motion plan based on the analyzed information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can automatically generate a robot motion plan using an AI model that takes collected information as input and automatically generates motion plans.

[0081] The analysis unit can estimate the emotions of the victims and determine the priority of the analysis results based on the estimated emotions. For example, if the emotions of the victims are unstable, the analysis unit will prioritize the analysis of information indicating a high risk to life. For example, if the emotions of the victims are stable, the analysis unit can also prioritize the analysis of detailed information. Furthermore, if the emotions of the victims are tense, the analysis unit can also prioritize the analysis of information that provides a sense of security. In this way, by determining the priority of the analysis results based on the emotions of the victims, important information can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0082] The analysis unit can analyze the collected information and create a 3D map of the disaster site's terrain. For example, the analysis unit can analyze the collected terrain information and generate a 3D map of the disaster site. The analysis unit can also analyze the collected obstacle information and reflect it in the 3D map. Furthermore, the analysis unit can analyze the collected location information of disaster victims and display it on the 3D map. This allows for an accurate understanding of the situation at the site by creating a 3D map of the disaster site's terrain. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the collected information as input and performs 3D mapping to create a 3D map of the disaster site's terrain.

[0083] The analysis unit can plan coordinated operations with other rescue robots based on the analyzed information. For example, the analysis unit can plan coordinated operations with other rescue robots based on the collected information. For example, the analysis unit can also plan the division of roles among rescue robots based on the collected information. Furthermore, the analysis unit can adjust the timing of the rescue robots' movements based on the collected information. This enables efficient rescue operations by planning coordinated operations with other rescue robots. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use collected information as input and an AI model to plan coordinated operations with other rescue robots.

[0084] The control unit can estimate the victim's emotions and adjust the operation method based on the estimated emotions. For example, if the victim's emotions are unstable, the control unit can simplify the operation method and respond quickly. For example, if the victim's emotions are stable, the control unit can also provide detailed operation instructions. Furthermore, if the victim's emotions are tense, the control unit can provide operation instructions in a calm tone. This allows for an appropriate response to the victim by adjusting the operation method based on their 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 control unit may be performed using AI, for example, or without AI. For example, the control unit can input image data of the victim captured by a camera into the generative AI and have the generative AI perform the estimation of the victim's emotions.

[0085] The control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. The control unit can also record robot movement logs and identify the cause of any abnormalities. Furthermore, the control unit can monitor the robot's movements and notify the operator if any abnormalities occur. This enables stable operation by monitoring the robot's movements in real time and automatically correcting any abnormalities. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities.

[0086] The control unit can record the robot's motion logs and save them for later analysis. For example, the control unit can record the robot's motion logs and save them for later analysis. The control unit can also save the robot's motion logs to the cloud and share them with other rescue teams. Furthermore, the control unit can analyze the robot's motion logs and use the results for future rescue operations. This means that by recording and later analyzing the robot's motion logs, the results can be used for future rescue operations. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can analyze the robot's motion logs using an AI model that records and saves the robot's motion logs for later analysis.

[0087] The control unit can estimate the victim's emotions and determine the priority of operations based on the estimated emotions. For example, if the victim's emotions are unstable, the control unit will prioritize operations that pose a high risk to life. For example, if the victim's emotions are stable, the control unit may also prioritize detailed operations. Furthermore, if the victim's emotions are tense, the control unit may also prioritize operations that provide a sense of security. In this way, by determining the priority of operations based on the victim's emotions, important operations can be prioritized. 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 control unit may be performed using AI, for example, or without AI. For example, the control unit can input image data of the victim captured by a camera into the generative AI and have the generative AI perform the estimation of the victim's emotions.

[0088] The control unit can simulate the robot's movements and verify the optimal operating procedures in advance. For example, the control unit can simulate the robot's movements and verify the optimal operating procedures in advance. The control unit can also simulate the robot's movements and consider countermeasures in case of abnormalities. Furthermore, the control unit can simulate the robot's movements and optimize the operator's operating procedures. This enables efficient rescue operations by simulating the robot's movements and verifying the optimal operating procedures in advance. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can simulate the robot's movements using an AI model that simulates the robot's movements and verifies the optimal operating procedures in advance.

