System

Abstract

A system is provided.SOLUTION: A system comprising: image recognition means for acquiring image data using a sensor or a camera and analyzing the image data; position information acquisition means for acquiring position information of a target identified by the image recognition means; sound generation means for generating sound for moving away the target identified by the image recognition means; autonomous robot means for automatically moving and tracking and exterminating the target based on the position information of the target; and data analysis means for analyzing biological information of the target and identifying a behavior pattern.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the agricultural field, damage to crops caused by pests in specific areas has become a serious problem. Conventional extermination methods are often inefficient and have a significant impact on the environment. Misidentification and an inability to respond quickly are particularly problematic. Furthermore, it is difficult to understand the behavioral patterns of pests, making it difficult to develop effective extermination measures. The present invention aims to solve these problems and provide an efficient and sustainable method for exterminating pests. [Means for solving the problem]

[0005] The present invention provides an image recognition means that acquires image data using various sensors or cameras and analyzes the image data. This allows for the identification of specific pests with high accuracy, and a location information acquisition means is provided as a means for acquiring their location information. Furthermore, a sound generation means is provided that generates sounds to repel identified pests, and a sound recognition means is provided that detects and analyzes the sounds of pests using a highly sensitive microphone, thereby achieving the detection and repelling of pests. Furthermore, an autonomous robot means is provided that automatically moves, tracks, and exterminates pests, enabling rapid and accurate extermination. Finally, a data analysis means is provided that analyzes the ecological information of pests and identifies their behavioral patterns, providing information for formulating effective extermination and prevention measures. By combining these means, an efficient and sustainable pest extermination system is realized.

[0006] A "sensor or camera" is a device installed to detect the movement of pests and is a device that acquires image data and audio data.

[0007] "Image recognition means" refers to software or hardware that includes techniques and algorithms for analyzing acquired image data and identifying specific pests.

[0008] The "location information acquisition means" refers to a technology or device that acquires the location of the pest identified by the image recognition means as latitude and longitude information.

[0009] "Sound generating means" refers to a system or device for generating sounds or sounds to repel pests.

[0010] "Autonomous robotic means" means a robot that has the ability to move automatically based on the location information of designated pests and to track and exterminate the pests.

[0011] "Data analysis means" refers to systems or devices that include technologies and algorithms for collecting and analyzing behavioral patterns and ecological information of pests.

[0012] "Voice recognition means" refers to technology or equipment that uses a highly sensitive microphone to capture surrounding sounds and identify and analyze the sounds of specific pests from among those sounds.

[0013] A GPS tracker is a device that is attached to pests to track and transmit their location information in real time. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system for efficient and sustainable pest control that combines image recognition, voice recognition, autonomous robots, and data analytics to consistently detect, track, and eliminate pests.

[0036] System Configuration

[0037] The system consists of the following main components:

[0038] 1. Sensor or camera

[0039] Multiple sensors or cameras are installed in agricultural fields or protected areas of crops to capture real-time video and audio data of the environment.

[0040] 2. Image Recognition Methods

[0041] It includes algorithms for analyzing image data obtained from sensors or cameras and identifying pests.

[0042] 3. Location information acquisition means

[0043] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[0044] 4. Voice Generation Method

[0045] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[0046] 5. Autonomous Robotic Means

[0047] It has the ability to receive location information from the server and track and exterminate pests.

[0048] 6. Data Analysis Methods

[0049] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[0050] Program processing explanation

[0051] Detecting pests using image recognition AI

[0052] Server: Receives video data sent from sensors or cameras. Uses image recognition AI to detect specific pests in the video data. For example, if surveillance cameras are installed around a field and a wild boar appears in the video, it will be identified and its location will be generated.

[0053] Server: Records the location information of identified pests and displays it on the management screen, allowing for monitoring of their entry into crop protection areas and immediate response.

[0054] Keeping pests away with AI-powered audio alerts

[0055] Device: Monitors surrounding sounds with a highly sensitive microphone and captures audio data in real time.

