system

Abstract

The system according to this embodiment aims to effectively collect and analyze geospatial data and utilize it to solve social problems. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an inference unit, and a provision unit. The collection unit collects geospatial data. The analysis unit analyzes the geospatial data collected by the collection unit. The inference unit performs inferences based on the data analyzed by the analysis unit. The provision unit provides the results obtained by the inference unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it cannot be said that geospatial data has been sufficiently collected and analyzed effectively and utilized to solve social problems, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively collect and analyze geospatial data and utilize it to solve social problems.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an inference unit, and a data provision unit. The data collection unit collects geospatial data. The analysis unit analyzes the geospatial data collected by the data collection unit. The inference unit performs inferences based on the data analyzed by the analysis unit. The data provision unit provides the results obtained by the inference unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively collect and analyze geospatial data and utilize it to solve social problems. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The geospatial data analysis system according to an embodiment of the present invention is a system in which an LLM (Large-Scale Language Model) learns and infers based on geospatial data to which absolute coordinates have been assigned, utilizing RTK, SAR satellites, optical satellites, and drones. This geospatial data analysis system aims to support the resolution of social issues such as disaster response, urban planning, and environmental conservation. Specifically, it consists of the following steps. First, geospatial data is collected using RTK, SAR satellites, optical satellites, and drones. RTK provides highly accurate positional information, and SAR satellites provide data for detecting changes in terrain. Optical satellites provide high-resolution images, and drones provide detailed ground photographs. These data are assigned absolute coordinates and integrated as geospatial data. Next, the LLM learns from this geospatial data and performs inference. For example, in disaster response, changes in terrain are detected using SAR satellite images, and damage is visualized using optical satellite images and drone photographs. This makes it possible to optimize evacuation plans. In urban planning, it provides data to streamline infrastructure placement and transportation network design. In environmental conservation, it provides data to monitor illegal logging and ecosystem changes and strengthen conservation activities. Furthermore, LLM enables rapid and highly accurate analysis and decision support. This can contribute to solving challenges faced by public institutions and private companies. For example, in disaster response, SAR satellite imagery can be used to detect topographic changes, and optical satellite imagery and drone photography can be used to visualize damage and optimize evacuation plans. In urban planning, infrastructure placement and transportation network design can be made more efficient. In environmental conservation, illegal logging and ecosystem changes can be monitored to strengthen conservation activities. Because this system can handle everything from geospatial data collection to analysis, inference, and decision support in an integrated manner, it is expected to make a significant contribution to solving social issues. For example, in disaster response, it enables the rapid formulation of evacuation plans and assessment of damage, and in urban planning, it enables efficient infrastructure placement and transportation network design. In environmental conservation, monitoring of illegal logging and ecosystem changes will be strengthened, and conservation activities will be carried out more effectively. As a result, the geospatial data analysis system can achieve rapid and highly accurate analysis and decision support.

[0029] The geospatial data analysis system according to this embodiment comprises a collection unit, an analysis unit, an inference unit, and a provision unit. The collection unit collects geospatial data. The collection unit collects geospatial data using, for example, RTK, SAR satellites, optical satellites, and drones. RTK provides highly accurate positional information. SAR satellites provide data for detecting topographic changes. Optical satellites provide high-resolution images. Drones provide detailed ground photographs. The analysis unit analyzes the geospatial data collected by the collection unit. The analysis unit analyzes the collected geospatial data to detect topographic changes and damage. The analysis unit detects topographic changes using SAR satellite images and visualizes damage using optical satellite images and drone photographs. The inference unit performs inferences based on the data analyzed by the analysis unit. The inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the analyzed data. The inference unit provides data to streamline infrastructure placement and transportation network design. The inference unit provides data to monitor illegal logging and ecosystem changes, and to strengthen conservation activities. The provision unit provides the results obtained by the inference unit. For example, the provision unit provides the results obtained by the inference unit to public institutions and private companies. This allows the geospatial data analysis system to perform the entire process from geospatial data collection to analysis, inference, and provision in an integrated manner.

[0030] The data collection unit collects geospatial data. This unit uses, for example, RTK, SAR satellites, optical satellites, and drones to collect geospatial data. RTK (Real-time Kinematic) is a technology that provides highly accurate positional information between a ground-based reference station and a mobile station, enabling location information to be acquired with centimeter-level accuracy. This allows for accurate understanding of detailed terrain changes and infrastructure layouts. SAR (Synthetic Aperture Radar) satellites acquire surface data using radio waves, enabling the detection of terrain changes regardless of weather or time of day. SAR satellites can quickly detect terrain changes caused by natural disasters such as earthquakes and landslides, helping to assess the extent of damage. Optical satellites provide high-resolution images, allowing for the acquisition of detailed information about the Earth's surface. Optical satellite images are used in various fields, including urban planning, agriculture, and environmental monitoring. Drones can take detailed ground photographs from low altitudes, making them suitable for collecting detailed data on specific areas or objects. Drones can efficiently cover hard-to-reach locations and wide areas. This allows the data collection unit to utilize a variety of devices and technologies to collect extensive and detailed geospatial data and transmit it to a central database in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and inference units. In addition, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the geospatial data collected by the collection unit. For example, the analysis unit analyzes the collected geospatial data to detect topographic changes and damage. Specifically, it uses SAR satellite imagery to detect topographic changes and optical satellite imagery and drone photographs to visualize the damage. SAR satellite imagery can detect minute changes in the Earth's surface and is useful for quickly understanding topographic changes caused by natural disasters such as earthquakes and landslides. Optical satellite imagery provides high-resolution visual information, allowing for visual confirmation of damage details such as building damage and road closures. Drone photographs provide detailed images taken from low altitudes and are suitable for understanding the damage to specific areas or objects in detail. The analysis unit integrates this data and performs real-time analysis using AI. The AI ​​uses image recognition technology to identify topographic changes and damaged areas and anomaly detection algorithms to detect unusual patterns and abnormal data. This allows the analysis unit to quickly and accurately analyze the collected data and understand the surrounding risk situation in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical topographic change data, it can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The inference unit performs inferences based on data analyzed by the analysis unit. For example, the inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the analyzed data. Specifically, it plans the optimization of evacuation routes and the placement of evacuation shelters based on analyzed topographic change data. For example, in the event of a disaster such as an earthquake or flood, it identifies areas where damage is expected and areas requiring evacuation, and calculates the optimal evacuation route. It also provides data to streamline infrastructure placement and transportation network design. For example, in the construction plan of new roads and bridges, it proposes the optimal placement based on topographic and traffic volume data. Furthermore, the inference unit provides data to monitor illegal logging and ecosystem changes and strengthen conservation activities. For example, it monitors deforestation and ecosystem changes and provides information to determine the priority of conservation activities. The inference unit uses AI to analyze this data and simulate multiple scenarios to identify the most effective countermeasures. This allows the inference unit to perform highly accurate inferences based on the analyzed data and provide information for taking appropriate measures. Furthermore, the inference unit can continuously correct its inference results based on real-time updated data to respond to the latest situations. For example, the system instantly updates its inference results in response to the progression of a disaster and changes in the environment, proposing the most appropriate countermeasures. Furthermore, the inference unit can perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the inference unit to always provide highly accurate inferences based on the latest information, supporting a rapid and appropriate response.