[0089] The control unit can share the robot's movements with other rescue teams and cooperate in rescue operations. For example, the control unit can share the robot's movement information with other rescue teams and cooperate in rescue operations. The control unit can also share the robot's movement plan with other rescue teams and coordinate rescue operations. Furthermore, the control unit can share the robot's movement log with other rescue teams and utilize it for future rescue operations. This improves the efficiency of rescue operations by sharing the robot's movements with other rescue teams and cooperating in rescue operations. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can use an AI model to share the robot's movement information with other rescue teams and cooperate in rescue operations.

[0090] The Dynamics Department can estimate the emotions of victims and adjust its actions based on the estimated emotions. For example, if a victim's emotions are unstable, the Dynamics Department can perform its actions cautiously to reassure the victim. For example, if a victim's emotions are stable, the Dynamics Department can perform its actions quickly to streamline rescue operations. Furthermore, if a victim's emotions are tense, the Dynamics Department can perform its actions in a calm tone to reassure the victim. In this way, by adjusting the Dynamics Department's actions based on the victim's emotions, an appropriate response to the victim becomes possible. 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 Dynamics Department may be performed using AI, for example, or without AI. For example, the Dynamics Department can input image data of victims captured by a camera into a generative AI and have the generative AI perform the estimation of the victims' emotions.

[0091] The mechanics department can optimize the movements of robot arms and cranes to perform tasks efficiently. For example, the mechanics department can optimize the movement of a robot arm to efficiently lift heavy rubble. It can also optimize the movement of a robot crane to efficiently move large obstacles. Furthermore, the mechanics department can optimize the coordinated movement of the robot arm and crane to efficiently perform complex tasks. This makes efficient work possible by optimizing the movements of robot arms and cranes. Some or all of the above-described processes in the mechanics department may be performed using AI, for example, or without AI. For example, the mechanics department can perform tasks efficiently using an AI model that optimizes the movements of arms and cranes.

[0092] The Mechanics Department can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the Mechanics Department can monitor the robot's movements in real time and automatically correct any abnormalities. The Mechanics Department can also record robot movement logs and identify the cause of any abnormalities. Furthermore, the Mechanics Department can monitor the robot's movements and notify the operator if any abnormalities occur. This enables stable operation by monitoring the robot's movements in real time and automatically correcting any abnormalities. Some or all of the above processes in the Mechanics Department may be performed using AI, for example, or without AI. For example, the Mechanics Department can control the robot using an AI model that monitors the robot's movements in real time and automatically corrects any abnormalities.

[0093] The Dynamics Department can estimate the emotions of the victim and determine the priority of its actions based on the estimated emotions. For example, if the victim's emotions are unstable, the Dynamics Department will prioritize actions that pose a high risk to life. For example, if the victim's emotions are stable, the Dynamics Department may also prioritize detailed actions. Furthermore, if the victim's emotions are tense, the Dynamics Department may also prioritize actions that provide a sense of security. In this way, by determining the priority of the Dynamics Department's actions based on the victim's emotions, important actions can be prioritized. 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 Dynamics Department may be performed using AI, for example, or without AI. For example, the Dynamics Department can input image data of the victim captured by a camera into a generative AI and have the generative AI perform the estimation of the victim's emotions.

[0094] The Mechanics Department can simulate the movements of robot arms and cranes and verify the optimal operating procedures in advance. For example, the Mechanics Department can simulate the movements of a robot arm and verify the optimal operating procedures in advance. The Mechanics Department can also simulate the movements of a robot crane and verify the optimal operating procedures in advance. Furthermore, the Mechanics Department can simulate the coordinated movements of a robot arm and crane and verify the optimal operating procedures in advance. This enables efficient work by simulating the movements of robot arms and cranes and verifying the optimal operating procedures in advance. Some or all of the above-described processes in the Mechanics Department may be performed using AI, for example, or without AI. For example, the Mechanics Department can simulate robot movements using an AI model that simulates the movements of arms and cranes and verifies the optimal operating procedures in advance.

