system

Abstract

The system according to this embodiment aims to effectively prevent damage caused by wild animals in agricultural land. [Solution] The system according to the embodiment comprises an installation unit, a detection unit, a deterrent unit, and a tracking unit. The installation unit installs a drone scarecrow in the farmland. The detection unit detects animals. The deterrent unit deters the animals detected by the detection unit with high-frequency waves or light. The tracking unit automatically tracks the animals that have been deterred by the deterrent unit.

Description

Technical Field

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[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, means for effectively preventing damage caused by wild animals on farmland have not been fully established, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively prevent damage caused by wild animals on farmland.

Means for Solving the Problems

[0006] The system according to the embodiment includes an installation unit, a detection unit, a deterrence unit, and a tracking unit. The installation unit installs a drone scarecrow on the farmland. The detection unit detects wild animals. The deterrence unit scares the wild animals detected by the detection unit with high frequency or light. The tracking unit automatically tracks the wild animals scared by the deterrence unit.

Effects of the Invention

[0007] The system according to this embodiment can effectively prevent damage caused by wild animals in agricultural land. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 drone scarecrow system according to an embodiment of the present invention is a system for preventing damage caused by wild animals to unmanaged forests and abandoned farmland, which are increasing due to the aging population and the decline in demand for domestically produced timber. This drone scarecrow system installs a drone scarecrow in farmland, and when an animal approaches the farmland, it intimidates the animal with high-frequency sounds or light, and automatically tracks the animal until it leaves the farmland, thereby repelling the animal. For example, the drone scarecrow system installs a drone scarecrow in farmland. The drone scarecrow flies around the farmland and monitors for the approach of animals. For example, the drone scarecrow is equipped with a camera and sensors, and can monitor the movement of animals in real time. Next, when the drone scarecrow system detects an animal, it intimidates the animal with high-frequency sounds or light. For example, when an animal approaches the farmland, the drone scarecrow can startle and drive away the animal by emitting high-frequency sounds or shining a strong light. Furthermore, the drone scarecrow system automatically tracks the animal until it leaves the farmland. For example, the drone scarecrow continues to track the animal while maintaining a certain distance until it leaves the farmland. This prevents animals from returning to farmland. This mechanism helps prevent damage to crops. For example, when animals such as deer or wild boars approach farmland, the drone scarecrow automatically intimidates and tracks them, protecting the crops. The drone scarecrow system can also notify administrators of the approach of animals. For example, when the drone scarecrow detects an animal, it can send video and alerts to the administrator, enabling a quick response. In this way, the drone scarecrow system can prevent damage from animals to unmanaged forests and abandoned farmland, which are increasing due to an aging population and declining demand for domestic timber, and pass on the beautiful rural landscape to future generations. In this way, the drone scarecrow system can prevent damage from animals and reduce damage to crops.

[0029] The drone scarecrow system according to the embodiment comprises an installation unit, a detection unit, a deterrent unit, and a tracking unit. The installation unit installs the drone scarecrow in the farmland. The installation unit, for example, places the drone scarecrow around the farmland to monitor for approaching animals. The installation unit can also, for example, install the drone scarecrow in the center of the farmland to monitor all directions. The installation unit can also, for example, install the drone scarecrow around the farmland to focus on monitoring a specific area. The installation unit can also, for example, install multiple drone scarecrows to have overlapping monitoring ranges. The detection unit detects animals. The detection unit monitors the movement of animals in real time, for example, using cameras or sensors. The detection unit can also detect animals using image recognition technology, for example. The detection unit can also, for example, analyze the movement of animals to detect their approach. The deterrent unit deters animals detected by the detection unit with high-frequency sounds or light. The deterrent unit, for example, generates high-frequency sounds when an animal approaches the farmland. The intimidation unit can, for example, emit a strong light when an animal approaches farmland. The intimidation unit can also, for example, intimidate an animal by combining high-frequency sound and light when it approaches farmland. The tracking unit automatically tracks the animal that has been intimidated by the intimidation unit. The tracking unit tracks the animal while maintaining a constant distance until it leaves farmland. The tracking unit can also, for example, correct its tracking path in real time until the animal leaves farmland. The tracking unit can also, for example, track the animal in cooperation with other drones or sensors until it leaves farmland. As a result, the drone scarecrow system according to this embodiment can prevent damage from wild animals and reduce damage to crops.

[0030] The installation unit installs drone scarecrows on farmland. For example, the installation unit places drone scarecrows around the perimeter of farmland to monitor for approaching animals. Specifically, the drone scarecrows are placed at regular intervals along the boundary of the farmland, and each drone is installed so that it has an overlapping monitoring range. This allows for seamless monitoring of the entire farmland. The installation unit can also install drone scarecrows in the center of the farmland to monitor all directions. In this case, the drone scarecrows are equipped with 360-degree rotatable cameras and sensors and are designed to provide a panoramic view of the entire farmland. The installation unit can also install drone scarecrows around the perimeter of farmland to focus on monitoring specific areas. For example, by placing drone scarecrows intensively in areas where animal intrusions are particularly frequent or in areas where important crops are cultivated, effective monitoring becomes possible. The installation unit can also install multiple drone scarecrows, giving them overlapping monitoring ranges. This ensures that even if one drone scarecrow malfunctions, the other drone scarecrows can cover its range, thus ensuring the continuity of monitoring. Furthermore, the installation unit is designed to flexibly change the placement and monitoring range of the drone scarecrow, allowing for optimal placement according to the season and crop growth. This enables the installation unit to efficiently monitor the entire farmland and prevent animal intrusion.

[0031] The detection unit detects animals. The detection unit monitors the movement of animals in real time, for example, using cameras and sensors. Specifically, high-resolution cameras and infrared sensors mounted on the drone scarecrow constantly monitor the movement around farmland and detect the approach of animals. The detection unit can also detect animals using image recognition technology, for example. Image recognition technology uses AI to analyze camera footage and identify the shape and movement of animals. For example, an image recognition algorithm using deep learning learns the characteristics of animals and identifies them in real time. The detection unit can also analyze the movement of animals and detect their approach, for example. For example, by analyzing the patterns of animal movement and detecting abnormal movements, the approach of animals can be detected early. Furthermore, the detection unit can also detect animal sounds using voice recognition technology. This allows for more accurate detection by utilizing not only visual but also auditory information. The detection unit transmits the detected information to the central control system in real time and works in cooperation with the intimidation unit and tracking unit to take a rapid response. This allows the detection unit to accurately grasp the movements of animals around farmland and take swift and effective countermeasures.

[0032] The deterrent unit uses high-frequency sounds and light to intimidate animals detected by the detection unit. Specifically, when an animal approaches farmland, it emits high-frequency sounds. High-frequency sounds are unpleasant to animals and have the effect of deterring them from approaching. The deterrent unit can also, for example, emit a strong light when an animal approaches farmland. The strong light stimulates the animal's vision and has the effect of preventing it from approaching. The deterrent unit can also, for example, combine high-frequency sounds and light to intimidate animals when they approach farmland. This can produce a stronger deterrent effect on animals. Furthermore, the deterrent unit can flexibly change its deterrent method depending on the type of animal and its behavioral pattern. For example, high-frequency sounds may be effective against certain animals, while light may be effective against others. In such cases, the deterrent unit can select the optimal deterrent method based on information from the detection unit. The deterrent unit can also adjust the timing and intensity of the deterrent to maximize its effectiveness. For example, by gradually increasing the intensity of the deterrent according to the speed and distance at which the animal approaches farmland, a more effective deterrent can be achieved. This allows the deterrent to prevent animals from entering and reduce damage to crops.