[0033] The information provider unit provides the results obtained by the inference unit. For example, the information provider unit provides the results obtained by the inference unit to public institutions and private companies. Specifically, it provides information on optimizing evacuation plans and infrastructure placement to local governments and disaster prevention organizations to support rapid response. For example, in the event of a disaster, it provides information on evacuation routes and shelters in real time to ensure the safety of residents. It also provides information on infrastructure placement and transportation network design to construction companies and urban planning departments to support efficient infrastructure development. Furthermore, it provides information on illegal logging and ecosystem changes to environmental protection organizations and research institutions to support the strengthening of conservation activities. The information provider unit can provide this information in various formats. For example, it can make it easily accessible to users through web portals and mobile apps. It can also provide APIs to link with other systems and applications, facilitating data sharing and integration. Furthermore, the information provider unit can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can revise evacuation routes and improve information provision methods based on feedback on evacuation plans. In addition, the information provider unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using a combination of methods such as web notifications, email, SMS, and voice calls. This allows the service provider to deliver information to users quickly and reliably, maximizing the effectiveness of the geospatial data analysis system.

[0034] The data collection unit can collect geospatial data using RTK, SAR satellites, optical satellites, and drones. For example, the data collection unit can provide high-precision location information using RTK. The data collection unit can provide data for detecting terrain changes using SAR satellites. The data collection unit can provide high-resolution images using optical satellites. The data collection unit can provide detailed ground photographs using drones. This allows the data collection unit to collect high-precision geospatial data using a variety of devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geospatial data collected using RTK, SAR satellites, optical satellites, and drones into a generating AI, and the generating AI can integrate the data.

[0035] The analysis unit can analyze collected geospatial data to detect topographic changes and damage. For example, the analysis unit analyzes collected geospatial data to detect topographic changes. The analysis unit analyzes SAR satellite images to detect damage. The analysis unit analyzes optical satellite images and drone photographs to visualize the damage. This allows the analysis unit to quickly detect topographic changes and damage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected geospatial data into a generating AI, and the generating AI can detect topographic changes and damage.

[0036] The inference unit can optimize evacuation plans, infrastructure layouts, and conservation activities based on the analyzed data. For example, the inference unit optimizes evacuation plans based on the analyzed data. The inference unit provides data for optimizing infrastructure layouts. The inference unit provides data for optimizing conservation activities. This enables the inference unit to optimize evacuation plans, infrastructure layouts, and conservation activities. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the analyzed data into a generating AI, and the generating AI can then optimize evacuation plans, infrastructure layouts, and conservation activities.

[0037] The providing unit can provide the results obtained by the inference unit to public institutions and private companies. For example, the providing unit provides the results obtained by the inference unit to public institutions. The providing unit provides the results obtained by the inference unit to private companies. In this way, the providing unit can provide useful information to public institutions and private companies. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the results obtained by the inference unit into a generating AI, and the generating AI can generate information to be provided to public institutions and private companies.

[0038] The analysis unit can detect terrain changes using SAR satellite imagery and visualize damage using optical satellite imagery and drone photographs. For example, the analysis unit analyzes SAR satellite imagery to detect terrain changes. The analysis unit analyzes optical satellite imagery to visualize damage. The analysis unit analyzes drone photographs to visualize damage. This allows the analysis unit to grasp terrain changes and damage in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input SAR satellite imagery, optical satellite imagery, and drone photographs into a generating AI, and the generating AI can visualize terrain changes and damage.

[0039] The inference unit can provide data to streamline infrastructure placement and transportation network design. For example, the inference unit provides data to streamline infrastructure placement. The inference unit provides data to streamline transportation network design. This enables the inference unit to streamline infrastructure placement and transportation network design. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input infrastructure placement and transportation network design data into a generating AI, and the generating AI can provide data for streamlining.

[0040] The inference unit can monitor illegal logging and ecosystem changes and provide data to strengthen conservation activities. For example, the inference unit provides data to monitor illegal logging. The inference unit provides data to monitor ecosystem changes. The inference unit provides data to strengthen conservation activities. This enables the inference unit to monitor illegal logging and ecosystem changes and to strengthen conservation activities. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input data on illegal logging and ecosystem changes into a generating AI, and the generating AI can provide data to strengthen conservation activities.

[0041] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past collected data. The data collection unit analyzes past collected data and identifies areas for improvement in the data collection method. The data collection unit optimizes the data collection method based on past collected data. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI, which can then select the optimal data collection method.

