System

Abstract

An object of a system according to an embodiment is to monitor the state of forests and climate change in real time and quickly take appropriate measures.SOLUTION: A system includes a monitoring part, an alarm part, a reproduction plan proposal part, an illegal activity detection part, and an educational program generation part. The monitoring unit monitors the state and density of forests and climate change in real time. The alarm unit predicts a fire risk based on the data acquired by the monitoring unit, and issues an alarm. The regeneration plan proposing section proposes a forest regeneration plan based on the data acquired by the monitoring section. The illegal activity detection unit detects illegal felling or hunting through analysis of an unmanned drone or a satellite image. The education program generation unit provides information on forest protection and generates an education program.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] Conventional technology has the drawback of making it difficult to monitor forest conditions and climate change in real time and to take appropriate measures quickly.

[0005] The system according to the embodiment aims to monitor forest conditions and climate change in real time and to take appropriate measures promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an alarm unit, a restoration plan proposal unit, an illegal activity detection unit, and an educational program generation unit. The monitoring unit monitors forest conditions, density, and climate change in real time. The alarm unit predicts fire risks and issues warnings based on data acquired by the monitoring unit. The restoration plan proposal unit proposes forest restoration plans based on data acquired by the monitoring unit. The illegal activity detection unit detects illegal logging and poaching through analysis of unmanned drone and satellite images. The educational program generation unit provides information and generates educational programs related to forest protection. [Effects of the Invention]

[0007] The system according to the embodiment can monitor forest conditions and climate change in real time and take appropriate measures promptly. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The forest protection system according to an embodiment of the present invention uses AI to monitor forest conditions, density, and climate change in real time, and provides early warnings, guides forest fire prevention activities, and proposes forest regeneration plans. This allows the forest protection system to protect and manage forests efficiently and effectively.

[0029] A forest protection system according to an embodiment includes a monitoring unit, an alert unit, a restoration plan proposal unit, an illegal activity detection unit, and an educational program generation unit. The monitoring unit monitors forest conditions, density, and climate change in real time. For example, AI analyzes image data acquired from unmanned drones or satellites to assess forest health, tree density, and the impact of climate change. The alert unit predicts fire risk and issues an alert based on the data acquired by the monitoring unit. For example, it analyzes data such as temperature, humidity, and wind speed, and issues an alert if the risk of fire increases. The restoration plan proposal unit proposes a forest restoration plan based on the data acquired by the monitoring unit. For example, it proposes optimal tree planting plans and restoration methods for areas that have been logged or damaged by fire. The illegal activity detection unit detects illegal logging and poaching by analyzing unmanned drone and satellite images. For example, AI analyzes image data and issues an alert and notifies relevant authorities if it detects abnormal activity. The educational program generation unit provides information and generates educational programs related to forest protection. For example, AI can generate content explaining the importance of forests and the need for conservation efforts, and provide it as educational programs in schools and communities. This allows forest protection systems to protect and manage forests efficiently and effectively. For example, real-time monitoring can constantly monitor forest health, and early warning systems can reduce fire risks. Forest protection efforts can also be strengthened through early detection of illegal logging and poaching. Furthermore, information and education programs can raise awareness of forest conservation throughout the local community.

[0030] The monitoring unit can analyze acoustic data from the forest and detect animal activity or abnormal sounds to assess the health of the forest. For example, the monitoring unit uses AI to analyze acoustic data collected from microphones installed in the forest to detect animal activity or abnormal sounds. For example, it can detect the cries of specific animals or abnormal silence to assess the health of the forest. In this way, the health of the forest can be assessed by analyzing the acoustic data.

[0031] The monitoring unit analyzes data from the soil sensor and can monitor the soil's nutritional status and moisture content in real time. For example, the monitoring unit uses AI to analyze data collected from the soil sensor and monitor the soil's nutritional status and moisture content in real time. For example, it analyzes the soil's pH value and moisture content to evaluate the health of the forest. This makes it possible to monitor the soil's nutritional status and moisture content in real time.

[0032] The monitoring unit analyzes the ecosystem of plants and animals within the forest and can support conservation efforts for specific species. For example, AI can analyze the ecosystem of plants and animals within the forest and can support conservation efforts for specific species. For example, it can identify the habitats of endangered species and carry out conservation efforts. This can support conservation efforts for specific species.