[0056] Server: Using voice recognition AI, identifies the sounds of pests from the acquired audio data. If a specific pest sound is recognized, it generates its location information and sends a command to the device to generate an alarm sound.

[0057] Device: Plays a specified alarm sound to scare pests away from the extermination area. For example, a specific frequency of sound can be emitted to scare pests away from the area.

[0058] Autonomous robots track and eradicate pests

[0059] Server: Sends the location information of detected pests to the autonomous robot.

[0060] Autonomous robot: Based on location information, it moves in real time and tracks pests. It continuously identifies the location of pests using sensors and cameras, and continues to track them until extermination is complete.

[0061] Autonomous robots: They capture and exterminate pests, for example by using a net to capture them while tracking them or by using other extermination methods, and then send a completion report to a server after the extermination is complete.

[0062] Understanding the ecology of pests through data analysis

[0063] Server: Collects data on pest behavior and analyzes it using data mining AI. This allows the behavioral patterns and movement routes of pests to be identified and reflected in extermination plans.

[0064] Server: Captured pests are fitted with GPS trackers to track their location in real time, allowing for analysis of their movement patterns and the development of more effective extermination methods.

[0065] Specific examples

[0066] Example of pest detection using image recognition AI

[0067] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[0068] An example of keeping pests away using voice alert AI

[0069] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[0070] Server: "Send alarm command to device."

[0071] Terminal: "Play specified alarm sound. Keeps pests away."

[0072] An example of tracking and exterminating pests using an autonomous robot

[0073] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[0074] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[0075] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[0076] Example of understanding behavioral patterns through data analysis

[0077] Server: "We are currently analyzing extermination data using data mining AI. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[0078] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[0079] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[0080] In this way, a system is provided in which each element works together to efficiently exterminate harmful animals.

[0081] The processing flow will be explained below.

[0082] Program processing flow

[0083] Detecting pests using image recognition AI

[0084] Step 1:

[0085] Terminal: "Acquire video data from surveillance cameras in real time."

[0086] Step 2:

[0087] Terminal: "Send the acquired video data to the server at regular intervals."

[0088] Step 3:

[0089] Server: "Input the received video data into the image recognition AI."

[0090] Step 4:

[0091] Server: "Image recognition AI analyzes video data to identify pests. For example, it can identify the shapes of wild boars and deer."

[0092] Step 5:

[0093] Server: "Generate location information (latitude, longitude) for identified pests."

[0094] Step 6:

[0095] Server: "Record the generated location information in the database and display it on the management screen."

[0096] Keeping pests away with AI-powered audio alerts

[0097] Step 1:

[0098] Device: "High-sensitivity microphone constantly monitors environmental sounds."

[0099] Step 2:

[0100] Terminal: "Send the acquired audio data to the server at regular intervals."

[0101] Step 3:

[0102] Server: "Input the received voice data into the voice recognition AI."

[0103] Step 4:

[0104] Server: "The voice recognition AI analyzes the audio data to identify the sounds of pests. For example, it can identify the sounds of wild boars."

[0105] Step 5:

[0106] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[0107] Step 6:

[0108] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[0109] Autonomous robots track and eradicate pests

[0110] Step 1:

[0111] Server: "Send the location of detected pests to the autonomous robot."

[0112] Step 2:

[0113] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[0114] Step 3:

[0115] Autonomous robots: "Once they arrive, they monitor the surrounding area using sensors and cameras to locate pests."

[0116] Step 4:

[0117] Autonomous robots: "Prepare means (nets, tranquilizer guns, etc.) to track and capture pests."

[0118] Step 5:

[0119] Autonomous robots: "Capture and reliably eliminate pests."

[0120] Step 6:

[0121] Autonomous robot: "Reports to the server that disinfection is complete."

[0122] Understanding the ecology of pests through data analysis

[0123] Step 1:

[0124] Server: "Stores pest and extermination data collected from sensors and cameras in a database."