[0042] The data collection unit can filter geospatial data based on specific environmental conditions or seasons. For example, during rainy weather, the data collection unit filters the collected data to remove unnecessary data. During winter, the data collection unit filters the collected data to remove the effects of snow. During summer, the data collection unit filters the collected data to remove the effects of sunlight. In this way, the data collection unit can remove unnecessary data by filtering the data based on specific environmental conditions or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data based on environmental conditions and seasons into a generating AI, and the generating AI can perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering geographic location information when collecting geospatial data. For example, in urban areas, the data collection unit prioritizes the collection of data on buildings and roads. In rural areas, the data collection unit prioritizes the collection of data on farmland and forests. In disaster areas, the data collection unit prioritizes the collection of data on the extent of damage. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographic location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographic location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0044] The data collection unit can analyze social media activity and collect relevant data when collecting geospatial data. For example, the data collection unit prioritizes collecting data on locations that are trending on social media. The data collection unit analyzes the content of social media posts and collects relevant geospatial data. The data collection unit determines the scope of data to collect based on the location information of social media. This allows the data collection unit to efficiently collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI, and the generating AI can collect relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For less important data, the analysis unit performs a simplified analysis. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a terrain analysis algorithm to terrain data, a damage analysis algorithm to damage data, and an environmental analysis algorithm to environmental data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. It postpones the analysis of older data. The analysis unit dynamically adjusts the analysis priority according to the data collection timing. This enables efficient analysis by allowing the analysis unit to determine the analysis priority based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit dynamically adjusts the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0049] The inference unit can improve the accuracy of its inferences by considering the interrelationships between data during inference. For example, the inference unit analyzes the interrelationships between data to improve the accuracy of its inferences. The inference unit optimizes the inference algorithm by considering the interrelationships between data. The inference unit adjusts the inference results based on the interrelationships between data. As a result, the inference unit improves the accuracy of its inferences by considering the interrelationships between data. Some or all of the above processes in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the interrelationships between data into a generative AI, and the generative AI can improve the accuracy of the inferences.

[0050] The inference unit can perform inference while considering the attribute information of the data provider. The inference unit adjusts the inference results, for example, by considering the reliability of the data provider. The inference unit optimizes the inference algorithm, taking into account the expertise of the data provider. The inference unit adjusts the inference results based on the attribute information of the data provider. As a result, the accuracy of the inference unit improves by considering the attribute information of the data provider. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the attribute information of the data provider into a generating AI and perform inference using the generating AI.

[0051] The inference unit can perform inference while considering the geographical distribution of the data. For example, the inference unit can analyze the geographical distribution of the data to improve the accuracy of the inference. The inference unit can optimize the inference algorithm by considering the geographical distribution of the data. The inference unit can adjust the inference results based on the geographical distribution of the data. As a result, the inference unit improves the accuracy of its inference by considering the geographical distribution of the data. Some or all of the above processes in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the geographical distribution of the data into a generative AI and perform inference using the generative AI.

[0052] The inference unit can improve the accuracy of its inferences by referring to relevant literature during the inference process. For example, the inference unit can refer to relevant literature to improve the accuracy of its inferences. The inference unit can optimize its inference algorithm based on the relevant literature. The inference unit can adjust the inference results based on the relevant literature. As a result, the inference unit improves the accuracy of its inferences by referring to relevant literature. Some or all of the above processes in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input relevant literature into a generating AI, and the generating AI can improve the accuracy of its inferences.

[0053] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider provides a detailed explanation for important information. For less important information, the provider provides a simplified explanation. The provider dynamically adjusts the level of detail provided according to the importance of the information. This enables efficient information provision by adjusting the level of detail provided according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into a generating AI, and the generating AI can adjust the level of detail provided.

[0054] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit applies a rapid and concise delivery algorithm to disaster information. For urban planning information, it applies a detailed and visually easy-to-understand delivery algorithm. For environmental conservation information, it applies a delivery algorithm that involves continuous monitoring and reporting. In this way, the delivery unit improves the accuracy of information delivery by applying the appropriate delivery algorithm according to the category of information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI, and the generating AI can apply an appropriate delivery algorithm.

[0055] The information provider can determine the priority of information provision based on when the information was collected. For example, the provider may prioritize providing the most recent information. Older information may be provided later. The provider dynamically adjusts the priority of information provision according to when the information was collected. This enables efficient information provision by allowing the provider to determine the priority of information provision based on when the information was collected. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the information collection dates into a generating AI, and the generating AI can determine the priority of information provision.

[0056] The information provider can adjust the order of information delivery based on the relevance of the information at the time of delivery. For example, the information provider can prioritize the delivery of highly relevant information. The information provider can postpone the delivery of less relevant information. The information provider can dynamically adjust the order of delivery according to the relevance of the information. This enables efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into a generating AI, and the generating AI can adjust the order of delivery.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] A geospatial data analysis system can also include a prediction unit. Based on collected geospatial data and analysis results, the prediction unit can forecast future topographic changes and the likelihood of disasters. For example, the prediction unit can combine historical earthquake data with current topographic data to predict future earthquake risk. It can also combine meteorological data with topographic data to predict flood and landslide risk. Furthermore, it can combine urban planning data with traffic data to predict future traffic congestion locations. This allows the prediction unit to provide data for identifying future risks in advance and taking appropriate countermeasures.

[0059] The analysis unit can determine the priority of analysis based on the reliability of the data during the analysis process. For example, it can prioritize the analysis of highly reliable data and postpone the analysis of less reliable data. The analysis unit dynamically adjusts the analysis priority according to the reliability of the data. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the reliability of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the reliability of the data into a generating AI, and the generating AI can determine the priority of analysis.

[0060] The information provider can adjust its method of delivery by considering the attribute information of the information recipient. For example, if the information recipient is a specialist, detailed technical information may be provided. If the information recipient is a member of the general public, concise and easy-to-understand information may be provided. If the information recipient is an emergency responder, information that enables them to act quickly may be provided. In this way, the information provider can provide appropriate information by considering the attribute information of the information recipient. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the attribute information of the information recipient into a generating AI and adjust the method of delivery using the generating AI.