[0033] The monitoring unit can analyze acoustic data from the forest and detect the sounds of illegal logging and poaching. For example, the monitoring unit uses AI to analyze acoustic data collected from microphones installed in the forest and detect the sounds of illegal logging and poaching. For example, it detects the sounds of chainsaws and gunfire and issues an alarm. This makes it possible to detect the sounds of illegal logging and poaching.

[0034] The alarm unit can detect minute temperature changes in the forest and issue an alarm in the early stages of a fire. For example, the alarm unit uses AI to analyze data collected from temperature sensors installed in the forest and detect minute temperature changes. For example, it can detect a temperature rise in the early stages of a fire and issue an alarm. This makes it possible to issue an alarm in the early stages of a fire by detecting minute temperature changes.

[0035] The warning unit can analyze data on wind direction and speed and predict the spread of a fire. For example, AI can analyze data on wind direction and speed and predict the spread of a fire. For example, it can predict the direction of a fire's progress based on changes in wind direction and speed and propose appropriate fire prevention measures. In this way, by analyzing data on wind direction and speed, it is possible to predict the spread of a fire.

[0036] The alarm unit can analyze the evacuation behavior of animals in the forest and predict the occurrence of fires. For example, the alarm unit uses AI to analyze the evacuation behavior of animals in the forest and predict the occurrence of fires. For example, it predicts the risk of fire when animals exhibit abnormal behavior. In this way, it is possible to predict the occurrence of fires by analyzing the evacuation behavior of animals.

[0037] The warning unit can analyze data on humidity and precipitation within the forest and propose measures to reduce the risk of fire. For example, AI can analyze data on humidity and precipitation within the forest and propose measures to reduce the risk of fire. For example, strengthening fire prevention measures in areas with low humidity. By analyzing humidity and precipitation data, it is possible to propose measures to reduce the risk of fire.

[0038] The regeneration plan proposal unit can analyze the nutritional state of the soil and propose an optimal tree planting plan. For example, the regeneration plan proposal unit uses AI to analyze data collected from a soil sensor and evaluate the nutritional state of the soil. For example, it analyzes the pH value and nutrient content of the soil and proposes an optimal tree planting plan. In this way, by analyzing the nutritional state of the soil, it is possible to propose an optimal tree planting plan.

[0039] The regeneration plan proposal unit can analyze past forest regeneration data and propose regeneration plans based on success stories. For example, AI can analyze past forest regeneration data and propose regeneration plans based on success stories. For example, a new plan can be created by referring to past successful tree planting plans. This makes it possible to propose regeneration plans based on past success stories.

[0040] The regeneration plan proposal unit can propose a plan that optimizes the materials and labor required for forest regeneration. For example, the regeneration plan proposal unit uses AI to propose a plan that optimizes the materials and labor required for forest regeneration. For example, it creates a plan that minimizes the amount of materials and labor required. In this way, by proposing a plan that optimizes materials and labor, efficient forest regeneration can be achieved.

[0041] The revitalization plan proposal unit can monitor the progress of the revitalization plan in real time and revise the plan as necessary. For example, the revitalization plan proposal unit uses AI to monitor the progress of the revitalization plan in real time and revise the plan as necessary. For example, it monitors the progress of tree planting and adjusts the plan. This makes it possible to monitor the progress of the revitalization plan in real time and revise the plan as necessary.

[0042] The illegal activity detection unit can analyze infrared images at night and detect illegal activities. For example, the illegal activity detection unit analyzes infrared images acquired by unmanned drones or satellites at night using AI to detect illegal logging or poaching activities. For example, it can detect abnormal heat sources in infrared images and issue an alarm. This makes it possible to detect illegal activities by analyzing infrared images at night.

[0043] The illegal activity detection unit can analyze acoustic data and detect the sounds of illegal logging and poaching. For example, the illegal activity detection unit uses AI to analyze acoustic data collected from microphones installed in the forest and detect the sounds of illegal logging and poaching. For example, it detects the sounds of chainsaws and gunfire and issues an alarm. In this way, the sound of illegal logging and poaching can be detected by analyzing the acoustic data.

[0044] The illegal activity detection unit can analyze the behavioral patterns of animals in the forest and predict the risk of poaching. For example, the illegal activity detection unit uses AI to analyze the behavioral patterns of animals in the forest and predict the risk of poaching. For example, it predicts the risk of poaching if an animal exhibits abnormal behavior. In this way, it is possible to predict the risk of poaching by analyzing the behavioral patterns of animals.