[0033] The tracking unit automatically tracks animals that have been intimidated by the intimidation unit. Specifically, it tracks the animal while maintaining a constant distance until it leaves the farmland. The tracking unit tracks the animal's position and movement in real time, for example, using GPS and acceleration sensors mounted on the drone scarecrow. The tracking unit can also correct its tracking path in real time until the animal leaves the farmland. This allows for quick correction of the tracking path and effective tracking even if the animal flees in an unexpected direction. The tracking unit can also track the animal in cooperation with other drones and sensors until it leaves the farmland. For example, multiple drone scarecrows working together to track an animal enables more effective tracking over a wider area. Furthermore, the tracking unit can flexibly change its tracking method depending on the type of animal and its behavioral patterns. For example, it can track a specific animal while maintaining a constant distance, while approaching and tracking another animal to enable more effective intimidation. The tracking unit can also adjust the tracking speed and distance to maximize the effectiveness of the tracking. For example, increasing the tracking speed to prevent animals from approaching farmland makes tracking more effective. This allows the tracking unit to prevent animal intrusion and reduce damage to crops.

[0034] The detection unit can detect animals using image recognition technology. The detection unit can detect animals using, for example, deep learning technology. The detection unit can also detect animals using, for example, pattern recognition technology. The detection unit can also detect animals using, for example, image analysis technology. As a result, the accuracy of animal detection is improved by using image recognition technology. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data acquired by a camera into a generating AI and have the generating AI perform animal detection.

[0035] The deterrent unit can generate high-frequency sound when an animal approaches farmland. The deterrent unit generates high-frequency sound of, for example, 20 kHz or higher. The deterrent unit can also generate high-frequency sound using, for example, a high-frequency sound generator. The deterrent unit can also generate high-frequency sound intermittently. This allows for effective deterrence of animals by generating high-frequency sound. Some or all of the above-described processes in the deterrent unit may be performed using, for example, AI, or without AI. For example, when the deterrent unit detects the approach of an animal, it can instruct the generating AI to generate high-frequency sound, and the generating AI can then generate the high-frequency sound.

[0036] The deterrent unit can emit a strong light when an animal approaches farmland. The deterrent unit can emit, for example, a laser beam. The deterrent unit can also emit, for example, an LED light. The deterrent unit can also emit light with adjustable intensity. This allows for effective deterrence of animals by emitting a strong light. Some or all of the above-described processes in the deterrent unit may be performed using, for example, AI, or without AI. For example, when the deterrent unit detects the approach of an animal, it can instruct a generating AI to emit light, and the generating AI can emit light.

[0037] The tracking unit can track an animal while maintaining a constant distance until it leaves the farmland. For example, the tracking unit can track the animal while maintaining a distance of several meters to several tens of meters. The tracking unit can also adjust the tracking distance according to the animal's movement speed. The tracking unit can also set a tracking path based on the animal's behavior pattern. This prevents the animal from returning to the farmland by tracking it while maintaining a constant distance. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the animal's position data into a generating AI, which can then set a tracking path and perform tracking.

[0038] The notification unit can notify the administrator of the approach of an animal. For example, when the notification unit detects the approach of an animal, it can notify the administrator by email. For example, when the notification unit detects the approach of an animal, it can also issue an alarm to the administrator. For example, when the notification unit detects the approach of an animal, it can also send video to the administrator. This enables a quick response by notifying the administrator of the approach of an animal. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, when the notification unit detects the approach of an animal, it can instruct a generating AI to send a notification, and the generating AI can send a notification to the administrator.

[0039] The installation unit can analyze the topography and environmental conditions of the farmland and automatically select the optimal installation location. For example, the installation unit can use the drone scarecrow to acquire topographic data of the farmland and select an installation location prioritizing flat areas. The installation unit can also use the drone scarecrow to detect surrounding vegetation and obstacles and select a location where visibility is ensured. The installation unit can also use the drone scarecrow to consider wind direction and sunlight conditions and select a location where stable flight is possible. In this way, the optimal installation location can be selected by analyzing the topography and environmental conditions of the farmland. Some or all of the above processing in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input topographic data of the farmland into a generating AI, and the generating AI can select the optimal installation location.

[0040] The installation unit can detect surrounding obstacles and set an optimal flight path during installation. For example, the installation unit can have the drone scarecrow detect surrounding buildings and trees and set a flight path that avoids collisions. The installation unit can also have the drone scarecrow detect power lines and communication cables and set a safe flight path. The installation unit can also have the drone scarecrow detect other drones or flying objects and set a flight path that avoids interference. In this way, an optimal flight path can be set by detecting surrounding obstacles. Some or all of the above processing in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input surrounding obstacle data into a generating AI, and the generating AI can set an optimal flight path.

[0041] The installation unit can adjust its installation location during installation, taking into account the growth status of crops in the farmland. For example, the installation unit can use the drone scarecrow to acquire crop growth data and select a location to focus on monitoring areas where growth is progressing. The installation unit can also use the drone scarecrow to identify crop types and prioritize installation in areas with a high risk of animal damage to specific crops. For example, the installation unit can use the drone scarecrow to consider the crop harvest time and select a location suitable for important periods before and after harvest. This allows for the selection of the optimal installation location by considering the growth status of crops in the farmland. Some or all of the above processes in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input crop growth data into a generating AI, which can then adjust the installation location.

[0042] The installation unit can analyze the surrounding weather conditions in real time during installation and determine the optimal installation timing. For example, the installation unit can use a drone scarecrow to acquire weather data and determine the installation timing considering wind speed and direction. The installation unit can also use a drone scarecrow to analyze rainfall forecasts and select an installation timing that avoids rainy weather. The installation unit can also use a drone scarecrow to consider temperature and humidity and select an installation timing that allows the equipment to perform optimally. In this way, the optimal installation timing can be determined by analyzing the surrounding weather conditions in real time. Some or all of the above processing in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input weather data into a generating AI, and the generating AI can determine the optimal installation timing.

[0043] The detection unit can apply different detection algorithms depending on the type and size of the animal upon detection. For example, the detection unit can use a drone scarecrow to identify the type of animal and apply a highly sensitive detection algorithm to large animals such as deer and wild boars. Alternatively, the detection unit can use a drone scarecrow to measure the size of the animal and apply an appropriate detection algorithm to smaller animals as well. The detection unit can also use a drone scarecrow to analyze the animal's behavior patterns and apply the optimal detection algorithm to specific behaviors. By applying different detection algorithms depending on the type and size of the animal, detection accuracy is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on the type and size of the animal into a generating AI, which can then apply the optimal detection algorithm.

[0044] The detection unit can prevent false detections by considering changes in ambient sound and light during detection. For example, the detection unit can prevent false detections by having the drone scarecrow analyze ambient sound and exclude wind and rain sounds. The detection unit can also prevent false detections caused by sunlight or car headlights by having the drone scarecrow detect changes in light. The detection unit can also prevent false detections by having the drone scarecrow comprehensively analyze the surrounding movements and exclude movements other than animals. In this way, false detections can be prevented by considering changes in ambient sound and light. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on changes in ambient sound and light into a generating AI, which can then make adjustments to prevent false detections.

[0045] The detection unit can adjust its detection range when detecting something, taking into account the geographical conditions of the farmland. For example, the detection unit can use a drone scarecrow to acquire topographic data of the farmland and detect a wide area in flat areas. For example, the detection unit can use a drone scarecrow to analyze the geographical conditions of the farmland and focus detection on a narrower area in mountainous regions. For example, the detection unit can use a drone scarecrow to recognize the boundaries of the farmland and expand the detection range near the boundaries. This allows for setting an optimal detection range by considering the geographical conditions of the farmland. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input topographic data of the farmland into a generating AI, which can then adjust the detection range.

[0046] The detection unit can improve detection accuracy by coordinating with other drones and sensors when detection occurs. For example, the detection unit can improve detection accuracy by having a drone scarecrow communicate with other drones and integrate data from multiple viewpoints. The detection unit can also improve detection accuracy by having a drone scarecrow cooperate with ground sensors and supplement ground data. The detection unit can also improve detection accuracy by having a drone scarecrow cooperate with a cloud database and refer to past data. In this way, detection accuracy is improved by coordinating with other drones and sensors. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from other drones and sensors into a generating AI, and the generating AI can integrate the data to improve detection accuracy.