[0061] The data collection unit can prioritize the collection of geospatial data based on specific events or situations. For example, if a large-scale event is held, data in the surrounding area will be prioritized for collection. If a disaster occurs, data in the affected area will be prioritized for collection. To monitor seasonal changes, data related to a specific season will be prioritized for collection. This allows the data collection unit to efficiently collect data by prioritizing collection based on specific events or situations. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from a specific event or situation into a generating AI, which can then determine the collection priority.

[0062] The inference unit can perform inference while considering the temporal changes in the data. For example, it can compare past data with current data to predict future changes. The inference unit analyzes the temporal changes in the data to improve the accuracy of the inference. The inference unit optimizes the inference algorithm based on the temporal changes in the data. As a result, the inference unit improves the accuracy of its inference by considering the temporal changes in the data. Some or all of the above processes in the inference unit may be performed using AI or not. For example, the inference unit can input the temporal changes in the data into a generative AI and perform inference using the generative AI.

[0063] The information provider can adjust its delivery method by considering the recipient's feedback at the time of delivery. For example, it can adjust the level of detail of the information provided based on the recipient's feedback. It can change the format of the information provided based on the recipient's feedback. It can adjust the timing of the information provided based on the recipient's feedback. In this way, the information provider can provide appropriate information by considering the recipient's feedback. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the recipient's feedback into a generating AI, and the generating AI can adjust the delivery method.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects geospatial data. The collection unit collects geospatial data using, for example, RTK, SAR satellites, optical satellites, and drones. RTK provides high-precision location information, SAR satellites provide data for detecting terrain changes, optical satellites provide high-resolution images, and drones provide detailed ground photographs. Step 2: The analysis unit analyzes the geospatial data collected by the collection unit. For example, the analysis unit analyzes the collected geospatial data to detect changes in terrain and damage. The analysis unit detects changes in terrain using SAR satellite imagery and visualizes the damage using optical satellite imagery and drone photographs. Step 3: The inference unit performs inferences based on the data analyzed by the analysis unit. For example, the inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the analyzed data. The inference unit provides data to streamline infrastructure placement and transportation network design, and provides data to monitor illegal logging and ecosystem changes and strengthen conservation activities. Step 4: The provider unit provides the results obtained by the inference unit. For example, the provider unit provides the results obtained by the inference unit to public institutions or private companies.

[0066] (Example of form 2) The geospatial data analysis system according to an embodiment of the present invention is a system in which an LLM (Large-Scale Language Model) learns and infers based on geospatial data to which absolute coordinates have been assigned, utilizing RTK, SAR satellites, optical satellites, and drones. This geospatial data analysis system aims to support the resolution of social issues such as disaster response, urban planning, and environmental conservation. Specifically, it consists of the following steps. First, geospatial data is collected using RTK, SAR satellites, optical satellites, and drones. RTK provides highly accurate positional information, and SAR satellites provide data for detecting changes in terrain. Optical satellites provide high-resolution images, and drones provide detailed ground photographs. These data are assigned absolute coordinates and integrated as geospatial data. Next, the LLM learns from this geospatial data and performs inference. For example, in disaster response, changes in terrain are detected using SAR satellite images, and damage is visualized using optical satellite images and drone photographs. This makes it possible to optimize evacuation plans. In urban planning, it provides data to streamline infrastructure placement and transportation network design. In environmental conservation, it provides data to monitor illegal logging and ecosystem changes and strengthen conservation activities. Furthermore, LLM enables rapid and highly accurate analysis and decision support. This can contribute to solving challenges faced by public institutions and private companies. For example, in disaster response, SAR satellite imagery can be used to detect topographic changes, and optical satellite imagery and drone photography can be used to visualize damage and optimize evacuation plans. In urban planning, infrastructure placement and transportation network design can be made more efficient. In environmental conservation, illegal logging and ecosystem changes can be monitored to strengthen conservation activities. Because this system can handle everything from geospatial data collection to analysis, inference, and decision support in an integrated manner, it is expected to make a significant contribution to solving social issues. For example, in disaster response, it enables the rapid formulation of evacuation plans and assessment of damage, and in urban planning, it enables efficient infrastructure placement and transportation network design. In environmental conservation, monitoring of illegal logging and ecosystem changes will be strengthened, and conservation activities will be carried out more effectively. As a result, the geospatial data analysis system can achieve rapid and highly accurate analysis and decision support.

[0067] The geospatial data analysis system according to this embodiment comprises a collection unit, an analysis unit, an inference unit, and a provision unit. The collection unit collects geospatial data. The collection unit collects geospatial data using, for example, RTK, SAR satellites, optical satellites, and drones. RTK provides highly accurate positional information. SAR satellites provide data for detecting topographic changes. Optical satellites provide high-resolution images. Drones provide detailed ground photographs. The analysis unit analyzes the geospatial data collected by the collection unit. The analysis unit analyzes the collected geospatial data to detect topographic changes and damage. The analysis unit detects topographic changes using SAR satellite images and visualizes damage using optical satellite images and drone photographs. The inference unit performs inferences based on the data analyzed by the analysis unit. The inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the analyzed data. The inference unit provides data to streamline infrastructure placement and transportation network design. The inference unit provides data to monitor illegal logging and ecosystem changes, and to strengthen conservation activities. The provision unit provides the results obtained by the inference unit. For example, the provision unit provides the results obtained by the inference unit to public institutions and private companies. This allows the geospatial data analysis system to perform the entire process from geospatial data collection to analysis, inference, and provision in an integrated manner.