[0125] Step 2:

[0126] Server: "The collected data is fed into a data mining AI."

[0127] Step 3:

[0128] Server: "Data mining AI will analyze pest behavior patterns and preferred environments."

[0129] Step 4:

[0130] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[0131] Step 5:

[0132] User: "I'll put a GPS tracker on the captured vermin."

[0133] Step 6:

[0134] GPS tracker: "Sends real-time location information of pests to a server."

[0135] Step 7:

[0136] Server: "Analyze GPS data to understand pest movement patterns."

[0137] Step 8:

[0138] Server: "Plan the next cleanup and notify the user."

[0139] The above is a specific processing flow for carrying out the present invention, which makes it possible to provide a system that realizes efficient and sustainable pest control.

[0140] Example 1

[0141] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Pests cause serious damage to agricultural crops, and traditional extermination methods are difficult to implement efficiently and sustainably. Furthermore, there are insufficient means to monitor pest intrusions in real time and respond quickly, which means the damage tends to spread. To solve these problems, a system is needed that can detect, track, and exterminate pests in an integrated manner, while analyzing their behavioral patterns to prevent future intrusions.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0144] In this invention, the server includes image recognition means for acquiring image data using a sensor or camera and analyzing the image data, location information acquisition means for acquiring location information of a target identified by the image recognition means, sound generation means for generating sound to repel the target identified by the image recognition means, autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, data analysis means for analyzing the target's biological information and identifying its behavioral pattern, communication means for transmitting the acquired video and audio data to the server in real time, and command generation means for generating an alarm sound to be played and transmitting it to a terminal. This makes it possible to perform a consistent process from detecting, tracking, and exterminating pests to analyzing their behavior.

[0145] "Sensor or camera" means a device for acquiring still or video images of the environment and capable of collecting data in real time.

[0146] "Image recognition means" includes algorithms and technologies for analyzing acquired image data and identifying and specifying specific objects (e.g., pests) from within the data.

[0147] The "location information acquisition means" is a means for measuring the location of an object identified by the image recognition means and generating and acquiring that information.

[0148] "Sound generation means" refers to a device or technology that generates warning sounds or the like based on identified sound data in order to keep a specific target away.

[0149] "Autonomous robot means" refers to a type of robot that has the ability to move automatically based on location information sent from a server and track and eliminate specific targets.

[0150] "Data analysis means" refers to systems or technologies that collect and analyze behavioral data of subjects to extract specific patterns and information.

[0151] "Communication means" refers to technology or equipment for transmitting acquired video and audio data to a server in real time, and has the ability to exchange data via a network.

[0152] The "command generation means" is a mechanism that generates commands to instruct the terminal to execute an alarm or other processing based on the data analyzed by the server.

[0153] The present invention relates to a system for efficiently and sustainably eradicating pests in protected areas of agricultural crops, which is composed of a combination of sensors, cameras, image recognition means, autonomous robots, data analysis means, communication means, and sound generation means.

[0154] Hardware and software used

[0155] The specific hardware used in this system includes high-resolution cameras, high-sensitivity microphones, cloud servers, autonomous robots, and GPS trackers. Image recognition is performed using image recognition AI algorithms such as YOLOv5. Voice recognition is performed using deep learning-based voice recognition AI.

[0156] System Operation Overview

[0157] 1. Sensor or camera placement and data acquisition

[0158] The sensors or cameras are installed in agricultural fields and protected areas of agricultural crops to capture real-time video and audio data of the surrounding area. In particular, all-weather, high-resolution cameras and highly sensitive microphones are used to monitor pests that target crops.

[0159] 2. Analysis of video data

[0160] The acquired video data is sent to a server. The server receives the video data in real time and uses image recognition techniques (such as YOLOv5) to detect pests. Once a detected pest is identified, its location information is automatically generated and displayed on the management screen.