[0047] The intimidation unit can apply different intimidation methods depending on the type of animal and its behavioral pattern during intimidation. For example, the intimidation unit can use a drone scarecrow to identify the type of animal and emit high-frequency sound to deer and bright light to wild boars. The intimidation unit can also use a drone scarecrow to analyze the animal's behavioral pattern and apply the most appropriate intimidation method to a specific behavior. The intimidation unit can also use a drone scarecrow to monitor the animal's reaction in real time and select the most effective intimidation method. This improves the intimidation effect by applying the most appropriate intimidation method according to the type of animal and its behavioral pattern. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data on the type of animal and its behavioral pattern into a generating AI, which can then apply the most appropriate intimidation method.

[0048] The intimidation unit can select the optimal intimidation method by considering the surrounding environmental conditions during intimidation. For example, the drone scarecrow can detect the ambient brightness and emit a strong light at night. The intimidation unit can also analyze the surrounding sounds and generate high-frequency sounds in quiet environments. For example, the drone scarecrow can consider weather conditions and prioritize sound-based intimidation during rainy weather. In this way, the optimal intimidation method can be selected by considering the surrounding environmental conditions. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data on the surrounding environmental conditions into a generating AI, which can then select the optimal intimidation method.

[0049] The intimidation unit can adjust its intimidation methods when intimidating, taking into account the types and growth stages of crops in the farmland. For example, the intimidation unit can use a drone scarecrow to identify crop types and exert strong intimidation in areas with a high risk of animal damage to specific crops. The intimidation unit can also use a drone scarecrow to analyze crop growth and focus intimidation on areas where growth is advanced. For example, the intimidation unit can use a drone scarecrow to consider the harvest time and exert strong intimidation during important periods before and after harvest. This allows for the selection of the optimal intimidation method by considering the types and growth stages of crops in the farmland. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data on crop types and growth stages into a generating AI, which can then select the optimal intimidation method.

[0050] The intimidation unit can improve its intimidation effect by coordinating with other drones and sensors during intimidation. For example, the intimidation unit can have a drone scarecrow communicate with other drones and integrate data from multiple viewpoints to improve the intimidation effect. The intimidation unit can also have a drone scarecrow coordinating with ground sensors to supplement ground data and improve the intimidation effect. The intimidation unit can also have a drone scarecrow coordinating with a cloud database and referencing past data to improve the intimidation effect. In this way, the intimidation effect is improved by coordinating with other drones and sensors. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data from other drones and sensors into a generating AI, which can then integrate the data to improve the intimidation effect.

[0051] The tracking unit can analyze the animal's behavior patterns during tracking and set the optimal tracking path. For example, the tracking unit can use a drone scarecrow to analyze the animal's behavior patterns in real time and set the optimal tracking path. Alternatively, the tracking unit can use a drone scarecrow to refer to past data and set the tracking path based on the animal's behavior patterns. The tracking unit can also use a drone scarecrow to measure the animal's movement speed and set the optimal tracking path. In this way, the optimal tracking path can be set by analyzing the animal's behavior patterns. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data on the animal's behavior patterns into a generating AI, which can then set the optimal tracking path.

[0052] The tracking unit can detect surrounding obstacles during tracking and correct the tracking path in real time. For example, the tracking unit can correct the tracking path when the drone scarecrow detects surrounding obstacles in real time. The tracking unit can also correct the tracking path when the drone scarecrow detects other drones or flying objects. The tracking unit can also correct the tracking path in real time when the drone scarecrow refers to terrain data. This allows the tracking path to be corrected in real time by detecting surrounding obstacles. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input surrounding obstacle data into a generating AI, and the generating AI can correct the tracking path.

[0053] The tracking unit can adjust its tracking path while tracking, taking into account the topography and environmental conditions of the farmland. For example, the tracking unit can have the drone scarecrow acquire topographic data of the farmland and set a straight tracking path in flat areas. The tracking unit can also have the drone scarecrow analyze the geographical conditions of the farmland and set a curved tracking path in mountainous areas. The tracking unit can also have the drone scarecrow recognize the boundaries of the farmland and widen the tracking path near the boundaries. This allows for the setting of an optimal tracking path by taking into account the topography and environmental conditions of the farmland. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input topographic data of the farmland into a generating AI, and the generating AI can adjust the tracking path.

[0054] The tracking unit can improve tracking accuracy by coordinating with other drones and sensors during tracking. For example, the tracking unit can improve tracking accuracy by having the drone scarecrow communicate with other drones and integrate data from multiple viewpoints. The tracking unit can also improve tracking accuracy by having the drone scarecrow cooperate with ground sensors and supplement ground data. The tracking unit can also improve tracking accuracy by having the drone scarecrow cooperate with a cloud database and refer to past data. In this way, tracking accuracy is improved by coordinating with other drones and sensors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data from other drones and sensors into a generating AI, and the generating AI can integrate the data to improve tracking accuracy.

[0055] The notification unit can apply different notification methods depending on the type of animal and its behavioral pattern when a notification is sent. For example, the notification unit can use a drone scarecrow to identify the type of animal and send an audio notification for deer and a visual notification for wild boars. The notification unit can also use a drone scarecrow to analyze the animal's behavioral pattern and apply the most suitable notification method for a specific behavior. The notification unit can also use a drone scarecrow to monitor the animal's reaction in real time and select the most effective notification method. This improves the effectiveness of notifications by applying the most suitable notification method according to the type of animal and its behavioral pattern. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the type of animal and its behavioral pattern into a generating AI, which can then apply the most suitable notification method.

[0056] The notification unit can select the optimal notification method when sending a notification, taking into account the condition of the farmland and the manager's schedule. For example, the notification unit can use a drone scarecrow to monitor the condition of the farmland in real time and send a notification at the appropriate time. For example, the notification unit can use a drone scarecrow to refer to the manager's schedule and send a notification when the manager is available. For example, the notification unit can use a drone scarecrow to consider important events in the farmland (such as harvest time) and send a notification at the appropriate time. This allows the system to select the optimal notification method by considering the condition of the farmland and the manager's schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the condition of the farmland and the manager's schedule into a generating AI, which can then select the optimal notification method.

[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] The drone scarecrow system can also be equipped with an energy management unit. This unit monitors the drone scarecrow's battery level and guides it to a charging station as needed. For example, the energy management unit can automatically return the drone scarecrow to a charging station when its battery level falls below a certain point. The energy management unit can also charge the drone scarecrow's battery using, for example, solar panels. Furthermore, the energy management unit can optimize the drone scarecrow's flight path to minimize energy consumption. This extends the drone scarecrow's operating time and enables more efficient operation.

[0059] The drone scarecrow system can also be equipped with a communication unit. The communication unit enables real-time data communication between the drone scarecrow and the administrator. For example, when the drone scarecrow detects an animal, the communication unit can transmit video to the administrator in real time. The communication unit can also enable the drone scarecrow to periodically report on the condition of the farmland, allowing the administrator to monitor it remotely. The communication unit can also enable the drone scarecrow to cooperate with other drones and sensors and share data. This makes it easier for the administrator to understand the condition of the farmland and enables quicker responses.

[0060] The drone scarecrow system can also be equipped with a prediction unit. The prediction unit analyzes past data and predicts animal appearance patterns. For example, based on past animal appearance data, the prediction unit can predict the times and locations where animals are likely to appear. The prediction unit can also consider weather data to predict conditions that increase the risk of animal appearances. The prediction unit can also analyze the growth status of crops to predict the times when animals are most likely to target them. This allows for the optimization of the placement and operation of drone scarecrows based on predictions, thereby preventing damage caused by animals.

[0061] The drone scarecrow system can also be equipped with a maintenance unit. The maintenance unit monitors the status of the drone scarecrow and notifies the user of necessary maintenance. For example, the maintenance unit can monitor the wear and tear of the drone scarecrow's parts and notify the user when replacement is needed. The maintenance unit can also check the status of the drone scarecrow's sensors and cameras and notify the administrator if any abnormalities are found. The maintenance unit can also automatically update the drone scarecrow's software. This helps maintain the reliability of the drone scarecrow and enables stable operation over a long period of time.

