System

Abstract

To provide a system for realizing secure and safe mountain climbing by predicting a risk in mountain climbing and proposing a safety measure.SOLUTION: The system includes a data collection section, a big data generation section, a risk prediction section, a safety measure proposal section, a real-time situation grasping section, a warning section, a knowledge sharing section, a community formation section, and a mountain climbing plan optimization section. The data collection unit collects past mountain climbing data. The big data generation unit accumulates data as big data. The risk prediction unit predicts a risk. The safety measure proposal unit proposes a safety measure. The real-time situation grasping unit collects and analyzes real-time data during the mountain climbing. The warning unit issues a warning based on the analyzed data. The knowledge sharing unit shares knowledge based on the collected data and the analysis result. The community forming unit manages the formed community. The mountain climbing plan optimization section optimizes the mountain climbing plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, risk management in mountain climbing relies on empirical rules, and knowledge accumulation and sharing is insufficient, so there is room for improvement in improving safety.

[0005] The system according to the embodiment aims to realize a safe and secure mountain climbing experience by predicting risks involved in mountain climbing and proposing safety measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a big data conversion unit, a risk prediction unit, a safety measure proposal unit, a real-time situation understanding unit, a warning unit, a knowledge sharing unit, a community formation unit, and a mountain climbing plan optimization unit. The data collection unit collects past mountain climbing data. The big data conversion unit accumulates the data collected by the data collection unit as big data. The risk prediction unit analyzes the data accumulated by the big data conversion unit to predict risks. The safety measure proposal unit proposes safety measures based on the risks predicted by the risk prediction unit. The real-time situation understanding unit collects and analyzes real-time data during mountain climbing. The warning unit issues warnings based on the data analyzed by the real-time situation understanding unit. The knowledge sharing unit shares knowledge based on the collected data and analysis results. The community formation unit manages the community formed by the knowledge sharing unit. The mountain climbing plan optimization unit optimizes the mountain climbing plan based on the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict risks in mountain climbing and propose safety measures, thereby enabling safe and secure mountain climbing. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A mountain climbing safety system according to an embodiment of the present invention is a system that reduces the risk of injury, accidents, and getting lost while climbing mountains, thereby enabling safe and secure mountain climbing. This system collects past mountain climbing data, stores it as big data, and utilizes AI to predict risks, propose safety measures, grasp the situation in real time and issue warnings, share knowledge and form communities, and optimize mountain climbing plans. As a result, the mountain climbing safety system reduces the risks involved in mountain climbing, enabling safe and secure mountain climbing.

[0029] A mountain climbing safety system according to an embodiment includes a data collection unit, a big data conversion unit, a risk prediction unit, a safety measure proposal unit, a real-time situation understanding unit, a warning unit, a knowledge sharing unit, a community formation unit, and a mountain climbing plan optimization unit. The data collection unit collects past mountain climbing data, such as GPS data, weather data, and accident data. The data collection unit can also collect climber equipment data and health data. The big data conversion unit accumulates the data collected by the data collection unit as big data. For example, the collected data is stored in cloud storage and used for analysis. The risk prediction unit analyzes the data accumulated by the big data conversion unit to predict risks. For example, it predicts risks for specific routes and weather conditions using statistical models and machine learning algorithms. The safety measure proposal unit proposes safety measures based on the risks predicted by the risk prediction unit. For example, it recommends equipment or suggests route changes. The real-time situation understanding unit collects and analyzes real-time data during mountain climbing, such as collecting sensor data and location information to understand the climber's situation. The warning unit issues a warning based on the data analyzed by the real-time situation understanding unit. For example, it sends out an audio alert or a notification message. The knowledge sharing unit shares knowledge based on the collected data and analysis results. For example, it shares past experiences and technical advice. The community formation unit manages the community formed by the knowledge sharing unit. For example, it manages the member selection criteria and activity details. The mountain climbing plan optimization unit optimizes the mountain climbing plan based on the data collected by the data collection unit. For example, it proposes the optimal mountain climbing route and schedule taking into account the climber's experience, physical strength, weather forecast, etc. In this way, the mountain climbing safety system according to the embodiment can reduce the risks involved in mountain climbing and ensure safe and secure mountain climbing.

[0030] The data collection unit uses a drone to collect video data of past climbing routes, and AI can analyze the video data to identify changes in the terrain and dangerous areas. The data collection unit, for example, uses a drone to collect video data of past climbing routes. For example, high-resolution video of mountainous areas is acquired, and AI analyzes the video to identify changes in the terrain and dangerous areas. The data collection unit also regularly collects video using a drone and records seasonal changes in the terrain. For example, it analyzes changes in the terrain after snowmelt and the risk of landslides. The data collection unit also uses drone video to create 3D maps of dangerous areas on past climbing routes and provide them to climbers. For example, it identifies areas with a high risk of falling and displays warnings. This improves climbing safety by identifying changes in the terrain and dangerous areas.

[0031] The data collection unit collects climbers' equipment data, and AI can analyze the relationship between equipment and accidents. For example, the data collection unit collects climbers' equipment data, and AI can analyze the relationship between equipment and accidents. For example, it investigates the impact of shoe type and backpack weight on the accident rate. The data collection unit also collects detailed data on the equipment used by climbers and compares the equipment's performance with the occurrence of accidents. For example, it analyzes the risk of climbers wearing highly waterproof shoes falling. The data collection unit also evaluates whether specific equipment is effective in preventing accidents based on the equipment data. For example, it analyzes whether using a lightweight backpack reduces fatigue and the risk of accidents. This helps select appropriate equipment by analyzing the relationship between equipment and accidents.

[0032] The data collection unit can collect data on outdoor activities other than mountain climbing and identify common risk factors. The data collection unit, for example, collects data on outdoor activities other than mountain climbing and identifies common risk factors. For example, it analyzes accident data from camping and hiking and extracts common risk factors. The data collection unit also integrates data on outdoor activities in general and analyzes risk factors. For example, it compares the effects of weather and terrain and identifies common risk factors. The data collection unit also complements risk factors for mountain climbing based on data on camping and hiking. For example, it evaluates the risks of rest points during mountain climbing based on data on accidents at campsites. In this way, safety measures can be strengthened by collecting data on outdoor activities in general and identifying common risk factors.