[0095] The Mechanics Department can share robot movements with other rescue teams and cooperate in rescue operations. For example, the Mechanics Department can share robot movement information with other rescue teams and cooperate in rescue operations. The Mechanics Department can also share robot movement plans with other rescue teams and coordinate rescue operations. Furthermore, the Mechanics Department can share robot movement logs with other rescue teams and utilize them for future rescue operations. This improves the efficiency of rescue operations by sharing robot movements with other rescue teams and cooperating in rescue operations. Some or all of the above processes in the Mechanics Department may be performed using AI, for example, or not. For example, the Mechanics Department can use an AI model to share robot movement information with other rescue teams and cooperate in rescue operations.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Disaster relief robot systems can also be equipped with a health monitoring unit to monitor the health status of disaster victims. This unit can, for example, measure vital signs such as heart rate, blood pressure, and body temperature in real time. This allows for understanding the health status of victims and prioritizing the rescue of those with the highest levels of urgency. For instance, it can identify victims with abnormally high heart rates and rescue them quickly. It can also detect victims with low body temperature and take action to avoid the risk of hypothermia. Furthermore, it can identify victims with abnormally low blood pressure and take action to avoid the risk of shock. By monitoring the health status of disaster victims, more effective rescue operations become possible.

[0098] The disaster relief robot system can also be equipped with a communication unit that estimates the emotions of disaster victims and communicates with them based on those estimated emotions. For example, the communication unit can analyze the facial expressions and tone of voice of disaster victims and send reassuring messages to those who are feeling fear or anxiety. For example, if a disaster victim is feeling fear, it can send a message such as, "It's okay, help will be here soon." It can also send a message such as, "Don't worry, we've found you," to a disaster victim who is feeling anxious. Furthermore, if a disaster victim is in a state of panic, it can send a message such as, "Take a deep breath and calm down." In this way, by communicating appropriately based on the emotions of disaster victims, their sense of security can be enhanced.

[0099] Disaster relief robot systems can also be equipped with a tracking unit that tracks the location of disaster victims in real time. The tracking unit, for example, tracks the location of disaster victims in real time using GPS or beacons. This allows for accurate location tracking and rapid rescue. For example, even if a disaster victim is moving, the tracking unit updates their location in real time, providing the rescue team with the latest location information. Furthermore, even if a disaster victim is inside a building, the tracking unit can pinpoint their location and provide information to the rescue team. The tracking unit can also transmit the disaster victim's location information to the cloud and share it with other rescue teams. This real-time tracking of disaster victims' locations enables rapid and efficient rescue operations.

[0100] Disaster relief robot systems can also be equipped with a support unit that estimates the emotions of disaster victims and provides assistance based on those estimated emotions. For example, the support unit can analyze the victim's facial expressions and tone of voice and provide psychological support to victims who are feeling fear or anxiety. If a victim is feeling fear, the support unit can offer reassuring words. Similarly, if a victim is feeling anxious, the support unit can offer words of encouragement. Furthermore, if a victim is in a state of panic, the support unit can encourage them to calm down. By providing appropriate support based on the victim's emotions, the psychological burden on the victim can be reduced.

[0101] Disaster relief robot systems can also be equipped with a health support unit that monitors the health status of disaster victims and conducts rescue operations based on the estimated health status. The health support unit can, for example, measure vital signs such as heart rate, blood pressure, and body temperature of disaster victims in real time and respond quickly if an abnormality is detected. For example, it can identify disaster victims with abnormally high heart rates and rescue them quickly. It can also detect disaster victims with low body temperature and take action to avoid the risk of hypothermia. Furthermore, it can identify disaster victims with abnormally low blood pressure and take action to avoid the risk of shock. This enables monitoring of the health status of disaster victims and allows for rapid and appropriate rescue operations.

[0102] The disaster relief robot system may also be equipped with an information provision unit that estimates the emotions of disaster victims and provides information to them based on those estimated emotions. For example, the information provision unit can analyze the facial expressions and tone of voice of disaster victims and provide reassuring information to those who are feeling fear or anxiety. For example, if a disaster victim is feeling fear, it may provide information such as, "The rescue team will arrive shortly." It can also provide information such as, "Please evacuate to a safe place" to disaster victims who are feeling anxious. Furthermore, if a disaster victim is in a state of panic, it may provide information such as, "Please stay calm." In this way, by providing appropriate information based on the emotions of disaster victims, their sense of security can be enhanced.

[0103] Disaster relief robot systems can also be equipped with a cloud sharing unit that transmits the location information of disaster victims to the cloud and shares it with other rescue teams. For example, the cloud sharing unit can transmit the location information of disaster victims to the cloud in real time and share it with other rescue teams. This allows multiple rescue teams to cooperate and rescue disaster victims quickly. For instance, even if a disaster victim is on the move, the cloud sharing unit updates their location information in real time, providing other rescue teams with the latest location information. Furthermore, even if a disaster victim is inside a building, the cloud sharing unit can pinpoint their location and provide that information to other rescue teams. In addition, the cloud sharing unit can share information such as the health status and emotional state of disaster victims. This enables more efficient rescue operations by transmitting the location information of disaster victims to the cloud and sharing it with other rescue teams.