[0062] The drone scarecrow system can also be equipped with a data analysis unit. This unit analyzes the data collected by the drone scarecrow and uses it to improve farmland management. For example, the unit can analyze animal sighting data to understand trends in animal damage. It can also analyze crop growth data to predict optimal harvest times. Furthermore, it can analyze weather data to aid in farm work planning. This enables data-driven farmland management and improves crop productivity.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The installation team installs the drone scarecrows in the farmland. The installation team can, for example, place the drone scarecrows around or in the center of the farmland to monitor all directions or focus on a specific area. It is also possible to install multiple drone scarecrows to create overlapping monitoring areas. Step 2: The detection unit detects the animal. The detection unit can, for example, monitor the animal's movements in real time using cameras or sensors and detect the animal using image recognition technology. It can also analyze the animal's movements and detect its approach. Step 3: The deterrent unit deters animals detected by the detection unit using high-frequency sounds or light. For example, when an animal approaches farmland, the deterrent unit can emit high-frequency sounds or emit bright light. It can also deter animals by combining high-frequency sounds and light. Step 4: The tracking unit automatically tracks the animal that has been threatened by the intimidation unit. The tracking unit can, for example, track the animal while maintaining a constant distance until it leaves the farmland, and can adjust the tracking path in real time. It can also track in cooperation with other drones and sensors.

[0065] (Example of form 2) The drone scarecrow system according to an embodiment of the present invention is a system for preventing damage caused by wild animals to unmanaged forests and abandoned farmland, which are increasing due to the aging population and the decline in demand for domestically produced timber. This drone scarecrow system installs a drone scarecrow in farmland, and when an animal approaches the farmland, it intimidates the animal with high-frequency sounds or light, and automatically tracks the animal until it leaves the farmland, thereby repelling the animal. For example, the drone scarecrow system installs a drone scarecrow in farmland. The drone scarecrow flies around the farmland and monitors for the approach of animals. For example, the drone scarecrow is equipped with a camera and sensors, and can monitor the movement of animals in real time. Next, when the drone scarecrow system detects an animal, it intimidates the animal with high-frequency sounds or light. For example, when an animal approaches the farmland, the drone scarecrow can startle and drive away the animal by emitting high-frequency sounds or shining a strong light. Furthermore, the drone scarecrow system automatically tracks the animal until it leaves the farmland. For example, the drone scarecrow continues to track the animal while maintaining a certain distance until it leaves the farmland. This prevents animals from returning to farmland. This mechanism helps prevent damage to crops. For example, when animals such as deer or wild boars approach farmland, the drone scarecrow automatically intimidates and tracks them, protecting the crops. The drone scarecrow system can also notify administrators of the approach of animals. For example, when the drone scarecrow detects an animal, it can send video and alerts to the administrator, enabling a quick response. In this way, the drone scarecrow system can prevent damage from animals to unmanaged forests and abandoned farmland, which are increasing due to an aging population and declining demand for domestic timber, and pass on the beautiful rural landscape to future generations. In this way, the drone scarecrow system can prevent damage from animals and reduce damage to crops.

[0066] The drone scarecrow system according to the embodiment comprises an installation unit, a detection unit, a deterrent unit, and a tracking unit. The installation unit installs the drone scarecrow in the farmland. The installation unit, for example, places the drone scarecrow around the farmland to monitor for approaching animals. The installation unit can also, for example, install the drone scarecrow in the center of the farmland to monitor all directions. The installation unit can also, for example, install the drone scarecrow around the farmland to focus on monitoring a specific area. The installation unit can also, for example, install multiple drone scarecrows to have overlapping monitoring ranges. The detection unit detects animals. The detection unit monitors the movement of animals in real time, for example, using cameras or sensors. The detection unit can also detect animals using image recognition technology, for example. The detection unit can also, for example, analyze the movement of animals to detect their approach. The deterrent unit deters animals detected by the detection unit with high-frequency sounds or light. The deterrent unit, for example, generates high-frequency sounds when an animal approaches the farmland. The intimidation unit can, for example, emit a strong light when an animal approaches farmland. The intimidation unit can also, for example, intimidate an animal by combining high-frequency sound and light when it approaches farmland. The tracking unit automatically tracks the animal that has been intimidated by the intimidation unit. The tracking unit tracks the animal while maintaining a constant distance until it leaves farmland. The tracking unit can also, for example, correct its tracking path in real time until the animal leaves farmland. The tracking unit can also, for example, track the animal in cooperation with other drones or sensors until it leaves farmland. As a result, the drone scarecrow system according to this embodiment can prevent damage from wild animals and reduce damage to crops.

[0067] The installation unit installs drone scarecrows on farmland. For example, the installation unit places drone scarecrows around the perimeter of farmland to monitor for approaching animals. Specifically, the drone scarecrows are placed at regular intervals along the boundary of the farmland, and each drone is installed so that it has an overlapping monitoring range. This allows for seamless monitoring of the entire farmland. The installation unit can also install drone scarecrows in the center of the farmland to monitor all directions. In this case, the drone scarecrows are equipped with 360-degree rotatable cameras and sensors and are designed to provide a panoramic view of the entire farmland. The installation unit can also install drone scarecrows around the perimeter of farmland to focus on monitoring specific areas. For example, by placing drone scarecrows intensively in areas where animal intrusions are particularly frequent or in areas where important crops are cultivated, effective monitoring becomes possible. The installation unit can also install multiple drone scarecrows, giving them overlapping monitoring ranges. This ensures that even if one drone scarecrow malfunctions, the other drone scarecrows can cover its range, thus ensuring the continuity of monitoring. Furthermore, the installation unit is designed to flexibly change the placement and monitoring range of the drone scarecrow, allowing for optimal placement according to the season and crop growth. This enables the installation unit to efficiently monitor the entire farmland and prevent animal intrusion.

[0068] The detection unit detects animals. The detection unit monitors the movement of animals in real time, for example, using cameras and sensors. Specifically, high-resolution cameras and infrared sensors mounted on the drone scarecrow constantly monitor the movement around farmland and detect the approach of animals. The detection unit can also detect animals using image recognition technology, for example. Image recognition technology uses AI to analyze camera footage and identify the shape and movement of animals. For example, an image recognition algorithm using deep learning learns the characteristics of animals and identifies them in real time. The detection unit can also analyze the movement of animals and detect their approach, for example. For example, by analyzing the patterns of animal movement and detecting abnormal movements, the approach of animals can be detected early. Furthermore, the detection unit can also detect animal sounds using voice recognition technology. This allows for more accurate detection by utilizing not only visual but also auditory information. The detection unit transmits the detected information to the central control system in real time and works in cooperation with the intimidation unit and tracking unit to take a rapid response. This allows the detection unit to accurately grasp the movements of animals around farmland and take swift and effective countermeasures.

[0069] The deterrent unit uses high-frequency sounds and light to intimidate animals detected by the detection unit. Specifically, when an animal approaches farmland, it emits high-frequency sounds. High-frequency sounds are unpleasant to animals and have the effect of deterring them from approaching. The deterrent unit can also, for example, emit a strong light when an animal approaches farmland. The strong light stimulates the animal's vision and has the effect of preventing it from approaching. The deterrent unit can also, for example, combine high-frequency sounds and light to intimidate animals when they approach farmland. This can produce a stronger deterrent effect on animals. Furthermore, the deterrent unit can flexibly change its deterrent method depending on the type of animal and its behavioral pattern. For example, high-frequency sounds may be effective against certain animals, while light may be effective against others. In such cases, the deterrent unit can select the optimal deterrent method based on information from the detection unit. The deterrent unit can also adjust the timing and intensity of the deterrent to maximize its effectiveness. For example, by gradually increasing the intensity of the deterrent according to the speed and distance at which the animal approaches farmland, a more effective deterrent can be achieved. This allows the deterrent to prevent animals from entering and reduce damage to crops.