[0045] The illegal activity detection unit can learn patterns of illegal activity and propose preventive measures. For example, the illegal activity detection unit uses AI to analyze data on past illegal activities and learn patterns of illegal activity. For example, it can grasp patterns of illegal activity in specific time periods or areas and propose preventive measures. In this way, by learning patterns of illegal activity, it can propose preventive measures.

[0046] The educational program generation unit can analyze the latest research data on forest protection and reflect it in the educational program. For example, the educational program generation unit uses AI to analyze the latest research data on forest protection and reflect it in the educational program. For example, the educational content is updated based on the latest research results. In this way, the latest research data can be analyzed and reflected in the educational program.

[0047] The educational program generation unit can analyze the forest protection issues of each region and propose educational programs specialized for that region. For example, the educational program generation unit uses AI to analyze the forest protection issues of each region and propose educational programs specialized for that region. For example, educational content is created to solve forest protection issues in a specific region. In this way, by analyzing the issues of each region, it is possible to propose educational programs specialized for that region.

[0048] The educational program generation unit can develop games and simulations related to forest protection and incorporate them into educational programs. For example, the educational program generation unit can use AI to develop games related to forest protection and incorporate them into educational programs. For example, it can create a simulation game for learning about the importance of forest protection. By developing games and simulations, they can be incorporated into educational programs.

[0049] The educational program generation unit generates documentary footage on forest protection and can provide visually appealing educational content. For example, the educational program generation unit uses AI to generate documentary footage on forest protection and incorporate it into an educational program. For example, a video is created that emphasizes the importance of forest protection. In this way, by generating documentary footage, visually appealing educational content can be provided.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The forest conservation system also has a community liaison department. This department can work with local residents and organizations to plan events and workshops to promote forest conservation activities. For example, it could hold a tree-planting event in which local residents can participate to raise awareness of the importance of forest conservation. It could also work with local schools to implement programs to teach children about forest conservation. It could also work with local companies to hold charity events to raise funds for forest conservation activities. This would enable the entire community to support forest conservation activities and achieve sustainable forest management.

[0052] The forest protection system also includes a health diagnostics unit. This unit regularly diagnoses the health of the trees and plants in the forest, allowing for early action if any abnormalities are discovered. For example, it can analyze the color and shape of tree leaves to detect signs of disease or pests. It can also monitor the nutrient status and moisture content of the soil and create plans to supply fertilizer and water as needed. It can also analyze the ecosystem of plants and animals in the forest and support conservation efforts for specific species. This will help maintain the health of the forest and achieve sustainable forest management.

[0053] The forest conservation system also includes a tourism promotion department. This department can plan tourism programs to widely publicize the beauty and importance of forests. For example, it could plan hiking trails and nature observation tours within the forest to convey the charms of forests to tourists. It could also provide ecotourism programs that allow tourists to participate in forest conservation activities, offering opportunities for them to actually participate in tree planting and cleanup activities. It could also hold photo and video exhibitions showcasing the beauty of forests to raise public interest. This would widely publicize the importance of forest conservation through tourism and contribute to revitalizing the local economy.

[0054] The forest protection system also includes a data sharing section. This section can share data related to forest protection with related organizations and researchers, enabling joint conservation activities. For example, by sharing monitoring data and restoration plan data, effective conservation activities can be implemented in cooperation with other researchers and conservation organizations. The data sharing section can also make the data available to the general public, widely communicating the importance of forest protection. Furthermore, the data sharing section can prepare reports based on the results of data analysis and make policy recommendations. In this way, data sharing can lead to more effective forest protection activities and contribute to society at large.

[0055] The forest protection system also includes an energy management unit. The energy management unit can optimize energy consumption within the forest and achieve sustainable energy use. For example, solar and wind power can be used to supply the energy needed for forest protection activities. The energy management unit can also analyze energy consumption data and propose measures to improve energy efficiency. Furthermore, the energy management unit can provide educational programs aimed at reducing energy consumption and raising energy awareness among local residents. This will enable sustainable energy use and reduce the environmental impact of forest protection activities.