[0068] The data collection unit collects geospatial data. This unit uses, for example, RTK, SAR satellites, optical satellites, and drones to collect geospatial data. RTK (Real-time Kinematic) is a technology that provides highly accurate positional information between a ground-based reference station and a mobile station, enabling location information to be acquired with centimeter-level accuracy. This allows for accurate understanding of detailed terrain changes and infrastructure layouts. SAR (Synthetic Aperture Radar) satellites acquire surface data using radio waves, enabling the detection of terrain changes regardless of weather or time of day. SAR satellites can quickly detect terrain changes caused by natural disasters such as earthquakes and landslides, helping to assess the extent of damage. Optical satellites provide high-resolution images, allowing for the acquisition of detailed information about the Earth's surface. Optical satellite images are used in various fields, including urban planning, agriculture, and environmental monitoring. Drones can take detailed ground photographs from low altitudes, making them suitable for collecting detailed data on specific areas or objects. Drones can efficiently cover hard-to-reach locations and wide areas. This allows the data collection unit to utilize a variety of devices and technologies to collect extensive and detailed geospatial data and transmit it to a central database in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and inference units. In addition, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0069] The analysis unit analyzes the geospatial data collected by the collection unit. For example, the analysis unit analyzes the collected geospatial data to detect topographic changes and damage. Specifically, it uses SAR satellite imagery to detect topographic changes and optical satellite imagery and drone photographs to visualize the damage. SAR satellite imagery can detect minute changes in the Earth's surface and is useful for quickly understanding topographic changes caused by natural disasters such as earthquakes and landslides. Optical satellite imagery provides high-resolution visual information, allowing for visual confirmation of damage details such as building damage and road closures. Drone photographs provide detailed images taken from low altitudes and are suitable for understanding the damage to specific areas or objects in detail. The analysis unit integrates this data and performs real-time analysis using AI. The AI ​​uses image recognition technology to identify topographic changes and damaged areas and anomaly detection algorithms to detect unusual patterns and abnormal data. This allows the analysis unit to quickly and accurately analyze the collected data and understand the surrounding risk situation in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical topographic change data, it can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0070] The inference unit performs inferences based on data analyzed by the analysis unit. For example, the inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the analyzed data. Specifically, it plans the optimization of evacuation routes and the placement of evacuation shelters based on analyzed topographic change data. For example, in the event of a disaster such as an earthquake or flood, it identifies areas where damage is expected and areas requiring evacuation, and calculates the optimal evacuation route. It also provides data to streamline infrastructure placement and transportation network design. For example, in the construction plan of new roads and bridges, it proposes the optimal placement based on topographic and traffic volume data. Furthermore, the inference unit provides data to monitor illegal logging and ecosystem changes and strengthen conservation activities. For example, it monitors deforestation and ecosystem changes and provides information to determine the priority of conservation activities. The inference unit uses AI to analyze this data and simulate multiple scenarios to identify the most effective countermeasures. This allows the inference unit to perform highly accurate inferences based on the analyzed data and provide information for taking appropriate measures. Furthermore, the inference unit can continuously correct its inference results based on real-time updated data to respond to the latest situations. For example, the system instantly updates its inference results in response to the progression of a disaster and changes in the environment, proposing the most appropriate countermeasures. Furthermore, the inference unit can perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the inference unit to always provide highly accurate inferences based on the latest information, supporting a rapid and appropriate response.

[0071] The information provider unit provides the results obtained by the inference unit. For example, the information provider unit provides the results obtained by the inference unit to public institutions and private companies. Specifically, it provides information on optimizing evacuation plans and infrastructure placement to local governments and disaster prevention organizations to support rapid response. For example, in the event of a disaster, it provides information on evacuation routes and shelters in real time to ensure the safety of residents. It also provides information on infrastructure placement and transportation network design to construction companies and urban planning departments to support efficient infrastructure development. Furthermore, it provides information on illegal logging and ecosystem changes to environmental protection organizations and research institutions to support the strengthening of conservation activities. The information provider unit can provide this information in various formats. For example, it can make it easily accessible to users through web portals and mobile apps. It can also provide APIs to link with other systems and applications, facilitating data sharing and integration. Furthermore, the information provider unit can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can revise evacuation routes and improve information provision methods based on feedback on evacuation plans. In addition, the information provider unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using a combination of methods such as web notifications, email, SMS, and voice calls. This allows the service provider to deliver information to users quickly and reliably, maximizing the effectiveness of the geospatial data analysis system.

[0072] The data collection unit can collect geospatial data using RTK, SAR satellites, optical satellites, and drones. For example, the data collection unit can provide high-precision location information using RTK. The data collection unit can provide data for detecting terrain changes using SAR satellites. The data collection unit can provide high-resolution images using optical satellites. The data collection unit can provide detailed ground photographs using drones. This allows the data collection unit to collect high-precision geospatial data using a variety of devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geospatial data collected using RTK, SAR satellites, optical satellites, and drones into a generating AI, and the generating AI can integrate the data.

[0073] The analysis unit can analyze collected geospatial data to detect topographic changes and damage. For example, the analysis unit analyzes collected geospatial data to detect topographic changes. The analysis unit analyzes SAR satellite images to detect damage. The analysis unit analyzes optical satellite images and drone photographs to visualize the damage. This allows the analysis unit to quickly detect topographic changes and damage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected geospatial data into a generating AI, and the generating AI can detect topographic changes and damage.

[0074] The inference unit can optimize evacuation plans, infrastructure layouts, and conservation activities based on the analyzed data. For example, the inference unit optimizes evacuation plans based on the analyzed data. The inference unit provides data for optimizing infrastructure layouts. The inference unit provides data for optimizing conservation activities. This enables the inference unit to optimize evacuation plans, infrastructure layouts, and conservation activities. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the analyzed data into a generating AI, and the generating AI can then optimize evacuation plans, infrastructure layouts, and conservation activities.

[0075] The providing unit can provide the results obtained by the inference unit to public institutions and private companies. For example, the providing unit provides the results obtained by the inference unit to public institutions. The providing unit provides the results obtained by the inference unit to private companies. In this way, the providing unit can provide useful information to public institutions and private companies. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the results obtained by the inference unit into a generating AI, and the generating AI can generate information to be provided to public institutions and private companies.