[0070] The tracking unit automatically tracks animals that have been intimidated by the intimidation unit. Specifically, it tracks the animal while maintaining a constant distance until it leaves the farmland. The tracking unit tracks the animal's position and movement in real time, for example, using GPS and acceleration sensors mounted on the drone scarecrow. The tracking unit can also correct its tracking path in real time until the animal leaves the farmland. This allows for quick correction of the tracking path and effective tracking even if the animal flees in an unexpected direction. The tracking unit can also track the animal in cooperation with other drones and sensors until it leaves the farmland. For example, multiple drone scarecrows working together to track an animal enables more effective tracking over a wider area. Furthermore, the tracking unit can flexibly change its tracking method depending on the type of animal and its behavioral patterns. For example, it can track a specific animal while maintaining a constant distance, while approaching and tracking another animal to enable more effective intimidation. The tracking unit can also adjust the tracking speed and distance to maximize the effectiveness of the tracking. For example, increasing the tracking speed to prevent animals from approaching farmland makes tracking more effective. This allows the tracking unit to prevent animal intrusion and reduce damage to crops.

[0071] The detection unit can detect animals using image recognition technology. The detection unit can detect animals using, for example, deep learning technology. The detection unit can also detect animals using, for example, pattern recognition technology. The detection unit can also detect animals using, for example, image analysis technology. As a result, the accuracy of animal detection is improved by using image recognition technology. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data acquired by a camera into a generating AI and have the generating AI perform animal detection.

[0072] The deterrent unit can generate high-frequency sound when an animal approaches farmland. The deterrent unit generates high-frequency sound of, for example, 20 kHz or higher. The deterrent unit can also generate high-frequency sound using, for example, a high-frequency sound generator. The deterrent unit can also generate high-frequency sound intermittently. This allows for effective deterrence of animals by generating high-frequency sound. Some or all of the above-described processes in the deterrent unit may be performed using, for example, AI, or without AI. For example, when the deterrent unit detects the approach of an animal, it can instruct the generating AI to generate high-frequency sound, and the generating AI can then generate the high-frequency sound.

[0073] The deterrent unit can emit a strong light when an animal approaches farmland. The deterrent unit can emit, for example, a laser beam. The deterrent unit can also emit, for example, an LED light. The deterrent unit can also emit light with adjustable intensity. This allows for effective deterrence of animals by emitting a strong light. Some or all of the above-described processes in the deterrent unit may be performed using, for example, AI, or without AI. For example, when the deterrent unit detects the approach of an animal, it can instruct a generating AI to emit light, and the generating AI can emit light.

[0074] The tracking unit can track an animal while maintaining a constant distance until it leaves the farmland. For example, the tracking unit can track the animal while maintaining a distance of several meters to several tens of meters. The tracking unit can also adjust the tracking distance according to the animal's movement speed. The tracking unit can also set a tracking path based on the animal's behavior pattern. This prevents the animal from returning to the farmland by tracking it while maintaining a constant distance. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the animal's position data into a generating AI, which can then set a tracking path and perform tracking.

[0075] The notification unit can notify the administrator of the approach of an animal. For example, when the notification unit detects the approach of an animal, it can notify the administrator by email. For example, when the notification unit detects the approach of an animal, it can also issue an alarm to the administrator. For example, when the notification unit detects the approach of an animal, it can also send video to the administrator. This enables a quick response by notifying the administrator of the approach of an animal. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, when the notification unit detects the approach of an animal, it can instruct a generating AI to send a notification, and the generating AI can send a notification to the administrator.

[0076] The installation unit can estimate the user's emotions and adjust the placement of the drone scarecrow based on the estimated emotions. For example, if the user is feeling anxious, the installation unit can place the drone scarecrow in the center of the farmland to monitor all directions. If the user is feeling at ease, the installation unit can place the drone scarecrow around the farmland to focus on monitoring a specific area. If the user is feeling cautious, the installation unit can place multiple drone scarecrows to create overlapping monitoring areas. By adjusting the placement based on the user's emotions, the user's sense of security can be enhanced. Emotion estimation is achieved using an emotion estimation function, such as 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 installation unit may be performed using AI, or not using AI. For example, the installation unit can input user emotion data into the generative AI, which can then perform the adjustment of the placement.

[0077] The installation unit can analyze the topography and environmental conditions of the farmland and automatically select the optimal installation location. For example, the installation unit can use the drone scarecrow to acquire topographic data of the farmland and select an installation location prioritizing flat areas. The installation unit can also use the drone scarecrow to detect surrounding vegetation and obstacles and select a location where visibility is ensured. The installation unit can also use the drone scarecrow to consider wind direction and sunlight conditions and select a location where stable flight is possible. In this way, the optimal installation location can be selected by analyzing the topography and environmental conditions of the farmland. Some or all of the above processing in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input topographic data of the farmland into a generating AI, and the generating AI can select the optimal installation location.

[0078] The installation unit can detect surrounding obstacles and set an optimal flight path during installation. For example, the installation unit can have the drone scarecrow detect surrounding buildings and trees and set a flight path that avoids collisions. The installation unit can also have the drone scarecrow detect power lines and communication cables and set a safe flight path. The installation unit can also have the drone scarecrow detect other drones or flying objects and set a flight path that avoids interference. In this way, an optimal flight path can be set by detecting surrounding obstacles. Some or all of the above processing in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input surrounding obstacle data into a generating AI, and the generating AI can set an optimal flight path.

[0079] The installation unit can estimate the user's emotions and adjust the installation timing based on the estimated emotions. For example, if the user is anxious, the installation unit can quickly install the drone scarecrow. For example, if the user is calm, the installation unit can also systematically select the optimal installation timing. For example, if the user is feeling anxious, the installation unit can provide a detailed explanation before installation to reassure them. In this way, adjusting the installation timing based on the user's emotions can enhance the user's sense of security. 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 installation unit may be performed using AI, for example, or not using AI. For example, the installation unit can input user emotion data into the generative AI, which can then adjust the installation timing.

[0080] The installation unit can adjust its installation location during installation, taking into account the growth status of crops in the farmland. For example, the installation unit can use the drone scarecrow to acquire crop growth data and select a location to focus on monitoring areas where growth is progressing. The installation unit can also use the drone scarecrow to identify crop types and prioritize installation in areas with a high risk of animal damage to specific crops. For example, the installation unit can use the drone scarecrow to consider the crop harvest time and select a location suitable for important periods before and after harvest. This allows for the selection of the optimal installation location by considering the growth status of crops in the farmland. Some or all of the above processes in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input crop growth data into a generating AI, which can then adjust the installation location.

[0081] The installation unit can analyze the surrounding weather conditions in real time during installation and determine the optimal installation timing. For example, the installation unit can use a drone scarecrow to acquire weather data and determine the installation timing considering wind speed and direction. The installation unit can also use a drone scarecrow to analyze rainfall forecasts and select an installation timing that avoids rainy weather. The installation unit can also use a drone scarecrow to consider temperature and humidity and select an installation timing that allows the equipment to perform optimally. In this way, the optimal installation timing can be determined by analyzing the surrounding weather conditions in real time. Some or all of the above processing in the installation unit may be performed using AI, for example, or without AI. For example, the installation unit can input weather data into a generating AI, and the generating AI can determine the optimal installation timing.

[0082] The detection unit can estimate the user's emotions and adjust the detection sensitivity based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can increase the detection sensitivity to detect even small movements. For example, if the user is feeling at ease, the detection unit can set the detection sensitivity to normal to reduce false positives. For example, if the user is on alert, the detection unit can set the detection sensitivity to maximum to detect any movement. In this way, adjusting the detection sensitivity based on the user's emotions can enhance the user's sense of security. 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI, which can then adjust the detection sensitivity.