[0033] The data collection unit can record a climber's real-time impressions and situation using voice input. The data collection unit, for example, builds a system in which a climber uses voice input to record their real-time impressions and situation. For example, a climber leaves voice memos using a smartphone or wearable device while climbing. The data collection unit also analyzes the voice input data and saves the climber's impressions and situation as text data. For example, the voice data is automatically converted into text using voice recognition technology. The data collection unit also grasps the real-time situation based on the climber's voice input data and identifies risk factors. For example, the data collection unit extracts from the voice data moments when the climber feels fatigue or anxiety. In this way, risk factors can be identified by recording the climber's real-time impressions and situation.

[0034] The risk prediction unit analyzes past weather data and accident data to predict risks under specific weather conditions and propose specific countermeasures. For example, AI analyzes past weather data and accident data to predict risks under specific weather conditions. For example, it evaluates the risk of falling during heavy rain or strong winds. The risk prediction unit also proposes specific safety measures based on weather conditions. For example, it recommends wearing windbreakers during strong winds. The risk prediction unit also collects weather data in real time and builds a system to make risk predictions. For example, it proposes evacuation routes to respond to sudden changes in weather. This predicts risks under specific weather conditions and proposes specific countermeasures, thereby improving mountain climbing safety.

[0035] The risk prediction unit can analyze the climber's health data and make individual risk predictions. For example, the risk prediction unit collects the climber's health data and uses AI to make individual risk predictions. For example, it evaluates health risks during mountain climbing based on past medical history and physical fitness test results. The risk prediction unit also suggests individual safety measures based on the health data. For example, it recommends heart rate monitoring for climbers at risk of heart disease. The risk prediction unit also collects the climber's health data in real time and builds a system to make risk predictions. For example, it monitors fluctuations in body temperature and blood pressure and issues an alert if an abnormality is detected. In this way, the system analyzes the climber's health data and makes individual risk predictions, thereby improving the safety of mountain climbing.

[0036] The real-time situation assessment unit monitors climbers' body temperature and sweat rate, predicting the risk of heatstroke and issuing a warning. For example, the real-time situation assessment unit will build a system in which AI monitors climbers' body temperature and sweat rate in real time to predict the risk of heatstroke. For example, it will measure body temperature and sweat rate using a wearable device. The real-time situation assessment unit will also develop a system in which AI issues a warning when the risk of heatstroke increases. For example, it will notify the climber that "Your body temperature is rising. We recommend you hydrate and take a break." The real-time situation assessment unit will also evaluate the risk of heatstroke based on body temperature and sweat rate data and suggest preventive measures. For example, it will analyze the risk under specific temperature and humidity conditions and suggest appropriate countermeasures. This will improve mountain climbing safety by monitoring climbers' body temperature and sweat rate, predicting the risk of heatstroke, and issuing warnings.

[0037] The real-time situation assessment unit can analyze a climber's walking pattern and issue a warning if it detects abnormal movement. For example, the real-time situation assessment unit will build a system in which AI analyzes a climber's walking pattern in real time and detects abnormal movement. For example, it will monitor changes in walking speed and stride length. The real-time situation assessment unit will also develop a system in which AI issues a warning when abnormal movement is detected. For example, it will notify the climber, "An abnormality has been detected in your walking pattern. We recommend taking a break." The real-time situation assessment unit will also evaluate the risk of abnormal movement based on walking pattern data and suggest preventive measures. For example, it will suggest measures based on specific terrain or fatigue level. This will improve the safety of mountain climbing by analyzing a climber's walking pattern and issuing a warning if abnormal movement is detected.

[0038] The real-time situation assessment unit can share real-time situation assessment data with other climbers, thereby improving overall safety. The real-time situation assessment unit, for example, builds a platform for sharing real-time situation assessment data with other climbers. For example, climbers' location information and weather data are shared in real time. The real-time situation assessment unit also develops a system for improving overall safety by sharing real-time data with other climbers. For example, other climbers support climbers in dangerous situations. The real-time situation assessment unit also builds a system for climbers to share information with each other based on the real-time data. For example, advice and experiences about dangerous situations are shared in real time. In this way, overall safety is improved by sharing real-time situation assessment data with other climbers.

[0039] The real-time situation grasping unit can automatically optimize a climber's route based on real-time data and suggest a safe route. The real-time situation grasping unit, for example, builds a system that automatically optimizes a climber's route based on real-time data. For example, it analyzes weather and terrain data and suggests a safe route. The real-time situation grasping unit also develops a system that collects climber's location information and weather data in real time and suggests an optimal route. For example, it suggests a route to avoid dangerous situations. The real-time situation grasping unit also builds a system that dynamically adjusts a climber's route based on real-time data. For example, it suggests a route change to respond to a sudden change in weather. In this way, the climber's route is automatically optimized based on real-time data and a safe route is suggested, thereby improving the safety of mountain climbing.

[0040] The knowledge sharing department can use AI to analyze the content posted by climbers and automatically filter and share highly reliable information. For example, the knowledge sharing department will build a system in which AI analyzes the content posted by climbers and automatically filters out highly reliable information. For example, it will evaluate the reliability of the posted content and prioritize sharing of highly rated information. The knowledge sharing department will also analyze the content posted by climbers and develop an algorithm to automatically select highly reliable information. For example, it will evaluate reliability based on past posting history and ratings. The knowledge sharing department will also build a platform to share the filtered, highly reliable information with other climbers. For example, it will share reliable route information and equipment advice. In this way, the quality of information sharing among climbers will be improved by automatically filtering and sharing highly reliable information.

[0041] The knowledge sharing unit analyzes climbers' past posting history and identifies experts on a specific topic to answer questions within the community. The knowledge sharing unit, for example, analyzes climbers' past posting history and builds a system to identify experts on a specific topic. For example, it identifies climbers who are knowledgeable about specific routes or equipment. The knowledge sharing unit also develops a system to identify experts and have them answer questions within the community. For example, it automatically assigns questions on a specific topic to experts. The knowledge sharing unit also utilizes the knowledge of experts to build a system that promotes knowledge sharing within the community. For example, it shares the experts' answers with other climbers. In this way, experts on a specific topic can be identified and have them answer questions within the community, improving the quality of knowledge sharing.