[0056] The forest protection system also includes a climate change response department. This department can propose climate change adaptation measures for forests and achieve sustainable forest management. For example, it can identify areas susceptible to the effects of climate change and propose appropriate tree planting plans and protection measures. It can also analyze climate change data and develop measures based on future climate change scenarios. Furthermore, the climate change response department can provide educational programs to local residents about the impacts of climate change and measures to raise awareness. This will enable it to propose climate change adaptation measures and achieve sustainable forest management.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The monitoring unit monitors forest conditions, density, and climate change in real time. For example, AI analyzes image data acquired by unmanned drones and satellites to assess forest health, tree density, and the impact of climate change. Step 2: The alarm unit predicts the risk of fire based on the data acquired by the monitoring unit and issues an alert. For example, it analyzes data such as temperature, humidity, and wind speed, and issues an alert if the risk of fire increases. Step 3: The Regeneration Plan Proposal Department proposes a reforestation plan based on the data collected by the Monitoring Department. For example, it proposes optimal tree planting plans and regeneration methods for areas that have been logged or damaged by fire. Step 4: The illegal activity detection unit detects illegal logging and poaching through analysis of unmanned drone and satellite images. For example, if AI analyzes image data and detects abnormal activity, it will issue an alert and notify the relevant authorities. Step 5: The educational program generation unit generates information and educational programs related to forest conservation. For example, AI could generate content explaining the importance of forests and the need for conservation activities, and provide it as an educational program for schools and communities.

[0059] (Example 2) The forest protection system according to an embodiment of the present invention uses AI to monitor forest conditions, density, and climate change in real time, and provides early warnings, guides forest fire prevention activities, and proposes forest regeneration plans. This allows the forest protection system to protect and manage forests efficiently and effectively.

[0060] A forest protection system according to an embodiment includes a monitoring unit, an alert unit, a restoration plan proposal unit, an illegal activity detection unit, and an educational program generation unit. The monitoring unit monitors forest conditions, density, and climate change in real time. For example, AI analyzes image data acquired from unmanned drones or satellites to assess forest health, tree density, and the impact of climate change. The alert unit predicts fire risk and issues an alert based on the data acquired by the monitoring unit. For example, it analyzes data such as temperature, humidity, and wind speed, and issues an alert if the risk of fire increases. The restoration plan proposal unit proposes a forest restoration plan based on the data acquired by the monitoring unit. For example, it proposes optimal tree planting plans and restoration methods for areas that have been logged or damaged by fire. The illegal activity detection unit detects illegal logging and poaching by analyzing unmanned drone and satellite images. For example, AI analyzes image data and issues an alert and notifies relevant authorities if it detects abnormal activity. The educational program generation unit provides information and generates educational programs related to forest protection. For example, AI can generate content explaining the importance of forests and the need for conservation efforts, and provide it as educational programs in schools and communities. This allows forest protection systems to protect and manage forests efficiently and effectively. For example, real-time monitoring can constantly monitor forest health, and early warning systems can reduce fire risks. Forest protection efforts can also be strengthened through early detection of illegal logging and poaching. Furthermore, information and education programs can raise awareness of forest conservation throughout the local community.

[0061] The monitoring unit can analyze acoustic data from the forest and detect animal activity or abnormal sounds to assess the health of the forest. For example, the monitoring unit uses AI to analyze acoustic data collected from microphones installed in the forest to detect animal activity or abnormal sounds. For example, it can detect the cries of specific animals or abnormal silence to assess the health of the forest. In this way, the health of the forest can be assessed by analyzing the acoustic data.

[0062] The monitoring unit analyzes data from the soil sensor and can monitor the soil's nutritional status and moisture content in real time. For example, the monitoring unit uses AI to analyze data collected from the soil sensor and monitor the soil's nutritional status and moisture content in real time. For example, it analyzes the soil's pH value and moisture content to evaluate the health of the forest. This makes it possible to monitor the soil's nutritional status and moisture content in real time.

[0063] The monitoring unit can use the emotion estimation function to analyze the emotions of people involved in forest conservation activities and provide emotional feedback based on the monitoring results. For example, the monitoring unit can use AI to analyze the emotions of people involved in forest conservation activities and provide emotional feedback based on the monitoring results. For example, it can send encouraging messages depending on the progress of the conservation activities. This provides emotional feedback, thereby maintaining the motivation of people involved in forest conservation activities.

[0064] The monitoring unit analyzes the ecosystem of plants and animals within the forest and can support conservation efforts for specific species. For example, AI can analyze the ecosystem of plants and animals within the forest and can support conservation efforts for specific species. For example, it can identify the habitats of endangered species and carry out conservation efforts. This can support conservation efforts for specific species.