[0083] The detection unit can apply different detection algorithms depending on the type and size of the animal upon detection. For example, the detection unit can use a drone scarecrow to identify the type of animal and apply a highly sensitive detection algorithm to large animals such as deer and wild boars. Alternatively, the detection unit can use a drone scarecrow to measure the size of the animal and apply an appropriate detection algorithm to smaller animals as well. The detection unit can also use a drone scarecrow to analyze the animal's behavior patterns and apply the optimal detection algorithm to specific behaviors. By applying different detection algorithms depending on the type and size of the animal, detection accuracy is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on the type and size of the animal into a generating AI, which can then apply the optimal detection algorithm.

[0084] The detection unit can prevent false detections by considering changes in ambient sound and light during detection. For example, the detection unit can prevent false detections by having the drone scarecrow analyze ambient sound and exclude wind and rain sounds. The detection unit can also prevent false detections caused by sunlight or car headlights by having the drone scarecrow detect changes in light. The detection unit can also prevent false detections by having the drone scarecrow comprehensively analyze the surrounding movements and exclude movements other than animals. In this way, false detections can be prevented by considering changes in ambient sound and light. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on changes in ambient sound and light into a generating AI, which can then make adjustments to prevent false detections.

[0085] The detection unit can estimate the user's emotions and adjust the notification method of the detection result based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit can immediately notify and provide detailed information. For example, if the user is feeling at ease, the detection unit can use the normal notification method and provide only the necessary information. For example, if the user is on alert, the detection unit can issue an emergency notification to prompt a quick response. In this way, adjusting the notification method based on the user's emotions can enhance the user's sense of security. 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI, and the generative AI can adjust the notification method.

[0086] The detection unit can adjust its detection range when detecting something, taking into account the geographical conditions of the farmland. For example, the detection unit can use a drone scarecrow to acquire topographic data of the farmland and detect a wide area in flat areas. For example, the detection unit can use a drone scarecrow to analyze the geographical conditions of the farmland and focus detection on a narrower area in mountainous regions. For example, the detection unit can use a drone scarecrow to recognize the boundaries of the farmland and expand the detection range near the boundaries. This allows for setting an optimal detection range by considering the geographical conditions of the farmland. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input topographic data of the farmland into a generating AI, which can then adjust the detection range.

[0087] The detection unit can improve detection accuracy by coordinating with other drones and sensors when detection occurs. For example, the detection unit can improve detection accuracy by having a drone scarecrow communicate with other drones and integrate data from multiple viewpoints. The detection unit can also improve detection accuracy by having a drone scarecrow cooperate with ground sensors and supplement ground data. The detection unit can also improve detection accuracy by having a drone scarecrow cooperate with a cloud database and refer to past data. In this way, detection accuracy is improved by coordinating with other drones and sensors. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from other drones and sensors into a generating AI, and the generating AI can integrate the data to improve detection accuracy.

[0088] The intimidation unit can estimate the user's emotions and adjust the intensity of the intimidation based on the estimated emotions. For example, if the user is feeling anxious, the intimidation unit can set the intensity of the intimidation to the maximum to reliably drive away the animal. For example, if the user is feeling at ease, the intimidation unit can also set the intensity of the intimidation to normal and perform the minimum necessary intimidation. For example, if the user is wary, the intimidation unit can also set the intensity of the intimidation to a higher level to give the animal a strong warning. In this way, adjusting the intensity of the intimidation based on the user's emotions can increase the user's sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or not using AI. For example, the intimidation unit can input the user's emotion data into the generative AI, and the generative AI can adjust the intensity of the intimidation.

[0089] The intimidation unit can apply different intimidation methods depending on the type of animal and its behavioral pattern during intimidation. For example, the intimidation unit can use a drone scarecrow to identify the type of animal and emit high-frequency sound to deer and bright light to wild boars. The intimidation unit can also use a drone scarecrow to analyze the animal's behavioral pattern and apply the most appropriate intimidation method to a specific behavior. The intimidation unit can also use a drone scarecrow to monitor the animal's reaction in real time and select the most effective intimidation method. This improves the intimidation effect by applying the most appropriate intimidation method according to the type of animal and its behavioral pattern. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data on the type of animal and its behavioral pattern into a generating AI, which can then apply the most appropriate intimidation method.

[0090] The intimidation unit can select the optimal intimidation method by considering the surrounding environmental conditions during intimidation. For example, the drone scarecrow can detect the ambient brightness and emit a strong light at night. The intimidation unit can also analyze the surrounding sounds and generate high-frequency sounds in quiet environments. For example, the drone scarecrow can consider weather conditions and prioritize sound-based intimidation during rainy weather. In this way, the optimal intimidation method can be selected by considering the surrounding environmental conditions. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data on the surrounding environmental conditions into a generating AI, which can then select the optimal intimidation method.

[0091] The intimidation unit can estimate the user's emotions and adjust the timing of the intimidation based on the estimated emotions. For example, if the user is feeling anxious, the intimidation unit will start intimidating the moment the animal approaches. For example, if the user is feeling at ease, the intimidation unit can also start intimidating when the animal approaches a certain distance. For example, if the user is wary, the intimidation unit can start intimidating when the animal is still far away. In this way, by adjusting the timing of the intimidation based on the user's emotions, the user's sense of security can be enhanced. 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 intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input the user's emotion data into the generative AI, which can then adjust the timing of the intimidation.

[0092] The intimidation unit can adjust its intimidation methods when intimidating, taking into account the types and growth stages of crops in the farmland. For example, the intimidation unit can use a drone scarecrow to identify crop types and exert strong intimidation in areas with a high risk of animal damage to specific crops. The intimidation unit can also use a drone scarecrow to analyze crop growth and focus intimidation on areas where growth is advanced. For example, the intimidation unit can use a drone scarecrow to consider the harvest time and exert strong intimidation during important periods before and after harvest. This allows for the selection of the optimal intimidation method by considering the types and growth stages of crops in the farmland. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data on crop types and growth stages into a generating AI, which can then select the optimal intimidation method.

[0093] The intimidation unit can improve its intimidation effect by coordinating with other drones and sensors during intimidation. For example, the intimidation unit can have a drone scarecrow communicate with other drones and integrate data from multiple viewpoints to improve the intimidation effect. The intimidation unit can also have a drone scarecrow coordinating with ground sensors to supplement ground data and improve the intimidation effect. The intimidation unit can also have a drone scarecrow coordinating with a cloud database and referencing past data to improve the intimidation effect. In this way, the intimidation effect is improved by coordinating with other drones and sensors. Some or all of the above processing in the intimidation unit may be performed using AI, for example, or without AI. For example, the intimidation unit can input data from other drones and sensors into a generating AI, which can then integrate the data to improve the intimidation effect.

[0094] The tracking unit can estimate the user's emotions and adjust the tracking distance based on the estimated emotions. For example, if the user is feeling anxious, the tracking unit can maintain a short distance from the animal and track it reliably. For example, if the user is feeling at ease, the tracking unit can maintain a normal distance from the animal and track it efficiently. For example, if the user is wary, the tracking unit can maintain the maximum distance from the animal and start tracking early. This allows for an increase in the user's sense of security by adjusting the tracking distance based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, or not using AI. For example, the tracking unit can input user emotion data into the generative AI, which can then adjust the tracking distance.

[0095] The tracking unit can analyze the animal's behavior patterns during tracking and set the optimal tracking path. For example, the tracking unit can use a drone scarecrow to analyze the animal's behavior patterns in real time and set the optimal tracking path. Alternatively, the tracking unit can use a drone scarecrow to refer to past data and set the tracking path based on the animal's behavior patterns. The tracking unit can also use a drone scarecrow to measure the animal's movement speed and set the optimal tracking path. In this way, the optimal tracking path can be set by analyzing the animal's behavior patterns. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data on the animal's behavior patterns into a generating AI, which can then set the optimal tracking path.