[0042] The mountain climbing plan optimization unit can analyze a climber's past climbing history and propose an optimal individual climbing plan. For example, the mountain climbing plan optimization unit builds a system in which AI analyzes a climber's past climbing history and proposes an optimal individual climbing plan. For example, it proposes an optimal route and schedule based on past climbing data. The mountain climbing plan optimization unit also develops an algorithm that optimizes individual climbing plans based on the climber's past history data. For example, it proposes a plan that suits the climber's experience and physical strength. The mountain climbing plan optimization unit also analyzes past climbing history, understands the climber's preferences and patterns, and proposes an optimal plan. For example, it makes a plan taking into account the routes and seasons that were preferred in the past. In this way, by analyzing a climber's past climbing history and proposing an optimal individual climbing plan, the safety and efficiency of mountain climbing is improved.

[0043] The mountain climbing plan optimization unit can propose an optimal refueling plan based on data on a climber's diet and hydration intake. For example, the mountain climbing plan optimization unit collects data on a climber's diet and hydration intake and builds a system in which AI proposes an optimal refueling plan. For example, it proposes the appropriate timing for refueling based on energy consumption during a climb. The mountain climbing plan optimization unit also develops an algorithm that optimizes the refueling plan necessary for a climber to maintain their physical strength based on diet and hydration intake data. For example, it proposes the amount of specific food or beverage intake. The mountain climbing plan optimization unit also analyzes a climber's past diet and hydration intake data and proposes an individual refueling plan. For example, it proposes the optimal timing and amount of refueling based on past data. In this way, by proposing an optimal refueling plan based on a climber's diet and hydration intake data, the safety and efficiency of mountain climbing are improved.

[0044] The mountain climbing plan optimization unit can apply the mountain climbing plan optimization algorithm to other sports and activities. For example, the mountain climbing plan optimization unit builds a system that applies the mountain climbing plan optimization algorithm to other sports, such as marathons and cycling. For example, it optimizes marathon training plans and cycling route plans. The mountain climbing plan optimization unit also collects data from other sports and activities and applies the optimization algorithm to them. For example, it analyzes marathon training data and cycling route data. The mountain climbing plan optimization unit also deploys the optimization algorithm for multiple purposes to develop a system that optimizes plans for various sports and activities. For example, it proposes optimal training plans for marathons and cycling. In this way, applying the mountain climbing plan optimization algorithm to other sports and activities improves the safety and efficiency of a wide range of activities.

[0045] The mountain climbing plan optimization unit is capable of proposing an optimal plan based on data of multiple climbers, in response to group climbing. The mountain climbing plan optimization unit, for example, builds a system that proposes a mountain climbing plan that is compatible with group climbing. For example, it proposes an optimal route and schedule based on the experience and physical strength data of multiple climbers. The mountain climbing plan optimization unit also collects data of multiple climbers and develops an algorithm that optimizes a plan that is compatible with group climbing. For example, it proposes a plan that suits the physical strength and experience of the entire group. The mountain climbing plan optimization unit also builds a system that proposes an optimal mountain climbing plan based on data of group climbing. For example, it proposes a route and schedule that takes into account the safety of the entire group. In this way, the system can propose an optimal plan based on data of multiple climbers, in response to group climbing, thereby improving the safety and efficiency of the entire group.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit collects climbers' equipment data, allowing AI to analyze the relationship between equipment and accidents. For example, it investigates the impact of shoe type and backpack weight on accident rates. The data collection unit also collects detailed data on the equipment used by climbers and compares the performance of the equipment with the occurrence of accidents. For example, it analyzes the risk of climbers wearing highly waterproof shoes falling. The data collection unit also evaluates whether specific equipment is effective in preventing accidents based on the equipment data. For example, it analyzes whether using a lightweight backpack reduces fatigue and the risk of accidents. This helps select appropriate equipment by analyzing the relationship between equipment and accidents.

[0048] The data collection unit uses a drone to collect video data of past climbing routes, and AI can analyze the video data to identify changes in the terrain and dangerous areas. For example, high-resolution video of mountainous areas is acquired, and AI analyzes the video to identify changes in the terrain and dangerous areas. The data collection unit also regularly collects video using drones and records seasonal changes in the terrain. For example, it analyzes changes in the terrain after snowmelt and the risk of landslides. The data collection unit also uses drone video to create 3D maps of dangerous areas on past climbing routes and provide them to climbers. For example, it can identify areas with a high risk of falling and display warnings. This improves climbing safety by identifying changes in the terrain and dangerous areas.

[0049] The data collection unit can collect data on outdoor activities other than mountain climbing and identify common risk factors. For example, it analyzes accident data from camping and hiking to extract common risk factors. The data collection unit also integrates data on outdoor activities in general and analyzes risk factors. For example, it compares the effects of weather and terrain to identify common risk factors. The data collection unit also complements risk factors for mountain climbing based on camping and hiking data. For example, it evaluates the risks at rest points during mountain climbing based on accident data at campsites. In this way, by collecting data on outdoor activities in general and identifying common risk factors, safety measures can be strengthened.

[0050] The data collection unit can record a climber's real-time impressions and situation using voice input. For example, a system is constructed in which a climber uses voice input to record their real-time impressions and situation. For example, they leave voice memos using a smartphone or wearable device while climbing. The data collection unit also analyzes the voice input data and saves the climber's impressions and situation as text data. For example, voice recognition technology is used to automatically convert the voice data into text. The data collection unit also grasps the real-time situation based on the climber's voice input data and identifies risk factors. For example, it extracts from the voice data moments when the climber feels fatigue or anxiety. In this way, risk factors can be identified by recording the climber's real-time impressions and situation.

[0051] The risk prediction unit analyzes past weather data and accident data to predict risks under specific weather conditions and propose specific countermeasures. For example, AI can analyze past weather data and accident data to predict risks under specific weather conditions. For example, it evaluates the risk of falling during heavy rain or strong winds. The risk prediction unit also proposes specific safety measures based on weather conditions. For example, it may recommend wearing windbreakers during strong winds. The risk prediction unit also collects weather data in real time and builds a system to make risk predictions. For example, it may propose evacuation routes to respond to sudden changes in weather. This improves mountain climbing safety by predicting risks under specific weather conditions and proposing specific countermeasures.