[0065] The monitoring unit can analyze acoustic data from the forest and detect the sounds of illegal logging and poaching. For example, the monitoring unit uses AI to analyze acoustic data collected from microphones installed in the forest and detect the sounds of illegal logging and poaching. For example, it detects the sounds of chainsaws and gunfire and issues an alarm. This makes it possible to detect the sounds of illegal logging and poaching.

[0066] The monitoring unit can use the emotion estimation function to generate content that emotionally appeals to the beauty and importance of forests, thereby increasing public interest. The monitoring unit, for example, uses the emotion estimation function to generate content that emotionally appeals to the beauty and importance of forests. For example, it creates a moving story using footage of beautiful landscapes and flora and fauna. In this way, by generating emotionally appealing content, it is possible to increase public interest in forest conservation.

[0067] The alarm unit can detect minute temperature changes in the forest and issue an alarm in the early stages of a fire. For example, the alarm unit uses AI to analyze data collected from temperature sensors installed in the forest and detect minute temperature changes. For example, it can detect a temperature rise in the early stages of a fire and issue an alarm. This makes it possible to issue an alarm in the early stages of a fire by detecting minute temperature changes.

[0068] The warning unit can analyze data on wind direction and speed and predict the spread of a fire. For example, AI can analyze data on wind direction and speed and predict the spread of a fire. For example, it can predict the direction of a fire's progress based on changes in wind direction and speed and propose appropriate fire prevention measures. In this way, by analyzing data on wind direction and speed, it is possible to predict the spread of a fire.

[0069] The alarm unit can use the emotion estimation function to analyze the anxiety and fear of local residents and propose appropriate fire prevention measures. The alarm unit can, for example, use the emotion estimation function to analyze the anxiety and fear of local residents and propose appropriate fire prevention measures. For example, fire prevention measures can be strengthened based on the emotion data of residents. In this way, by analyzing the anxiety and fear of local residents, appropriate fire prevention measures can be proposed.

[0070] The alarm unit can analyze the evacuation behavior of animals in the forest and predict the occurrence of fires. For example, the alarm unit uses AI to analyze the evacuation behavior of animals in the forest and predict the occurrence of fires. For example, it predicts the risk of fire when animals exhibit abnormal behavior. In this way, it is possible to predict the occurrence of fires by analyzing the evacuation behavior of animals.

[0071] The warning unit can analyze data on humidity and precipitation within the forest and propose measures to reduce the risk of fire. For example, AI can analyze data on humidity and precipitation within the forest and propose measures to reduce the risk of fire. For example, strengthening fire prevention measures in areas with low humidity. By analyzing humidity and precipitation data, it is possible to propose measures to reduce the risk of fire.

[0072] The alarm unit can use the emotion estimation function to generate educational content for raising fire prevention awareness among local residents. The alarm unit, for example, uses the emotion estimation function to generate educational content for raising fire prevention awareness among local residents. For example, a video for raising fire prevention awareness is created based on residents' emotion data. In this way, by generating educational content for raising fire prevention awareness, it is possible to raise fire prevention awareness among local residents.

[0073] The regeneration plan proposal unit can analyze the nutritional state of the soil and propose an optimal tree planting plan. For example, the regeneration plan proposal unit uses AI to analyze data collected from a soil sensor and evaluate the nutritional state of the soil. For example, it analyzes the pH value and nutrient content of the soil and proposes an optimal tree planting plan. In this way, by analyzing the nutritional state of the soil, it is possible to propose an optimal tree planting plan.

[0074] The regeneration plan proposal unit can analyze past forest regeneration data and propose regeneration plans based on success stories. For example, AI can analyze past forest regeneration data and propose regeneration plans based on success stories. For example, a new plan can be created by referring to past successful tree planting plans. This makes it possible to propose regeneration plans based on past success stories.

[0075] The revitalization plan proposal unit can use the emotion estimation function to analyze the emotions of local residents regarding the revitalization plan and propose a plan that is easy to accept. The revitalization plan proposal unit can, for example, use the emotion estimation function to analyze the emotions of local residents regarding the revitalization plan and propose a plan that is easy to accept. For example, the revitalization plan can be adjusted based on the emotion data of residents. In this way, by analyzing the emotions of local residents, it is possible to propose a revitalization plan that is easy to accept.