[0096] The tracking unit can detect surrounding obstacles during tracking and correct the tracking path in real time. For example, the tracking unit can correct the tracking path when the drone scarecrow detects surrounding obstacles in real time. The tracking unit can also correct the tracking path when the drone scarecrow detects other drones or flying objects. The tracking unit can also correct the tracking path in real time when the drone scarecrow refers to terrain data. This allows the tracking path to be corrected in real time by detecting surrounding obstacles. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input surrounding obstacle data into a generating AI, and the generating AI can correct the tracking path.

[0097] The tracking unit can estimate the user's emotions and adjust the tracking speed based on the estimated emotions. For example, if the user is feeling anxious, the tracking unit can set the tracking speed to maximum and track quickly. For example, if the user is feeling at ease, the tracking unit can also set the tracking speed to normal and track efficiently. For example, if the user is wary, the tracking unit can set the tracking speed higher and start tracking earlier. In this way, adjusting the tracking speed based on the user's emotions can enhance the user's sense of security. 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 tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user emotion data into the generative AI, which can then adjust the tracking speed.

[0098] The tracking unit can adjust its tracking path while tracking, taking into account the topography and environmental conditions of the farmland. For example, the tracking unit can have the drone scarecrow acquire topographic data of the farmland and set a straight tracking path in flat areas. The tracking unit can also have the drone scarecrow analyze the geographical conditions of the farmland and set a curved tracking path in mountainous areas. The tracking unit can also have the drone scarecrow recognize the boundaries of the farmland and widen the tracking path near the boundaries. This allows for the setting of an optimal tracking path by taking into account the topography and environmental conditions of the farmland. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input topographic data of the farmland into a generating AI, and the generating AI can adjust the tracking path.

[0099] The tracking unit can improve tracking accuracy by coordinating with other drones and sensors during tracking. For example, the tracking unit can improve tracking accuracy by having the drone scarecrow communicate with other drones and integrate data from multiple viewpoints. The tracking unit can also improve tracking accuracy by having the drone scarecrow cooperate with ground sensors and supplement ground data. The tracking unit can also improve tracking accuracy by having the drone scarecrow cooperate with a cloud database and refer to past data. In this way, tracking accuracy is improved by coordinating with other drones and sensors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data from other drones and sensors into a generating AI, and the generating AI can integrate the data to improve tracking accuracy.

[0100] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can provide a notification with detailed information to reassure them. For example, if the user is feeling at ease, the notification unit can also provide a notification with only the necessary minimum information. For example, if the user is on alert, the notification unit can provide a notification with urgent information to encourage a quick response. In this way, adjusting the content of the notification based on the user's emotions can enhance the user's sense of security. 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into the generative AI, and the generative AI can adjust the content of the notification.

[0101] The notification unit can apply different notification methods depending on the type of animal and its behavioral pattern when a notification is sent. For example, the notification unit can use a drone scarecrow to identify the type of animal and send an audio notification for deer and a visual notification for wild boars. The notification unit can also use a drone scarecrow to analyze the animal's behavioral pattern and apply the most suitable notification method for a specific behavior. The notification unit can also use a drone scarecrow to monitor the animal's reaction in real time and select the most effective notification method. This improves the effectiveness of notifications by applying the most suitable notification method according to the type of animal and its behavioral pattern. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the type of animal and its behavioral pattern into a generating AI, which can then apply the most suitable notification method.

[0102] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can immediately send a notification to provide reassurance. If the user is feeling reassured, the notification unit can use the normal notification timing and provide only the necessary information. If the user is on alert, the notification unit can send an emergency notification to encourage a quick response. In this way, adjusting the timing of notifications based on the user's emotions can enhance the user's sense of security. 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 notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI, which can then adjust the timing of notifications.

[0103] The notification unit can select the optimal notification method when sending a notification, taking into account the condition of the farmland and the manager's schedule. For example, the notification unit can use a drone scarecrow to monitor the condition of the farmland in real time and send a notification at the appropriate time. For example, the notification unit can use a drone scarecrow to refer to the manager's schedule and send a notification when the manager is available. For example, the notification unit can use a drone scarecrow to consider important events in the farmland (such as harvest time) and send a notification at the appropriate time. This allows the system to select the optimal notification method by considering the condition of the farmland and the manager's schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the condition of the farmland and the manager's schedule into a generating AI, which can then select the optimal notification method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The drone scarecrow system can also be equipped with an energy management unit. This unit monitors the drone scarecrow's battery level and guides it to a charging station as needed. For example, the energy management unit can automatically return the drone scarecrow to a charging station when its battery level falls below a certain point. The energy management unit can also charge the drone scarecrow's battery using, for example, solar panels. Furthermore, the energy management unit can optimize the drone scarecrow's flight path to minimize energy consumption. This extends the drone scarecrow's operating time and enables more efficient operation.

[0106] The drone scarecrow system can also be equipped with a communication unit. The communication unit enables real-time data communication between the drone scarecrow and the administrator. For example, when the drone scarecrow detects an animal, the communication unit can transmit video to the administrator in real time. The communication unit can also enable the drone scarecrow to periodically report on the condition of the farmland, allowing the administrator to monitor it remotely. The communication unit can also enable the drone scarecrow to cooperate with other drones and sensors and share data. This makes it easier for the administrator to understand the condition of the farmland and enables quicker responses.

[0107] The drone scarecrow system can also be equipped with a prediction unit. The prediction unit analyzes past data and predicts animal appearance patterns. For example, based on past animal appearance data, the prediction unit can predict the times and locations where animals are likely to appear. The prediction unit can also consider weather data to predict conditions that increase the risk of animal appearances. The prediction unit can also analyze the growth status of crops to predict the times when animals are most likely to target them. This allows for the optimization of the placement and operation of drone scarecrows based on predictions, thereby preventing damage caused by animals.

[0108] The drone scarecrow system can also be equipped with a maintenance unit. The maintenance unit monitors the status of the drone scarecrow and notifies the user of necessary maintenance. For example, the maintenance unit can monitor the wear and tear of the drone scarecrow's parts and notify the user when replacement is needed. The maintenance unit can also check the status of the drone scarecrow's sensors and cameras and notify the administrator if any abnormalities are found. The maintenance unit can also automatically update the drone scarecrow's software. This helps maintain the reliability of the drone scarecrow and enables stable operation over a long period of time.

[0109] The drone scarecrow system can also be equipped with a data analysis unit. This unit analyzes the data collected by the drone scarecrow and uses it to improve farmland management. For example, the unit can analyze animal sighting data to understand trends in animal damage. It can also analyze crop growth data to predict optimal harvest times. Furthermore, it can analyze weather data to aid in farm work planning. This enables data-driven farmland management and improves crop productivity.

[0110] The drone scarecrow system can also include a notification unit that estimates the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling anxious, the notification unit can provide a notification with detailed information to reassure them. If the user is feeling at ease, the notification unit can also provide a notification with only the essential information. If the user is on alert, the notification unit can provide a notification with urgent information to encourage a quick response. In this way, adjusting the notification content based on the user's emotions can enhance the user's sense of security.

[0111] The drone scarecrow system may also include a deterrent unit that estimates the user's emotions and adjusts the intensity of the deterrent based on those emotions. For example, if the user is feeling anxious, the deterrent unit can set the intensity to maximum to effectively drive away animals. If the user is feeling at ease, the deterrent unit can set the intensity to normal, providing only the minimum necessary deterrent. If the user is feeling wary, the deterrent unit can set the intensity to higher, giving a strong warning to animals. This allows the system to enhance the user's sense of security by adjusting the intensity of the deterrent based on their emotions.

[0112] The drone scarecrow system can also be equipped with a tracking unit that estimates the user's emotions and adjusts the tracking distance based on those emotions. For example, if the user is feeling anxious, the tracking unit can maintain a short distance from the animal and track it reliably. If the user is feeling at ease, the tracking unit can maintain a normal distance from the animal and track it efficiently. If the user is wary, the tracking unit can maintain the maximum distance from the animal and begin tracking early. This allows the system to enhance the user's sense of security by adjusting the tracking distance based on their emotions.