[0052] The risk prediction unit can analyze a climber's health data and make individual risk predictions. For example, a climber's health data is collected and AI makes individual risk predictions. For example, health risks during mountain climbing are assessed based on past medical history and physical fitness test results. The risk prediction unit also suggests individual safety measures based on the health data. For example, it may recommend heart rate monitoring for climbers at risk of heart disease. The risk prediction unit also collects climbers' health data in real time and builds a system to make risk predictions. For example, it monitors fluctuations in body temperature and blood pressure and issues an alert if an abnormality is detected. In this way, the safety of mountain climbing is improved by analyzing a climber's health data and making individual risk predictions.

[0053] The real-time situation assessment unit can monitor climbers' body temperature and sweat rate, predict the risk of heatstroke, and issue a warning. For example, we will build a system in which AI monitors climbers' body temperature and sweat rate in real time to predict the risk of heatstroke. For example, we will measure body temperature and sweat rate using a wearable device. The real-time situation assessment unit will also develop a system in which AI issues a warning when the risk of heatstroke increases. For example, it will notify the climber, "Your body temperature is rising. We recommend you hydrate and take a break." The real-time situation assessment unit will also evaluate the risk of heatstroke based on body temperature and sweat rate data and suggest preventive measures. For example, it will analyze the risk under specific temperature and humidity conditions and suggest appropriate countermeasures. This will improve the safety of mountain climbing by monitoring climbers' body temperature and sweat rate, predicting the risk of heatstroke, and issuing warnings.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects past climbing data, such as GPS data, weather data, accident data, and climber equipment and health data. Step 2: The big data conversion unit accumulates the data collected by the data collection unit as big data. For example, the collected data is stored in cloud storage and used for analysis. Step 3: The risk prediction unit analyzes the data accumulated by the big data conversion unit to predict risks, for example, by using statistical models and machine learning algorithms to predict risks for specific routes and weather conditions. Step 4: The safety measures proposal unit proposes safety measures based on the risks predicted by the risk prediction unit, such as recommending equipment or changing the route. Step 5: The real-time situation assessment unit collects and analyzes real-time data during the climb, such as sensor data and location information, to assess the climber's situation. Step 6: The warning unit issues a warning based on the data analyzed by the real-time situation understanding unit, for example, by issuing a voice alert or a notification message. Step 7: The knowledge sharing department shares knowledge based on the collected data and analysis results, such as past experiences and technical advice. Step 8: The Community Building Department manages the community formed by the Knowledge Sharing Department, for example, by managing the selection criteria for members and the content of their activities. Step 9: The mountain climbing plan optimization unit optimizes the mountain climbing plan based on the data collected by the data collection unit. For example, it proposes the optimal mountain climbing route and schedule, taking into account the climber's experience, physical strength, weather forecast, etc.

[0056] (Example 2) A mountain climbing safety system according to an embodiment of the present invention is a system that reduces the risk of injury, accidents, and getting lost while climbing mountains, thereby enabling safe and secure mountain climbing. This system collects past mountain climbing data, stores it as big data, and utilizes AI to predict risks, propose safety measures, grasp the situation in real time and issue warnings, share knowledge and form communities, and optimize mountain climbing plans. As a result, the mountain climbing safety system reduces the risks involved in mountain climbing, enabling safe and secure mountain climbing.

[0057] A mountain climbing safety system according to an embodiment includes a data collection unit, a big data conversion unit, a risk prediction unit, a safety measure proposal unit, a real-time situation understanding unit, a warning unit, a knowledge sharing unit, a community formation unit, and a mountain climbing plan optimization unit. The data collection unit collects past mountain climbing data, such as GPS data, weather data, and accident data. The data collection unit can also collect climber equipment data and health data. The big data conversion unit accumulates the data collected by the data collection unit as big data. For example, the collected data is stored in cloud storage and used for analysis. The risk prediction unit analyzes the data accumulated by the big data conversion unit to predict risks. For example, it predicts risks for specific routes and weather conditions using statistical models and machine learning algorithms. The safety measure proposal unit proposes safety measures based on the risks predicted by the risk prediction unit. For example, it recommends equipment or suggests route changes. The real-time situation understanding unit collects and analyzes real-time data during mountain climbing, such as collecting sensor data and location information to understand the climber's situation. The warning unit issues a warning based on the data analyzed by the real-time situation understanding unit. For example, it sends out an audio alert or a notification message. The knowledge sharing unit shares knowledge based on the collected data and analysis results. For example, it shares past experiences and technical advice. The community formation unit manages the community formed by the knowledge sharing unit. For example, it manages the member selection criteria and activity details. The mountain climbing plan optimization unit optimizes the mountain climbing plan based on the data collected by the data collection unit. For example, it proposes the optimal mountain climbing route and schedule taking into account the climber's experience, physical strength, weather forecast, etc. In this way, the mountain climbing safety system according to the embodiment can reduce the risks involved in mountain climbing and ensure safe and secure mountain climbing.

[0058] The data collection unit uses a drone to collect video data of past climbing routes, and AI can analyze the video data to identify changes in the terrain and dangerous areas. The data collection unit, for example, uses a drone to collect video data of past climbing routes. For example, high-resolution video of mountainous areas is acquired, and AI analyzes the video to identify changes in the terrain and dangerous areas. The data collection unit also regularly collects video using a drone and records seasonal changes in the terrain. For example, it analyzes changes in the terrain after snowmelt and the risk of landslides. The data collection unit also uses drone video to create 3D maps of dangerous areas on past climbing routes and provide them to climbers. For example, it identifies areas with a high risk of falling and displays warnings. This improves climbing safety by identifying changes in the terrain and dangerous areas.

[0059] The data collection unit collects climbers' equipment data, and AI can analyze the relationship between equipment and accidents. For example, the data collection unit collects climbers' equipment data, and AI can analyze the relationship between equipment and accidents. For example, it investigates the impact of shoe type and backpack weight on the accident rate. The data collection unit also collects detailed data on the equipment used by climbers and compares the equipment's performance with the occurrence of accidents. For example, it analyzes the risk of climbers wearing highly waterproof shoes falling. The data collection unit also evaluates whether specific equipment is effective in preventing accidents based on the equipment data. For example, it analyzes whether using a lightweight backpack reduces fatigue and the risk of accidents. This helps select appropriate equipment by analyzing the relationship between equipment and accidents.