[0076] The analysis unit can detect terrain changes using SAR satellite imagery and visualize damage using optical satellite imagery and drone photographs. For example, the analysis unit analyzes SAR satellite imagery to detect terrain changes. The analysis unit analyzes optical satellite imagery to visualize damage. The analysis unit analyzes drone photographs to visualize damage. This allows the analysis unit to grasp terrain changes and damage in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input SAR satellite imagery, optical satellite imagery, and drone photographs into a generating AI, and the generating AI can visualize terrain changes and damage.

[0077] The inference unit can provide data to streamline infrastructure placement and transportation network design. For example, the inference unit provides data to streamline infrastructure placement. The inference unit provides data to streamline transportation network design. This enables the inference unit to streamline infrastructure placement and transportation network design. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input infrastructure placement and transportation network design data into a generating AI, and the generating AI can provide data for streamlining.

[0078] The inference unit can monitor illegal logging and ecosystem changes and provide data to strengthen conservation activities. For example, the inference unit provides data to monitor illegal logging. The inference unit provides data to monitor ecosystem changes. The inference unit provides data to strengthen conservation activities. This enables the inference unit to monitor illegal logging and ecosystem changes and to strengthen conservation activities. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input data on illegal logging and ecosystem changes into a generating AI, and the generating AI can provide data to strengthen conservation activities.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of geospatial data collection based on the estimated emotions. For example, if the user is tense, the data collection unit can delay the collection timing and wait until the user is relaxed. If the user is relaxed, the data collection unit can advance the collection timing to collect data efficiently. If the user is in a hurry, the data collection unit can optimize the collection timing to collect data quickly. In this way, the data collection unit can efficiently collect data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can adjust the collection timing.

[0080] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past collected data. The data collection unit analyzes past collected data and identifies areas for improvement in the data collection method. The data collection unit optimizes the data collection method based on past collected data. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI, which can then select the optimal data collection method.

[0081] The data collection unit can filter geospatial data based on specific environmental conditions or seasons. For example, during rainy weather, the data collection unit filters the collected data to remove unnecessary data. During winter, the data collection unit filters the collected data to remove the effects of snow. During summer, the data collection unit filters the collected data to remove the effects of sunlight. In this way, the data collection unit can remove unnecessary data by filtering the data based on specific environmental conditions or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data based on environmental conditions and seasons into a generating AI, and the generating AI can perform the filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows the data collection unit to efficiently collect data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of data to collect.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering geographic location information when collecting geospatial data. For example, in urban areas, the data collection unit prioritizes the collection of data on buildings and roads. In rural areas, the data collection unit prioritizes the collection of data on farmland and forests. In disaster areas, the data collection unit prioritizes the collection of data on the extent of damage. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographic location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographic location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0084] The data collection unit can analyze social media activity and collect relevant data when collecting geospatial data. For example, the data collection unit prioritizes collecting data on locations that are trending on social media. The data collection unit analyzes the content of social media posts and collects relevant geospatial data. The data collection unit determines the scope of data to collect based on the location information of social media. This allows the data collection unit to efficiently collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI, and the generating AI can collect relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a concise analysis result. In this way, the analysis unit can provide easy-to-understand analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For less important data, the analysis unit performs a simplified analysis. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a terrain analysis algorithm to terrain data, a damage analysis algorithm to damage data, and an environmental analysis algorithm to environmental data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a quick analysis result. This allows the analysis unit to perform efficient analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. It postpones the analysis of older data. The analysis unit dynamically adjusts the analysis priority according to the data collection timing. This enables efficient analysis by allowing the analysis unit to determine the analysis priority based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, and the generating AI can determine the analysis priority.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit dynamically adjusts the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0091] The inference unit can estimate the user's emotions and adjust the inference criteria based on the estimated emotions. For example, if the user is tense, the inference unit provides a simple and easy-to-understand inference result. If the user is relaxed, the inference unit provides a detailed inference result. If the user is in a hurry, the inference unit provides a concise inference result. In this way, the inference unit can provide easy-to-understand inference results by adjusting the inference criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inference unit may be performed using AI, for example, or not using AI. For example, the inference unit can input user emotion data into a generative AI, and the generative AI can adjust the inference criteria.

[0092] The inference unit can improve the accuracy of its inferences by considering the interrelationships between data during inference. For example, the inference unit analyzes the interrelationships between data to improve the accuracy of its inferences. The inference unit optimizes the inference algorithm by considering the interrelationships between data. The inference unit adjusts the inference results based on the interrelationships between data. As a result, the inference unit improves the accuracy of its inferences by considering the interrelationships between data. Some or all of the above processes in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the interrelationships between data into a generative AI, and the generative AI can improve the accuracy of the inferences.

[0093] The inference unit can perform inference while considering the attribute information of the data provider. The inference unit adjusts the inference results, for example, by considering the reliability of the data provider. The inference unit optimizes the inference algorithm, taking into account the expertise of the data provider. The inference unit adjusts the inference results based on the attribute information of the data provider. As a result, the accuracy of the inference unit improves by considering the attribute information of the data provider. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the attribute information of the data provider into a generating AI and perform inference using the generating AI.

[0094] The inference unit can estimate the user's emotions and adjust the order in which the inference results are displayed based on the estimated emotions. For example, if the user is tense, the inference unit will prioritize displaying important results. If the user is relaxed, the inference unit will prioritize displaying detailed results. If the user is in a hurry, the inference unit will prioritize displaying results that can be displayed quickly. In this way, the inference unit can provide highly visible inference results by adjusting the display order of the inference results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inference unit may be performed using AI, for example, or not using AI. For example, the inference unit can input user emotion data into a generative AI, and the generative AI can adjust the order in which the inference results are displayed.

[0095] The inference unit can perform inference while considering the geographical distribution of the data. For example, the inference unit can analyze the geographical distribution of the data to improve the accuracy of the inference. The inference unit can optimize the inference algorithm by considering the geographical distribution of the data. The inference unit can adjust the inference results based on the geographical distribution of the data. As a result, the inference unit improves the accuracy of its inference by considering the geographical distribution of the data. Some or all of the above processes in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the geographical distribution of the data into a generative AI and perform inference using the generative AI.