[0113] The drone scarecrow system can also include an installation unit that estimates the user's emotions and adjusts the installation timing based on those emotions. For example, if the user is anxious, the installation unit can quickly install the drone scarecrow. If the user is calm, the installation unit can also systematically select the optimal installation timing. If the user is feeling anxious, the installation unit can provide detailed explanations before installation to reassure them. In this way, adjusting the installation timing based on the user's emotions can enhance the user's sense of security.

[0114] The drone scarecrow system can also include a detection unit that estimates the user's emotions and adjusts the detection sensitivity based on those emotions. For example, if the user is feeling anxious, the detection unit can increase its sensitivity to detect even small movements. If the user is feeling at ease, the detection unit can set the sensitivity to normal to reduce false detections. If the user is feeling alert, the detection unit can set the sensitivity to maximum to detect any movement. This allows the system to enhance the user's sense of security by adjusting the detection sensitivity based on their emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The installation team installs the drone scarecrows in the farmland. The installation team can, for example, place the drone scarecrows around or in the center of the farmland to monitor all directions or focus on a specific area. It is also possible to install multiple drone scarecrows to create overlapping monitoring areas. Step 2: The detection unit detects the animal. The detection unit can, for example, monitor the animal's movements in real time using cameras or sensors and detect the animal using image recognition technology. It can also analyze the animal's movements and detect its approach. Step 3: The deterrent unit deters animals detected by the detection unit using high-frequency sounds or light. For example, when an animal approaches farmland, the deterrent unit can emit high-frequency sounds or emit bright light. It can also deter animals by combining high-frequency sounds and light. Step 4: The tracking unit automatically tracks the animal that has been threatened by the intimidation unit. The tracking unit can, for example, track the animal while maintaining a constant distance until it leaves the farmland, and can adjust the tracking path in real time. It can also track in cooperation with other drones and sensors.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the installation unit, detection unit, intimidation unit, tracking unit, and notification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the smart device 14 and installs the drone scarecrow in the farmland. The detection unit monitors the movement of animals in real time using, for example, the camera 42 and sensors of the smart device 14. The intimidation unit intimidates animals by generating high-frequency sound or light using, for example, the control unit 46A of the smart device 14. The tracking unit automatically tracks animals using, for example, the control unit 46A of the smart device 14. The notification unit notifies the administrator of the approach of animals using, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the installation unit, detection unit, deterrent unit, tracking unit, and notification unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the smart glasses 214 and installs the drone scarecrow in the farmland. The detection unit monitors the movement of animals in real time using, for example, the camera 42 and sensors of the smart glasses 214. The deterrent unit deters animals by generating high-frequency sound or light using, for example, the control unit 46A of the smart glasses 214. The tracking unit automatically tracks animals using, for example, the control unit 46A of the smart glasses 214. The notification unit notifies the administrator of the approach of animals using, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the installation unit, detection unit, intimidation unit, tracking unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the headset terminal 314 and installs the drone scarecrow in the farmland. The detection unit monitors the movement of animals in real time using, for example, the camera 42 and sensors of the headset terminal 314. The intimidation unit intimidates animals by generating high-frequency sound or light using, for example, the control unit 46A of the headset terminal 314. The tracking unit automatically tracks animals using, for example, the control unit 46A of the headset terminal 314. The notification unit instructs the administrator of the approach of animals using, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the installation unit, detection unit, intimidation unit, tracking unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the robot 414 and installs the drone scarecrow in the farmland. The detection unit monitors the movement of animals in real time using, for example, the camera 42 and sensors of the robot 414. The intimidation unit intimidates animals by generating high-frequency sound or light using, for example, the control unit 46A of the robot 414. The tracking unit automatically tracks animals using, for example, the control unit 46A of the robot 414. The notification unit notifies the administrator of the approach of an animal using, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The installation section for setting up drone scarecrows in farmland, A detection unit that detects animals, A deterrent unit that deters animals detected by the aforementioned detection unit using high-frequency waves or light, A tracking unit that automatically tracks an animal that has been threatened by the aforementioned intimidation unit, Equipped with A system characterized by the following features. (Note 2) The detection unit, Detecting animals using image recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned intimidation part is, When animals approach farmland, they emit high-frequency sounds. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned intimidation part is, When animals approach farmland, a bright light is shone on them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned tracking unit is, Track the animal while maintaining a constant distance until it leaves the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 6) It is equipped with a notification unit that alerts the administrator to the approach of an animal. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned mounting section is The system estimates the user's emotions and adjusts the placement of the drone scarecrow based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned mounting section is The system analyzes the topography and environmental conditions of farmland and automatically selects the optimal installation location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned mounting section is During installation, the system detects surrounding obstacles and sets the optimal flight path. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned mounting section is It estimates the user's emotions and adjusts the timing of installation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned mounting section is When installing, adjust the installation location considering the growth stage of the crops in the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned mounting section is During installation, the system analyzes ambient weather conditions in real time to determine the optimal installation timing. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit, It estimates the user's emotions and adjusts the detection sensitivity based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit, When detecting an animal, different detection algorithms are applied depending on the type and size of the animal. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit, When detection occurs, the system takes into account changes in ambient sound and light to prevent false detections. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit, It estimates the user's emotions and adjusts the notification method of the detection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit, When detection occurs, the detection range is adjusted considering the geographical conditions of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit, When detection occurs, it works in conjunction with other drones and sensors to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned intimidation part is, It estimates the user's emotions and adjusts the intensity of the intimidation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned intimidation part is, When intimidating an animal, different intimidation methods are applied depending on the type of animal and its behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned intimidation part is, When intimidating, the most appropriate intimidation method is selected considering the surrounding environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned intimidation part is, It estimates the user's emotions and adjusts the timing of intimidation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned intimidation part is, When intimidating, the intimidation methods are adjusted considering the type and growth stage of the crops in the field. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned intimidation part is, During intimidation, it can work in conjunction with other drones and sensors to enhance the intimidation effect. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is, It estimates the user's emotions and adjusts the tracking distance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is, During tracking, the system analyzes the animal's behavior patterns to determine the optimal tracking path. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is, During tracking, the system detects surrounding obstacles and corrects the tracking path in real time. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is, It estimates the user's emotions and adjusts the tracking speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tracking unit is, During tracking, the tracking path is adjusted considering the terrain and environmental conditions of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tracking unit is, During tracking, it works in conjunction with other drones and sensors to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, different notification methods will be applied depending on the type of animal and its behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the most suitable notification method will be selected, taking into account the condition of the farmland and the manager's schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The installation section for setting up drone scarecrows in farmland, A detection unit that detects animals, A deterrent unit that deters animals detected by the aforementioned detection unit using high-frequency waves or light, A tracking unit that automatically tracks an animal that has been threatened by the aforementioned intimidation unit, Equipped with A system characterized by the following features.

2. The detection unit, Detecting animals using image recognition technology The system according to feature 1.

3. The aforementioned intimidation part is, When animals approach farmland, they emit high-frequency sounds. The system according to feature 1.

4. The aforementioned intimidation part is, When animals approach farmland, a bright light is shone on them. The system according to feature 1.

5. The aforementioned tracking unit is, Track the animal while maintaining a constant distance until it leaves the farmland. The system according to feature 1.

6. It is equipped with a notification unit that alerts the administrator to the approach of an animal. The system according to feature 1.

7. The aforementioned mounting section is The system estimates the user's emotions and adjusts the placement of the drone scarecrow based on those emotions. The system according to feature 1.

8. The aforementioned mounting section is The system analyzes the topography and environmental conditions of farmland and automatically selects the optimal installation location. The system according to feature 1.

9. The aforementioned mounting section is During installation, the system detects surrounding obstacles and sets the optimal flight path. The system according to feature 1.

10. The aforementioned mounting section is It estimates the user's emotions and adjusts the timing of installation based on those estimated emotions. The system according to feature 1.

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