[0060] The data collection unit can use the emotion estimation function to collect data on fear and anxiety felt by the climber in the past and analyze it as risk factors. The data collection unit, for example, uses the emotion estimation function to collect data on fear and anxiety felt by the climber in the past. For example, it analyzes voice and facial expression data during climbing to identify moments of fear and anxiety. The data collection unit also analyzes fear and anxiety felt on specific routes and situations based on the climber's emotion data. For example, it collects data on fear felt on steep slopes or in bad weather. The data collection unit also analyzes the emotion data as risk factors to identify situations in which fear and anxiety are likely to be felt. For example, it identifies high-risk areas based on emotion data at specific altitudes and terrain. In this way, high-risk situations can be identified by analyzing the fear and anxiety data as risk factors.

[0061] The data collection unit can collect data on outdoor activities other than mountain climbing and identify common risk factors. The data collection unit, for example, collects data on outdoor activities other than mountain climbing and identifies common risk factors. For example, it analyzes accident data from camping and hiking and extracts common risk factors. The data collection unit also integrates data on outdoor activities in general and analyzes risk factors. For example, it compares the effects of weather and terrain and identifies common risk factors. The data collection unit also complements risk factors for mountain climbing based on data on camping and hiking. For example, it evaluates the risks of rest points during mountain climbing based on data on accidents at campsites. In this way, safety measures can be strengthened by collecting data on outdoor activities in general and identifying common risk factors.

[0062] The data collection unit can record a climber's real-time impressions and situation using voice input. The data collection unit, for example, builds a system in which a climber uses voice input to record their real-time impressions and situation. For example, a climber leaves voice memos using a smartphone or wearable device while climbing. The data collection unit also analyzes the voice input data and saves the climber's impressions and situation as text data. For example, the voice data is automatically converted into text using voice recognition technology. The data collection unit also grasps the real-time situation based on the climber's voice input data and identifies risk factors. For example, the data collection unit extracts from the voice data moments when the climber feels fatigue or anxiety. In this way, risk factors can be identified by recording the climber's real-time impressions and situation.

[0063] The data collection unit uses the emotion estimation function to collect data on positive emotions felt by climbers while climbing, and can use the data to identify safe routes. The data collection unit, for example, uses the emotion estimation function to collect positive emotions felt by climbers while climbing. For example, it analyzes audio and facial expression data during climbing to identify moments when climbers felt joy or a sense of accomplishment. The data collection unit also identifies safe routes based on the positive emotion data. For example, it analyzes routes on which climbers felt positive emotions and recommends them as safe routes. The data collection unit also uses the climbers' positive emotion data to help optimize climbing plans. For example, it prioritizes suggesting routes that are more likely to evoke positive emotions. In this way, collecting positive emotion data and using it to identify safe routes improves the safety of climbing.

[0064] The risk prediction unit analyzes past weather data and accident data to predict risks under specific weather conditions and propose specific countermeasures. For example, AI analyzes past weather data and accident data to predict risks under specific weather conditions. For example, it evaluates the risk of falling during heavy rain or strong winds. The risk prediction unit also proposes specific safety measures based on weather conditions. For example, it recommends wearing windbreakers during strong winds. The risk prediction unit also collects weather data in real time and builds a system to make risk predictions. For example, it proposes evacuation routes to respond to sudden changes in weather. This predicts risks under specific weather conditions and proposes specific countermeasures, thereby improving mountain climbing safety.

[0065] The risk prediction unit can analyze the climber's health data and make individual risk predictions. For example, the risk prediction unit collects the climber's health data and uses AI to make individual risk predictions. For example, it evaluates health risks during mountain climbing based on past medical history and physical fitness test results. The risk prediction unit also suggests individual safety measures based on the health data. For example, it recommends heart rate monitoring for climbers at risk of heart disease. The risk prediction unit also collects the climber's health data in real time and builds a system to make risk predictions. For example, it monitors fluctuations in body temperature and blood pressure and issues an alert if an abnormality is detected. In this way, the system analyzes the climber's health data and makes individual risk predictions, thereby improving the safety of mountain climbing.

[0066] The risk prediction unit can use the emotion estimation function to monitor a climber's stress level in real time and suggest taking a break when stress increases. The risk prediction unit, for example, uses the emotion estimation function to monitor a climber's stress level in real time. For example, it analyzes heart rate and facial expression data to detect signs of stress. The risk prediction unit also builds a system in which AI suggests taking a break when stress levels increase. For example, it notifies the climber, "Your stress level is increasing. We recommend taking a break." The risk prediction unit also adjusts the climbing plan based on the stress data. For example, it suggests avoiding routes that are likely to increase stress. This improves climbing safety by monitoring a climber's stress level in real time and suggesting breaks at appropriate times.

[0067] The real-time situation assessment unit monitors climbers' body temperature and sweat rate, predicting the risk of heatstroke and issuing a warning. For example, the real-time situation assessment unit will build a system in which AI monitors climbers' body temperature and sweat rate in real time to predict the risk of heatstroke. For example, it will measure body temperature and sweat rate using a wearable device. The real-time situation assessment unit will also develop a system in which AI issues a warning when the risk of heatstroke increases. For example, it will notify the climber that "Your body temperature is rising. We recommend you hydrate and take a break." The real-time situation assessment unit will also evaluate the risk of heatstroke based on body temperature and sweat rate data and suggest preventive measures. For example, it will analyze the risk under specific temperature and humidity conditions and suggest appropriate countermeasures. This will improve mountain climbing safety by monitoring climbers' body temperature and sweat rate, predicting the risk of heatstroke, and issuing warnings.

[0068] The real-time situation assessment unit can analyze a climber's walking pattern and issue a warning if it detects abnormal movement. For example, the real-time situation assessment unit will build a system in which AI analyzes a climber's walking pattern in real time and detects abnormal movement. For example, it will monitor changes in walking speed and stride length. The real-time situation assessment unit will also develop a system in which AI issues a warning when abnormal movement is detected. For example, it will notify the climber, "An abnormality has been detected in your walking pattern. We recommend taking a break." The real-time situation assessment unit will also evaluate the risk of abnormal movement based on walking pattern data and suggest preventive measures. For example, it will suggest measures based on specific terrain or fatigue level. This will improve the safety of mountain climbing by analyzing a climber's walking pattern and issuing a warning if abnormal movement is detected.