[0104] The disaster relief robot system may also include a relief supplies distribution unit that estimates the emotions of disaster victims and provides relief supplies to them based on those estimated emotions. For example, the relief supplies distribution unit can analyze the facial expressions and tone of voice of disaster victims and provide necessary relief supplies to those who are feeling fear or anxiety. For example, if a disaster victim is feeling fear, the relief supplies distribution unit will provide them with blankets and food. If a disaster victim is feeling anxious, the relief supplies distribution unit can also provide them with water and medicine. Furthermore, if a disaster victim is in a state of panic, the relief supplies distribution unit can provide them with supplies to give them a sense of security. In this way, by providing appropriate relief supplies based on the emotions of disaster victims, their sense of security can be enhanced.

[0105] The disaster relief robot system can also be equipped with a 3D mapping unit that maps the location information of disaster victims in 3D. The 3D mapping unit can, for example, display the location information of disaster victims on a 3D map, providing visual information to rescue teams. This allows for accurate identification of disaster victims' locations and rapid rescue. For example, even if a disaster victim is inside a building, the 3D mapping unit can display their location on the 3D map and provide information to the rescue team. Furthermore, even if a disaster victim is moving, the 3D mapping unit can update their location in real time and display it on the 3D map. In addition, the 3D mapping unit can display not only the location information of disaster victims but also the location information of obstacles on the 3D map. This enables more efficient rescue operations by 3D mapping the location information of disaster victims.