[0076] The regeneration plan proposal unit can propose a plan that optimizes the materials and labor required for forest regeneration. For example, the regeneration plan proposal unit uses AI to propose a plan that optimizes the materials and labor required for forest regeneration. For example, it creates a plan that minimizes the amount of materials and labor required. In this way, by proposing a plan that optimizes materials and labor, efficient forest regeneration can be achieved.

[0077] The revitalization plan proposal unit can monitor the progress of the revitalization plan in real time and revise the plan as necessary. For example, the revitalization plan proposal unit uses AI to monitor the progress of the revitalization plan in real time and revise the plan as necessary. For example, it monitors the progress of tree planting and adjusts the plan. This makes it possible to monitor the progress of the revitalization plan in real time and revise the plan as necessary.

[0078] The revitalization plan proposal unit can use the emotion estimation function to monitor the emotions of local residents regarding the progress of the revitalization plan and provide feedback to maintain their motivation. The revitalization plan proposal unit can, for example, use the emotion estimation function to monitor the emotions of local residents regarding the progress of the revitalization plan and provide feedback to maintain their motivation. For example, it can send encouraging messages based on the residents' emotion data. This makes it possible to monitor the emotions of local residents and provide feedback to maintain their motivation.

[0079] The illegal activity detection unit can analyze infrared images at night and detect illegal activities. For example, the illegal activity detection unit analyzes infrared images acquired by unmanned drones or satellites at night using AI to detect illegal logging or poaching activities. For example, it can detect abnormal heat sources in infrared images and issue an alarm. This makes it possible to detect illegal activities by analyzing infrared images at night.

[0080] The illegal activity detection unit can analyze acoustic data and detect the sounds of illegal logging and poaching. For example, the illegal activity detection unit uses AI to analyze acoustic data collected from microphones installed in the forest and detect the sounds of illegal logging and poaching. For example, it detects the sounds of chainsaws and gunfire and issues an alarm. In this way, the sound of illegal logging and poaching can be detected by analyzing the acoustic data.

[0081] The illegal activity detection unit can use the emotion estimation function to analyze the emotions of local residents regarding illegal activities and provide information to encourage cooperation. The illegal activity detection unit can, for example, use the emotion estimation function to analyze the emotions of local residents regarding illegal activities and provide information to encourage cooperation. For example, it can send a message calling for cooperation based on residents' emotion data. In this way, by analyzing the emotions of local residents, it can provide information to encourage cooperation.

[0082] The illegal activity detection unit can analyze the behavioral patterns of animals in the forest and predict the risk of poaching. For example, the illegal activity detection unit uses AI to analyze the behavioral patterns of animals in the forest and predict the risk of poaching. For example, it predicts the risk of poaching if an animal exhibits abnormal behavior. In this way, it is possible to predict the risk of poaching by analyzing the behavioral patterns of animals.

[0083] The illegal activity detection unit can learn patterns of illegal activity and propose preventive measures. For example, the illegal activity detection unit uses AI to analyze data on past illegal activities and learn patterns of illegal activity. For example, it can grasp patterns of illegal activity in specific time periods or areas and propose preventive measures. In this way, by learning patterns of illegal activity, it can propose preventive measures.

[0084] The illegal activity detection unit can use the emotion estimation function to monitor the emotions of local residents regarding illegal activities and generate educational content to encourage cooperation. The illegal activity detection unit can, for example, use the emotion estimation function to monitor the emotions of local residents regarding illegal activities and generate educational content to encourage cooperation. For example, a video calling for cooperation is created based on residents' emotion data. In this way, by monitoring the emotions of local residents, educational content to encourage cooperation can be generated.

[0085] The educational program generation unit can analyze the latest research data on forest protection and reflect it in the educational program. For example, the educational program generation unit uses AI to analyze the latest research data on forest protection and reflect it in the educational program. For example, the educational content is updated based on the latest research results. In this way, the latest research data can be analyzed and reflected in the educational program.

[0086] The educational program generation unit can analyze the forest protection issues of each region and propose educational programs specialized for that region. For example, the educational program generation unit uses AI to analyze the forest protection issues of each region and propose educational programs specialized for that region. For example, educational content is created to solve forest protection issues in a specific region. In this way, by analyzing the issues of each region, it is possible to propose educational programs specialized for that region.