[0069] The real-time situation understanding unit can use the emotion estimation function to provide relaxing music or guidance when a climber feels fear or anxiety. For example, the real-time situation understanding unit uses the emotion estimation function to build a system that provides relaxing music when a climber feels fear or anxiety. For example, it analyzes the climber's facial expressions and voice data and plays appropriate music. The real-time situation understanding unit also develops a system that uses AI to provide relaxation guidance when a climber feels fear or anxiety. For example, it provides audio guidance such as "Take a deep breath and relax." The real-time situation understanding unit also suggests an environment in which the climber can relax based on the emotion data. For example, it suggests ways to relax in specific places or situations. This improves the safety of mountain climbing by providing relaxing music or guidance when a climber feels fear or anxiety.

[0070] The real-time situation assessment unit can share real-time situation assessment data with other climbers, thereby improving overall safety. The real-time situation assessment unit, for example, builds a platform for sharing real-time situation assessment data with other climbers. For example, climbers' location information and weather data are shared in real time. The real-time situation assessment unit also develops a system for improving overall safety by sharing real-time data with other climbers. For example, other climbers support climbers in dangerous situations. The real-time situation assessment unit also builds a system for climbers to share information with each other based on the real-time data. For example, advice and experiences about dangerous situations are shared in real time. In this way, overall safety is improved by sharing real-time situation assessment data with other climbers.

[0071] The real-time situation grasping unit can automatically optimize a climber's route based on real-time data and suggest a safe route. The real-time situation grasping unit, for example, builds a system that automatically optimizes a climber's route based on real-time data. For example, it analyzes weather and terrain data and suggests a safe route. The real-time situation grasping unit also develops a system that collects climber's location information and weather data in real time and suggests an optimal route. For example, it suggests a route to avoid dangerous situations. The real-time situation grasping unit also builds a system that dynamically adjusts a climber's route based on real-time data. For example, it suggests a route change to respond to a sudden change in weather. In this way, the climber's route is automatically optimized based on real-time data and a safe route is suggested, thereby improving the safety of mountain climbing.

[0072] The real-time situation grasping unit can use the emotion estimation function to identify locations where climbers felt positive emotions and recommend those locations to other climbers. For example, the real-time situation grasping unit uses the emotion estimation function to build a system for identifying locations where climbers felt positive emotions. For example, it analyzes the climber's facial expressions and voice data to identify locations where they felt joy or a sense of accomplishment. The real-time situation grasping unit also develops a system for recommending locations where climbers felt positive emotions to other climbers. For example, it notifies the climber that "This spot is a location where many climbers felt joy." The real-time situation grasping unit also identifies locations where climbers are likely to feel positive emotions based on the emotion data and reflects this in the climbing plan. For example, it prioritizes the suggestion of routes that are likely to evoke positive emotions. This allows climbers to identify locations where they felt positive emotions and recommend those locations to other climbers, thereby improving the enjoyment of climbing.

[0073] The knowledge sharing department can use AI to analyze the content posted by climbers and automatically filter and share highly reliable information. For example, the knowledge sharing department will build a system in which AI analyzes the content posted by climbers and automatically filters out highly reliable information. For example, it will evaluate the reliability of the posted content and prioritize sharing of highly rated information. The knowledge sharing department will also analyze the content posted by climbers and develop an algorithm to automatically select highly reliable information. For example, it will evaluate reliability based on past posting history and ratings. The knowledge sharing department will also build a platform to share the filtered, highly reliable information with other climbers. For example, it will share reliable route information and equipment advice. In this way, the quality of information sharing among climbers will be improved by automatically filtering and sharing highly reliable information.

[0074] The knowledge sharing unit analyzes climbers' past posting history and identifies experts on a specific topic to answer questions within the community. The knowledge sharing unit, for example, analyzes climbers' past posting history and builds a system to identify experts on a specific topic. For example, it identifies climbers who are knowledgeable about specific routes or equipment. The knowledge sharing unit also develops a system to identify experts and have them answer questions within the community. For example, it automatically assigns questions on a specific topic to experts. The knowledge sharing unit also utilizes the knowledge of experts to build a system that promotes knowledge sharing within the community. For example, it shares the experts' answers with other climbers. In this way, experts on a specific topic can be identified and have them answer questions within the community, improving the quality of knowledge sharing.

[0075] The knowledge sharing unit can use the emotion estimation function to prioritize the display of posts that evoke positive emotions, thereby revitalizing the community. The knowledge sharing unit, for example, uses the emotion estimation function to build a system that prioritizes the display of posts that evoke positive emotions. For example, posts that evoke a sense of joy or accomplishment are prioritized for display. The knowledge sharing unit also develops a system that revitalizes the community based on posts that evoke positive emotions. For example, it encourages comments and likes on positive posts. The knowledge sharing unit also identifies posts that evoke positive emotions based on emotion data and adjusts their display priority within the community. For example, posts that are likely to evoke positive emotions are displayed prominently. This helps revitalize the community by prioritizing the display of posts that evoke positive emotions.

[0076] The mountain climbing plan optimization unit can analyze a climber's past climbing history and propose an optimal individual climbing plan. For example, the mountain climbing plan optimization unit builds a system in which AI analyzes a climber's past climbing history and proposes an optimal individual climbing plan. For example, it proposes an optimal route and schedule based on past climbing data. The mountain climbing plan optimization unit also develops an algorithm that optimizes individual climbing plans based on the climber's past history data. For example, it proposes a plan that suits the climber's experience and physical strength. The mountain climbing plan optimization unit also analyzes past climbing history, understands the climber's preferences and patterns, and proposes an optimal plan. For example, it makes a plan taking into account the routes and seasons that were preferred in the past. In this way, by analyzing a climber's past climbing history and proposing an optimal individual climbing plan, the safety and efficiency of mountain climbing is improved.

[0077] The mountain climbing plan optimization unit can propose an optimal refueling plan based on data on a climber's diet and hydration intake. For example, the mountain climbing plan optimization unit collects data on a climber's diet and hydration intake and builds a system in which AI proposes an optimal refueling plan. For example, it proposes the appropriate timing for refueling based on energy consumption during a climb. The mountain climbing plan optimization unit also develops an algorithm that optimizes the refueling plan necessary for a climber to maintain their physical strength based on diet and hydration intake data. For example, it proposes the amount of specific food or beverage intake. The mountain climbing plan optimization unit also analyzes a climber's past diet and hydration intake data and proposes an individual refueling plan. For example, it proposes the optimal timing and amount of refueling based on past data. In this way, by proposing an optimal refueling plan based on a climber's diet and hydration intake data, the safety and efficiency of mountain climbing are improved.