[0096] The inference unit can improve the accuracy of its inferences by referring to relevant literature during the inference process. For example, the inference unit can refer to relevant literature to improve the accuracy of its inferences. The inference unit can optimize its inference algorithm based on the relevant literature. The inference unit can adjust the inference results based on the relevant literature. As a result, the inference unit improves the accuracy of its inferences by referring to relevant literature. Some or all of the above processes in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input relevant literature into a generating AI, and the generating AI can improve the accuracy of its inferences.

[0097] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is nervous, the service provider will provide simple and easily understandable information. If the user is relaxed, the service provider will provide detailed information. If the user is in a hurry, the service provider will provide concise information. In this way, the service provider can provide easily understandable information by adjusting the way the information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the way the information is presented.

[0098] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider provides a detailed explanation for important information. For less important information, the provider provides a simplified explanation. The provider dynamically adjusts the level of detail provided according to the importance of the information. This enables efficient information provision by adjusting the level of detail provided according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into a generating AI, and the generating AI can adjust the level of detail provided.

[0099] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit applies a rapid and concise delivery algorithm to disaster information. For urban planning information, it applies a detailed and visually easy-to-understand delivery algorithm. For environmental conservation information, it applies a delivery algorithm that involves continuous monitoring and reporting. In this way, the delivery unit improves the accuracy of information delivery by applying the appropriate delivery algorithm according to the category of information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI, and the generating AI can apply an appropriate delivery algorithm.

[0100] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is tense, the information provider will prioritize important information. If the user is relaxed, the information provider will prioritize detailed information. If the user is in a hurry, the information provider will prioritize information that can be provided quickly. This enables efficient information provision by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI, and the generative AI can determine the priority of information.

[0101] The information provider can determine the priority of information provision based on when the information was collected. For example, the provider may prioritize providing the most recent information. Older information may be provided later. The provider dynamically adjusts the priority of information provision according to when the information was collected. This enables efficient information provision by allowing the provider to determine the priority of information provision based on when the information was collected. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the information collection dates into a generating AI, and the generating AI can determine the priority of information provision.

[0102] The information provider can adjust the order of information delivery based on the relevance of the information at the time of delivery. For example, the information provider can prioritize the delivery of highly relevant information. The information provider can postpone the delivery of less relevant information. The information provider can dynamically adjust the order of delivery according to the relevance of the information. This enables efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into a generating AI, and the generating AI can adjust the order of delivery.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] A geospatial data analysis system can also include a prediction unit. Based on collected geospatial data and analysis results, the prediction unit can forecast future topographic changes and the likelihood of disasters. For example, the prediction unit can combine historical earthquake data with current topographic data to predict future earthquake risk. It can also combine meteorological data with topographic data to predict flood and landslide risk. Furthermore, it can combine urban planning data with traffic data to predict future traffic congestion locations. This allows the prediction unit to provide data for identifying future risks in advance and taking appropriate countermeasures.

[0105] The data collection unit can estimate the user's emotions and select the types of data to collect based on the estimated emotions. For example, if the user is nervous, the data collection unit will prioritize collecting data that will make the user feel at ease. If the user is relaxed, the data collection unit will collect detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows the data collection unit to efficiently collect data by selecting the types of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not.

[0106] The analysis unit can determine the priority of analysis based on the reliability of the data during the analysis process. For example, it can prioritize the analysis of highly reliable data and postpone the analysis of less reliable data. The analysis unit dynamically adjusts the analysis priority according to the reliability of the data. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the reliability of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the reliability of the data into a generating AI, and the generating AI can determine the priority of analysis.

[0107] The inference unit can estimate the user's emotions and adjust the accuracy of its inferences based on the estimated emotions. For example, if the user is nervous, the inference unit provides a simple and easy-to-understand inference result. If the user is relaxed, it provides a detailed inference result. If the user is in a hurry, it provides a concise inference result. In this way, the inference unit can provide easy-to-understand inference results by adjusting the accuracy of its inferences according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the inference unit may be performed using AI or not.

[0108] The information provider can adjust its method of delivery by considering the attribute information of the information recipient. For example, if the information recipient is a specialist, detailed technical information may be provided. If the information recipient is a member of the general public, concise and easy-to-understand information may be provided. If the information recipient is an emergency responder, information that enables them to act quickly may be provided. In this way, the information provider can provide appropriate information by considering the attribute information of the information recipient. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the attribute information of the information recipient into a generating AI and adjust the method of delivery using the generating AI.

[0109] The data collection unit can prioritize the collection of geospatial data based on specific events or situations. For example, if a large-scale event is held, data in the surrounding area will be prioritized for collection. If a disaster occurs, data in the affected area will be prioritized for collection. To monitor seasonal changes, data related to a specific season will be prioritized for collection. This allows the data collection unit to efficiently collect data by prioritizing collection based on specific events or situations. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from a specific event or situation into a generating AI, which can then determine the collection priority.

[0110] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit will delay the timing of the analysis and wait until the user is relaxed. If the user is relaxed, the analysis will be sped up to perform the analysis efficiently. If the user is in a hurry, the analysis will be optimized to perform the analysis quickly. In this way, the analysis unit can perform efficient analysis by adjusting the timing of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI.

[0111] The inference unit can perform inference while considering the temporal changes in the data. For example, it can compare past data with current data to predict future changes. The inference unit analyzes the temporal changes in the data to improve the accuracy of the inference. The inference unit optimizes the inference algorithm based on the temporal changes in the data. As a result, the inference unit improves the accuracy of its inference by considering the temporal changes in the data. Some or all of the above processes in the inference unit may be performed using AI or not. For example, the inference unit can input the temporal changes in the data into a generative AI and perform inference using the generative AI.