[0087] The educational program generation unit can use the emotion estimation function to analyze the emotions of participants in the educational program and propose effective educational methods. The educational program generation unit, for example, uses the emotion estimation function to analyze the emotions of participants in the educational program and propose effective educational methods. For example, the educational content is adjusted based on the participant's emotion data. In this way, by analyzing the participant's emotions, it is possible to propose effective educational methods.

[0088] The educational program generation unit can develop games and simulations related to forest protection and incorporate them into educational programs. For example, the educational program generation unit can use AI to develop games related to forest protection and incorporate them into educational programs. For example, it can create a simulation game for learning about the importance of forest protection. By developing games and simulations, they can be incorporated into educational programs.

[0089] The educational program generation unit generates documentary footage on forest protection and can provide visually appealing educational content. For example, the educational program generation unit uses AI to generate documentary footage on forest protection and incorporate it into an educational program. For example, a video is created that emphasizes the importance of forest protection. In this way, by generating documentary footage, visually appealing educational content can be provided.

[0090] The educational program generation unit can use the emotion estimation function to monitor the emotions of participants in the educational program and use the information to improve the program. The educational program generation unit can, for example, use the emotion estimation function to monitor the emotions of participants in the educational program and use the information to improve the program. For example, the educational content can be adjusted based on participant emotion data. In this way, monitoring the participants' emotions can be used to improve the educational program.

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

[0092] The forest conservation system also has a community liaison department. This department can work with local residents and organizations to plan events and workshops to promote forest conservation activities. For example, it could hold a tree-planting event in which local residents can participate to raise awareness of the importance of forest conservation. It could also work with local schools to implement programs to teach children about forest conservation. It could also work with local companies to hold charity events to raise funds for forest conservation activities. This would enable the entire community to support forest conservation activities and achieve sustainable forest management.

[0093] The forest protection system also includes a health diagnostics unit. This unit regularly diagnoses the health of the trees and plants in the forest, allowing for early action if any abnormalities are discovered. For example, it can analyze the color and shape of tree leaves to detect signs of disease or pests. It can also monitor the nutrient status and moisture content of the soil and create plans to supply fertilizer and water as needed. It can also analyze the ecosystem of plants and animals in the forest and support conservation efforts for specific species. This will help maintain the health of the forest and achieve sustainable forest management.

[0094] The forest conservation system also includes a tourism promotion department. This department can plan tourism programs to widely publicize the beauty and importance of forests. For example, it could plan hiking trails and nature observation tours within the forest to convey the charms of forests to tourists. It could also provide ecotourism programs that allow tourists to participate in forest conservation activities, offering opportunities for them to actually participate in tree planting and cleanup activities. It could also hold photo and video exhibitions showcasing the beauty of forests to raise public interest. This would widely publicize the importance of forest conservation through tourism and contribute to revitalizing the local economy.

[0095] The forest protection system can also use emotion estimation to analyze the emotions of people involved in forest protection activities and provide feedback to maintain their motivation. For example, it can send encouraging messages based on the progress of the protection activities. It can also generate graphs and charts based on emotion data to visually show the results of the protection activities, helping people realize the impact of their efforts. Furthermore, the emotion estimation function can be used to monitor satisfaction and stress levels with the protection activities and provide support as needed. By providing emotional feedback, this system can maintain the motivation of people involved in forest protection activities and support them in continuing their efforts.

[0096] The forest protection system also includes an educational program generation unit. The educational program generation unit can analyze the latest research data on forest protection and reflect it in the educational program. For example, it can update educational content based on the latest research results and provide it as an educational program for schools and communities. It can also analyze forest protection issues in each region and propose educational programs tailored to that region. Furthermore, it is possible to use an emotion estimation function to analyze the emotions of participants in the educational program and propose effective educational methods. This makes it possible to provide educational programs that reflect the latest research data and realize education that addresses issues in each region.

[0097] The forest protection system can also use its emotion estimation function to generate educational content to raise fire prevention awareness among local residents. For example, it can create videos to raise fire prevention awareness based on residents' emotion data. It can also use the emotion estimation function to analyze residents' anxiety and fear and propose appropriate fire prevention measures. It can also use the emotion data to evaluate the effectiveness of fire prevention training and improve the training content. In this way, it can provide educational content to raise fire prevention awareness among local residents using the emotion estimation function.