[0106] The disaster relief robot system can also be equipped with a psychological support unit that estimates the emotions of disaster victims and provides psychological support to them based on those estimated emotions. For example, the psychological support unit can analyze the facial expressions and tone of voice of disaster victims and provide psychological support to those who are feeling fear or anxiety. For example, if a disaster victim is feeling fear, the psychological support unit can offer reassuring words. If a disaster victim is feeling anxious, the psychological support unit can also offer words of encouragement. Furthermore, if a disaster victim is in a state of panic, the psychological support unit can also offer words to encourage them to calm down. In this way, by providing appropriate psychological support based on the disaster victim's emotions, the psychological burden on the disaster victim can be reduced.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects information from the disaster site. The collection unit can collect, for example, video information, audio information, and sensor data from the disaster site. The collection unit can also collect video from the disaster site in real time using a camera and collect audio information from the disaster site using a microphone. Furthermore, it can collect environmental information such as temperature, humidity, and atmospheric pressure using various sensors. Step 2: The analysis unit analyzes the information collected by the collection unit and selects the optimal route. The analysis unit analyzes the collected video information to identify the location and size of obstacles. It can also analyze the collected audio information to identify the location of victims. Furthermore, it can analyze the collected sensor data to understand the environmental conditions of the disaster site. Some or all of the processing in the analysis unit may be performed using AI, or it may be performed without using AI. Step 3: The control unit remotely operates the robot based on the route selected by the analysis unit. The control unit allows the operator to remotely control the robot and transmits and receives data between the robot and the operator using communication technology. The operator can also operate the robot using the control interface. Furthermore, the control unit can monitor the robot's movements in real time and automatically correct any abnormalities. Some or all of the processing in the control unit may be performed using AI or not. Step 4: The mechanics department uses a robot operated by the control unit to solve problems using powerful arms and cranes. The mechanics department can lift heavy rubble using its powerful arms and move large obstacles using its cranes. Furthermore, it can optimize the coordinated movements of the arms and cranes to efficiently perform complex tasks. Some or all of the processing in the mechanics department may or may not be performed using AI.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and force unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects video and audio information from the disaster site using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and selects the optimal route. The operation unit is implemented by, for example, the control unit 46A of the smart device 14, which allows an operator to remotely control the robot. The force unit is implemented by, for example, the control unit 46A of the smart device 14, which solves problems using a powerful arm or crane. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and force unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects video and audio information from the disaster site using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and selects the optimal route. The operation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which allows an operator to remotely control the robot. The force unit is implemented, for example, by the control unit 46A of the smart glasses 214, which solves problems using a powerful arm or crane. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and force unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects video and audio information from the disaster site using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to select the optimal route. The operation unit is implemented in the control unit 46A of the headset terminal 314, for example, and allows an operator to remotely control the robot. The force unit is implemented in the control unit 46A of the headset terminal 314, for example, and solves problems using a powerful arm or crane. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and force unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects video and audio information from the disaster site using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and selects the optimal route. The operation unit is implemented, for example, by the control unit 46A of the robot 414, which allows an operator to control the robot remotely. The force unit is implemented, for example, by the control unit 46A of the robot 414, which solves problems using a powerful arm or crane. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The collection unit collects information from the disaster site, An analysis unit analyzes the information collected by the aforementioned collection unit and selects the optimal route, An operating unit remotely controls the robot based on the route selected by the analysis unit, The robot, operated by the aforementioned control unit, has a mechanism section that uses powerful arms and cranes to solve problems. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information on obstacles at disaster sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to select the optimal route that avoids obstacles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned operating unit is An operator controls the robot remotely. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Department of Mechanics, Using powerful arms and cranes to lift heavy debris. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the emotions of disaster victims and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Collect environmental information such as temperature and humidity at disaster sites and use it to guide the robot's movements. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The collected information is updated in real time, and the robot's actions are adjusted based on the latest situation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the emotions of disaster victims and selects the types of information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system collects audio information from disaster sites and uses it to pinpoint the location of victims. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The collected information is sent to the cloud and shared with other rescue teams. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the emotions of disaster victims and adjusts the display method of the analysis results based on the estimated emotions of the disaster victims. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The collected information is analyzed to assess the level of danger at the disaster site. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Based on the analyzed information, the robot's motion plan is automatically generated. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the emotions of disaster victims and prioritizes the analysis results based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The collected information is analyzed, and the topography of the disaster site is mapped in 3D. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Based on the analyzed information, we plan coordinated operations with other rescue robots. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned operating unit is The system estimates the emotions of the victims and adjusts the operating procedures based on the estimated emotions of the victims. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned operating unit is The robot's movements are monitored in real time, and any abnormalities are automatically corrected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned operating unit is Record the robot's operation logs and save them for later analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned operating unit is The system estimates the emotions of the victims and determines the priority of operations based on the estimated emotions of the victims. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned operating unit is The robot's movements are simulated, and the optimal operating procedure is verified in advance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned operating unit is The robot's movements will be shared with other rescue teams, allowing them to cooperate in rescue operations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Department of Mechanics, The system estimates the emotions of the victims and adjusts the operation of the mechanics department based on the estimated emotions of the victims. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Department of Mechanics, Optimizing the movements of robot arms and cranes to perform tasks efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Department of Mechanics, The robot's movements are monitored in real time, and any abnormalities are automatically corrected. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Department of Mechanics, The system estimates the emotions of the victims and determines the priority of the mechanics' actions based on the estimated emotions of the victims. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Department of Mechanics, The robot's arm and crane movements are simulated to verify the optimal operating procedure in advance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Department of Mechanics, The robot's movements will be shared with other rescue teams, allowing them to cooperate in rescue operations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects information from the disaster site, An analysis unit analyzes the information collected by the aforementioned collection unit and selects the optimal route, An operating unit remotely controls the robot based on the route selected by the analysis unit, The robot, operated by the aforementioned control unit, has a mechanism section that uses powerful arms and cranes to solve problems. A system characterized by the following features.

2. The aforementioned collection unit is Collect information on obstacles at disaster sites. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to select the optimal route that avoids obstacles. The system according to feature 1.

4. The aforementioned operating unit is An operator controls the robot remotely. The system according to feature 1.

5. The aforementioned Department of Mechanics, Using powerful arms and cranes to lift heavy debris. The system according to feature 1.

6. The aforementioned collection unit is We estimate the emotions of disaster victims and prioritize the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Collect environmental information such as temperature and humidity at disaster sites and use it to guide the robot's movements. The system according to feature 1.

8. The aforementioned collection unit is The collected information is updated in real time, and the robot's actions are adjusted based on the latest situation. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the emotions of disaster victims and selects the types of information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is The system collects audio information from disaster sites and uses it to pinpoint the location of victims. The system according to feature 1.

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