[0078] The mountain climbing plan optimization unit can use the emotion estimation function to preferentially suggest routes that make climbers feel positive emotions. The mountain climbing plan optimization unit, for example, uses the emotion estimation function to identify routes that make climbers feel positive emotions and build a system that preferentially suggests them. For example, it proposes routes that make climbers feel joy or a sense of accomplishment based on past emotion data. The mountain climbing plan optimization unit also develops an algorithm that preferentially suggests routes that are likely to make climbers feel positive emotions based on the climber's emotion data. For example, it proposes routes that are likely to make climbers feel positive emotions with specific landscapes or terrain. The mountain climbing plan optimization unit also utilizes the emotion estimation data to build a system that preferentially suggests routes that make climbers feel positive emotions. For example, it proposes optimal routes based on past emotion data. In this way, by preferentially suggesting routes that make climbers feel positive emotions, the enjoyment and safety of mountain climbing is improved.

[0079] The mountain climbing plan optimization unit can apply the mountain climbing plan optimization algorithm to other sports and activities. For example, the mountain climbing plan optimization unit builds a system that applies the mountain climbing plan optimization algorithm to other sports, such as marathons and cycling. For example, it optimizes marathon training plans and cycling route plans. The mountain climbing plan optimization unit also collects data from other sports and activities and applies the optimization algorithm to them. For example, it analyzes marathon training data and cycling route data. The mountain climbing plan optimization unit also deploys the optimization algorithm for multiple purposes to develop a system that optimizes plans for various sports and activities. For example, it proposes optimal training plans for marathons and cycling. In this way, applying the mountain climbing plan optimization algorithm to other sports and activities improves the safety and efficiency of a wide range of activities.

[0080] The mountain climbing plan optimization unit is capable of proposing an optimal plan based on data of multiple climbers, in response to group climbing. The mountain climbing plan optimization unit, for example, builds a system that proposes a mountain climbing plan that is compatible with group climbing. For example, it proposes an optimal route and schedule based on the experience and physical strength data of multiple climbers. The mountain climbing plan optimization unit also collects data of multiple climbers and develops an algorithm that optimizes a plan that is compatible with group climbing. For example, it proposes a plan that suits the physical strength and experience of the entire group. The mountain climbing plan optimization unit also builds a system that proposes an optimal mountain climbing plan based on data of group climbing. For example, it proposes a route and schedule that takes into account the safety of the entire group. In this way, the system can propose an optimal plan based on data of multiple climbers, in response to group climbing, thereby improving the safety and efficiency of the entire group.

[0081] The mountain climbing plan optimization unit can use the emotion estimation function to propose a plan that avoids routes on which climbers have felt negative emotions. The mountain climbing plan optimization unit, for example, uses the emotion estimation function to identify routes on which climbers have felt negative emotions and build a system that proposes plans to avoid them. For example, routes on which climbers have felt fear or anxiety based on past emotion data are avoided. The mountain climbing plan optimization unit also develops an algorithm that proposes a plan that avoids routes on which climbers are likely to feel negative emotions based on climber emotion data. For example, routes on which climbers are likely to feel negative emotions in specific terrain or situations are avoided. The mountain climbing plan optimization unit also utilizes emotion estimation data to build a system that proposes a plan that avoids routes on which climbers have felt negative emotions. For example, it proposes an optimal route based on past emotion data. In this way, by proposing a plan that avoids routes on which climbers have felt negative emotions, the safety and comfort of mountain climbing is improved.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The data collection unit collects climbers' equipment data, allowing AI to analyze the relationship between equipment and accidents. For example, it investigates the impact of shoe type and backpack weight on accident rates. The data collection unit also collects detailed data on the equipment used by climbers and compares the performance of the equipment with the occurrence of accidents. For example, it analyzes the risk of climbers wearing highly waterproof shoes falling. The data collection unit also evaluates whether specific equipment is effective in preventing accidents based on the equipment data. For example, it analyzes whether using a lightweight backpack reduces fatigue and the risk of accidents. This helps select appropriate equipment by analyzing the relationship between equipment and accidents.

[0084] The data collection unit uses a drone to collect video data of past climbing routes, and AI can analyze the video data to identify changes in the terrain and dangerous areas. For example, high-resolution video of mountainous areas is acquired, and AI analyzes the video to identify changes in the terrain and dangerous areas. The data collection unit also regularly collects video using drones and records seasonal changes in the terrain. For example, it analyzes changes in the terrain after snowmelt and the risk of landslides. The data collection unit also uses drone video to create 3D maps of dangerous areas on past climbing routes and provide them to climbers. For example, it can identify areas with a high risk of falling and display warnings. This improves climbing safety by identifying changes in the terrain and dangerous areas.

[0085] The data collection unit can use the emotion estimation function to collect data on the fear and anxiety that a climber has felt in the past and analyze it as risk factors. For example, it can analyze voice and facial expression data during climbing to identify moments of fear and anxiety. The data collection unit also analyzes the fear and anxiety felt on specific routes and situations based on the climber's emotion data. For example, it can collect data on fear felt on steep slopes or in bad weather. The data collection unit also analyzes the emotion data as risk factors to identify situations in which fear and anxiety are likely to occur. For example, it can identify high-risk locations based on emotion data at specific altitudes and terrain. In this way, high-risk situations can be identified by analyzing the fear and anxiety data as risk factors.

[0086] The data collection unit can collect data on outdoor activities other than mountain climbing and identify common risk factors. For example, it analyzes accident data from camping and hiking to extract common risk factors. The data collection unit also integrates data on outdoor activities in general and analyzes risk factors. For example, it compares the effects of weather and terrain to identify common risk factors. The data collection unit also complements risk factors for mountain climbing based on camping and hiking data. For example, it evaluates the risks at rest points during mountain climbing based on accident data at campsites. In this way, by collecting data on outdoor activities in general and identifying common risk factors, safety measures can be strengthened.

[0087] The data collection unit can record a climber's real-time impressions and situation using voice input. For example, a system is constructed in which a climber uses voice input to record their real-time impressions and situation. For example, they leave voice memos using a smartphone or wearable device while climbing. The data collection unit also analyzes the voice input data and saves the climber's impressions and situation as text data. For example, voice recognition technology is used to automatically convert the voice data into text. The data collection unit also grasps the real-time situation based on the climber's voice input data and identifies risk factors. For example, it extracts from the voice data moments when the climber feels fatigue or anxiety. In this way, risk factors can be identified by recording the climber's real-time impressions and situation.