[0098] The forest protection system can also use its emotion estimation function to analyze the emotions of local residents regarding illegal activities and provide information to encourage cooperation. For example, it can send messages calling for cooperation based on residents' emotion data. It can also use the emotion estimation function to monitor the emotions of local residents regarding illegal activities and generate educational content to encourage cooperation. It can also use the emotion data to assess the risk of illegal activities and propose preventive measures. By analyzing the emotions of local residents, the system can provide information to encourage cooperation and contribute to the prevention of illegal activities.

[0099] The forest protection system also includes a data sharing section. This section can share data related to forest protection with related organizations and researchers, enabling joint conservation activities. For example, by sharing monitoring data and restoration plan data, effective conservation activities can be implemented in cooperation with other researchers and conservation organizations. The data sharing section can also make the data available to the general public, widely communicating the importance of forest protection. Furthermore, the data sharing section can prepare reports based on the results of data analysis and make policy recommendations. In this way, data sharing can lead to more effective forest protection activities and contribute to society at large.

[0100] The forest protection system also includes an energy management unit. The energy management unit can optimize energy consumption within the forest and achieve sustainable energy use. For example, solar and wind power can be used to supply the energy needed for forest protection activities. The energy management unit can also analyze energy consumption data and propose measures to improve energy efficiency. Furthermore, the energy management unit can provide educational programs aimed at reducing energy consumption and raising energy awareness among local residents. This will enable sustainable energy use and reduce the environmental impact of forest protection activities.

[0101] The forest protection system also includes a climate change response department. This department can propose climate change adaptation measures for forests and achieve sustainable forest management. For example, it can identify areas susceptible to the effects of climate change and propose appropriate tree planting plans and protection measures. It can also analyze climate change data and develop measures based on future climate change scenarios. Furthermore, the climate change response department can provide educational programs to local residents about the impacts of climate change and measures to raise awareness. This will enable it to propose climate change adaptation measures and achieve sustainable forest management.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The monitoring unit monitors forest conditions, density, and climate change in real time. For example, AI analyzes image data acquired by unmanned drones and satellites to assess forest health, tree density, and the impact of climate change. Step 2: The alarm unit predicts the risk of fire based on the data acquired by the monitoring unit and issues an alert. For example, it analyzes data such as temperature, humidity, and wind speed, and issues an alert if the risk of fire increases. Step 3: The Regeneration Plan Proposal Department proposes a reforestation plan based on the data collected by the Monitoring Department. For example, it proposes optimal tree planting plans and regeneration methods for areas that have been logged or damaged by fire. Step 4: The illegal activity detection unit detects illegal logging and poaching through analysis of unmanned drone and satellite images. For example, if AI analyzes image data and detects abnormal activity, it will issue an alert and notify the relevant authorities. Step 5: The educational program generation unit generates information and educational programs related to forest conservation. For example, AI could generate content explaining the importance of forests and the need for conservation activities, and provide it as an educational program for schools and communities.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. The monitoring department monitors forest conditions, density, and climate change in real time. an alarm unit that predicts a fire risk based on the data acquired by the monitoring unit and issues an alarm; a regeneration plan proposal unit that proposes a forest regeneration plan based on the data acquired by the monitoring unit; The Illegal Activity Detection Department detects illegal logging and poaching through the analysis of unmanned drones and satellite images. and an educational program generation unit that generates information and educational programs related to forest protection. A system characterized by:

2. The monitoring unit Using emotion estimation function, the emotions of the people involved in forest conservation activities are analyzed and emotional feedback is provided based on the monitoring results.

2. The system of claim 1.

3. The alarm unit Detecting minute temperature changes within the forest and issuing an alarm at the early stage of a fire 2. The system of claim 1.

4. The rehabilitation plan proposal unit Analyzing the nutritional status of the soil and proposing optimal planting plans 2. The system of claim 1.

5. The illegal activity detection unit: Analyzing nighttime thermal images to detect illegal activity 2. The system of claim 1.

6. The educational program generation unit Using an emotion estimation function, the emotions of the participants of the educational program are analyzed, and an effective educational method is proposed.

2. The system of claim 1.

7. The alarm unit Using emotion estimation functions, the system analyzes the anxiety and fear of local residents and proposes appropriate fire prevention measures.

2. The system of claim 1.

8. The rehabilitation plan proposal unit Using emotion estimation function, analyze the feelings of local residents regarding the regeneration plan and propose the plan that is easy to accept.

2. The system of claim 1.

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