[0112] The information provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is nervous, the information is provided in a simple and highly visible format. If the user is relaxed, the information is provided in a detailed format. If the user is in a hurry, the information is provided in a concise format. In this way, the information provider can provide highly visible information by adjusting the format of the information according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the information provider may be performed using AI or not using AI.

[0113] The information provider can adjust its delivery method by considering the recipient's feedback at the time of delivery. For example, it can adjust the level of detail of the information provided based on the recipient's feedback. It can change the format of the information provided based on the recipient's feedback. It can adjust the timing of the information provided based on the recipient's feedback. In this way, the information provider can provide appropriate information by considering the recipient's feedback. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the recipient's feedback into a generating AI, and the generating AI can adjust the delivery method.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The collection unit collects geospatial data. The collection unit collects geospatial data using, for example, RTK, SAR satellites, optical satellites, and drones. RTK provides high-precision location information, SAR satellites provide data for detecting terrain changes, optical satellites provide high-resolution images, and drones provide detailed ground photographs. Step 2: The analysis unit analyzes the geospatial data collected by the collection unit. For example, the analysis unit analyzes the collected geospatial data to detect changes in terrain and damage. The analysis unit detects changes in terrain using SAR satellite imagery and visualizes the damage using optical satellite imagery and drone photographs. Step 3: The inference unit performs inferences based on the data analyzed by the analysis unit. For example, the inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the analyzed data. The inference unit provides data to streamline infrastructure placement and transportation network design, and provides data to monitor illegal logging and ecosystem changes and strengthen conservation activities. Step 4: The provider unit provides the results obtained by the inference unit. For example, the provider unit provides the results obtained by the inference unit to public institutions or private companies.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects geospatial data using the camera 42 of the smart device 14 or a drone, and the control unit 46A detects highly accurate location information and terrain changes. The analysis unit analyzes the collected geospatial data using the specific processing unit 290 of the data processing unit 12 to detect terrain changes and damage conditions. The inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit provides the results obtained by the inference unit to public institutions and private companies using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects geospatial data using the camera 42 of the smart glasses 214 or a drone, and the control unit 46A detects highly accurate location information and terrain changes. The analysis unit analyzes the collected geospatial data using the specific processing unit 290 of the data processing unit 12, for example, and detects terrain changes and damage conditions. The inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit provides the results obtained by the inference unit to public institutions and private companies using the control unit 46A of the smart glasses 214, for example. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects geospatial data using the camera 42 of the headset terminal 314 or a drone, and the control unit 46A detects highly accurate location information and terrain changes. The analysis unit analyzes the collected geospatial data using, for example, the specific processing unit 290 of the data processing unit 12 to detect terrain changes and damage conditions. The inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit provides the results obtained by the inference unit to public institutions and private companies using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects geospatial data using the camera 42 of the robot 414 or a drone, and the control unit 46A detects highly accurate location information and terrain changes. The analysis unit analyzes the collected geospatial data using the specific processing unit 290 of the data processing unit 12, for example, and detects terrain changes and damage conditions. The inference unit optimizes evacuation plans, infrastructure placement, and conservation activities based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit provides the results obtained by the inference unit to public institutions and private companies using the control unit 46A of the robot 414, for example. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0169] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) A collection unit that collects geospatial data, An analysis unit analyzes the geospatial data collected by the aforementioned collection unit, An inference unit that performs inference based on the data analyzed by the analysis unit, The system comprises a providing unit that provides the results obtained by the inference unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect geospatial data using RTK, SAR satellites, optical satellites, and drones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected geospatial data is analyzed to detect changes in terrain and damage. The system described in Appendix 1, characterized by the features described herein. (Note 4) The inference unit, Based on the analyzed data, evacuation plans, infrastructure placement, and conservation activities will be optimized. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The results obtained by the inference unit will be provided to public institutions and private companies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, SAR satellite imagery is used to detect changes in terrain, and damage is visualized using optical satellite imagery and drone photography. The system described in Appendix 1, characterized by the features described herein. (Note 7) The inference unit, Provides data to streamline infrastructure placement and transportation network design. The system described in Appendix 1, characterized by the features described herein. (Note 8) The inference unit, We provide data to monitor illegal logging and ecosystem changes, and to strengthen conservation efforts. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and adjusts the timing of geospatial data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting geospatial data, filtering is performed based on specific environmental conditions or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting geospatial data, prioritize the collection of highly relevant data, taking geographic location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting geospatial data, analyze social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The inference unit, It estimates the user's emotions and adjusts the inference criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The inference unit, During inference, consider the interrelationships between data to improve the accuracy of the inference. The system described in Appendix 1, characterized by the features described herein. (Note 23) The inference unit, During inference, the attribute information of the data provider is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The inference unit, It estimates the user's emotions and adjusts the order in which the inference results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The inference unit, During inference, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The inference unit, When making inferences, refer to relevant literature to improve the accuracy of the inferences. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects geospatial data, An analysis unit analyzes the geospatial data collected by the aforementioned collection unit, An inference unit that performs inference based on the data analyzed by the analysis unit, The system comprises a providing unit that provides the results obtained by the inference unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect geospatial data using RTK, SAR satellites, optical satellites, and drones. The system according to feature 1.

3. The aforementioned analysis unit, The collected geospatial data is analyzed to detect changes in terrain and damage. The system according to feature 1.

4. The inference unit, Based on the analyzed data, evacuation plans, infrastructure placement, and conservation activities will be optimized. The system according to feature 1.

5. The aforementioned supply unit is, The results obtained by the aforementioned inference unit are provided to public institutions and private companies. The system according to feature 1.

6. The aforementioned analysis unit, SAR satellite imagery is used to detect changes in terrain, and damage is visualized using optical satellite imagery and drone photography. The system according to feature 1.

7. The inference unit, Provides data to streamline infrastructure placement and transportation network design. The system according to feature 1.

8. The inference unit, We provide data to monitor illegal logging and ecosystem changes, and to strengthen conservation efforts. The system according to feature 1.

Images