[0088] The data collection unit uses the emotion estimation function to collect data on positive emotions felt by climbers while climbing, and can use this data to identify safe routes. For example, it can analyze voice and facial expression data during climbing to identify moments when climbers felt joy or a sense of accomplishment. The data collection unit also identifies safe routes based on the positive emotion data. For example, it can analyze routes where climbers felt positive emotions and recommend them as safe routes. The data collection unit also uses the climbers' positive emotion data to help optimize climbing plans. For example, it can prioritize suggesting routes that are more likely to evoke positive emotions. In this way, collecting positive emotion data and using it to identify safe routes improves the safety of climbing.

[0089] The risk prediction unit analyzes past weather data and accident data to predict risks under specific weather conditions and propose specific countermeasures. For example, AI can analyze past weather data and accident data to predict risks under specific weather conditions. For example, it evaluates the risk of falling during heavy rain or strong winds. The risk prediction unit also proposes specific safety measures based on weather conditions. For example, it may recommend wearing windbreakers during strong winds. The risk prediction unit also collects weather data in real time and builds a system to make risk predictions. For example, it may propose evacuation routes to respond to sudden changes in weather. This improves mountain climbing safety by predicting risks under specific weather conditions and proposing specific countermeasures.

[0090] The risk prediction unit can analyze a climber's health data and make individual risk predictions. For example, a climber's health data is collected and AI makes individual risk predictions. For example, health risks during mountain climbing are assessed based on past medical history and physical fitness test results. The risk prediction unit also suggests individual safety measures based on the health data. For example, it may recommend heart rate monitoring for climbers at risk of heart disease. The risk prediction unit also collects climbers' health data in real time and builds a system to make risk predictions. For example, it monitors fluctuations in body temperature and blood pressure and issues an alert if an abnormality is detected. In this way, the safety of mountain climbing is improved by analyzing a climber's health data and making individual risk predictions.

[0091] The risk prediction unit uses the emotion estimation function to monitor a climber's stress level in real time and can suggest taking a break if stress increases. For example, the emotion estimation function can be used to monitor a climber's stress level in real time, for example by analyzing heart rate and facial expression data to detect signs of stress. The risk prediction unit also builds a system in which AI suggests taking a break when stress levels increase, for example by notifying the climber, "Your stress level is increasing. We recommend taking a break." The risk prediction unit also adjusts the climbing plan based on the stress data, for example by suggesting that routes that are likely to increase stress be avoided. This improves climbing safety by monitoring a climber's stress level in real time and suggesting breaks at appropriate times.

[0092] The real-time situation assessment unit can monitor climbers' body temperature and sweat rate, predict the risk of heatstroke, and issue a warning. For example, we will build a system in which AI monitors climbers' body temperature and sweat rate in real time to predict the risk of heatstroke. For example, we will measure body temperature and sweat rate using a wearable device. The real-time situation assessment unit will also develop a system in which AI issues a warning when the risk of heatstroke increases. For example, it will notify the climber, "Your body temperature is rising. We recommend you hydrate and take a break." The real-time situation assessment unit will also evaluate the risk of heatstroke based on body temperature and sweat rate data and suggest preventive measures. For example, it will analyze the risk under specific temperature and humidity conditions and suggest appropriate countermeasures. This will improve the safety of mountain climbing by monitoring climbers' body temperature and sweat rate, predicting the risk of heatstroke, and issuing warnings.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection unit collects past climbing data, such as GPS data, weather data, accident data, and climber equipment and health data. Step 2: The big data conversion unit accumulates the data collected by the data collection unit as big data. For example, the collected data is stored in cloud storage and used for analysis. Step 3: The risk prediction unit analyzes the data accumulated by the big data conversion unit to predict risks, for example, by using statistical models and machine learning algorithms to predict risks for specific routes and weather conditions. Step 4: The safety measures proposal unit proposes safety measures based on the risks predicted by the risk prediction unit, such as recommending equipment or changing the route. Step 5: The real-time situation assessment unit collects and analyzes real-time data during the climb, such as sensor data and location information, to assess the climber's situation. Step 6: The warning unit issues a warning based on the data analyzed by the real-time situation understanding unit, for example, by issuing a voice alert or a notification message. Step 7: The knowledge sharing department shares knowledge based on the collected data and analysis results, such as past experiences and technical advice. Step 8: The Community Building Department manages the community formed by the Knowledge Sharing Department, for example, by managing the selection criteria for members and the content of their activities. Step 9: The mountain climbing plan optimization unit optimizes the mountain climbing plan based on the data collected by the data collection unit. For example, it proposes the optimal mountain climbing route and schedule, taking into account the climber's experience, physical strength, weather forecast, etc.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects past climbing data; a big data generation unit that accumulates the data collected by the data collection unit as big data; a risk prediction unit that analyzes the data accumulated by the big data generation unit and predicts risks; a safety measure proposal unit that proposes safety measures based on the risks predicted by the risk prediction unit; A real-time situation assessment unit that collects and analyzes real-time data during mountain climbing; a warning unit that issues a warning based on the data analyzed by the real-time situation grasping unit; The knowledge sharing department shares knowledge based on collected data and analysis results, a community forming unit that manages the community formed by the knowledge sharing unit; a mountain climbing plan optimization unit that optimizes the mountain climbing plan based on the data collected by the data collection unit. A system characterized by:

2. The data collection unit Drones are used to collect video data of past climbing routes, and the AI ​​analyzes the video data to identify changes in the terrain and dangerous areas. The system of claim 1 .

3. The data collection unit The AI ​​will collect climbers' equipment data and analyze the relationship between equipment and accidents. The system of claim 1 .

4. The data collection unit Collect data on the fears and anxieties that climbers have experienced in the past and analyze them as risk factors. The system of claim 1 .

5. The data collection unit Collect data on outdoor activities other than mountain climbing to identify common risk factors The system of claim 1 .

6. The data collection unit Recording climbers' real-time impressions and situations using voice input The system of claim 1 .

7. The data collection unit Collecting data on the positive emotions experienced by climbers while climbing and using it to identify safe routes The system of claim 1 .

Images