[0161] 3. Analysis of audio data and generation of warning sounds

[0162] The device uses a highly sensitive microphone to collect surrounding sounds and transmits them to a server in real time. The server then uses a voice recognition tool to analyze the audio data and detect the sounds of specific pests. Based on the detected sounds, a command to generate an alarm sound is sent to the device and played back. This frightens pests away from the extermination area.

[0163] 4. Autonomous robots for tracking and eliminating pests

[0164] The server sends the location information of the detected pests to the autonomous robot. The autonomous robot moves in real time based on the received location information and tracks the pests. During tracking, it continuously identifies the location of the pests using sensors and cameras, and carries out extermination using nets, etc. Once extermination is complete, a completion report is sent to the server.

[0165] 5. Data analysis and behavioral pattern understanding

[0166] The server collects data on the behavior of pests and analyzes it using data analysis tools. This allows the behavioral patterns and movement routes of pests to be identified and reflected in the next extermination plan. In addition, GPS trackers are attached to captured pests to track their location in real time. This allows for the development of even more effective extermination methods.

[0167] Specific examples

[0168] Example of pest detection using image recognition AI

[0169] Server: "Receiving video data from camera 1. Identified a wild boar. Generate location information: latitude 35.1234, longitude 137.5678, and display it on the management screen."

[0170] An example of keeping pests away using voice alert AI

[0171] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[0172] Server: "Send a command to generate an alarm to the device."

[0173] Terminal: "Play specified alarm sound. Keeps pests away."

[0174] An example of tracking and exterminating pests using an autonomous robot

[0175] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[0176] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[0177] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[0178] Example of understanding behavioral patterns through data analysis

[0179] Server: "We are currently analyzing the extermination data using data analysis tools. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[0180] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[0181] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[0182] Prompt Sentence Examples

[0183] "One night in October, an IoT sensor detected the movement of a wild boar in a protected area for agricultural crops. Image recognition AI analyzed the footage to identify the boar and generate location information. An autonomous robot then used the location information to track the boar and capture it with a net. Please describe this entire process."

[0184] The system of the present invention provides a comprehensive solution for efficient and sustainable pest control, allowing users to monitor and control in real time through an administrative interface.

[0185] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0186] Step 1:

[0187] Initial setup and sensor preparation

[0188] Server: Checks the setting information of sensors and cameras, and verifies that each device is working properly. It distributes IP addresses and setting information to each device to ensure communication is established.

[0189] Input: Sensor and camera settings, device ID

[0190] Output: Device operation check result, device setting completion notification

[0191] Specific operation: The server sends the configuration information to each sensor, and once normal operation is confirmed, it receives a "configuration complete" notification.

[0192] Terminal: Check the installation location of sensors and cameras and place them appropriately. The installation locations should be set in agricultural land and crop protection areas.

[0193] Input: Location information, physical layout of sensors and cameras

[0194] Output: Placement completion notification

[0195] What it does: The device physically places cameras and sensors in farmland or protected areas and fixes them at appropriate angles.

[0196] Users: Monitor the operation of sensors and cameras through the admin panel and check for any issues.

[0197] Input: Sensor / camera status information on the management screen

[0198] Output: Results of normal operation of each device

[0199] What happens: The user checks the admin panel and confirms that all devices are working properly.

[0200] Step 2:

[0201] Acquisition and transmission of video data

[0202] Sensors / cameras: Capture real-time video data from the area where they are installed, with a particular focus on pest monitoring.

[0203] Input: Real-time video and audio from the scene

[0204] Output: Get raw data

[0205] Specific operation: Sensors and cameras continuously record and acquire video and audio within a specified range.

[0206] Terminal: Compresses the captured video data and sends it to the server, improving data transfer efficiency.

[0207] Input: Raw data (video and audio)

[0208] Output: Compressed data, notification of completion

[0209] Specific operation: The device compresses the video and audio data and uploads it to the server.

[0210] Step 3:

[0211] Image recognition for detecting pests

[0212] Server: Analyzes the received video data and runs image recognition AI to detect specific pests (e.g., wild boars).

[0213] Input: Compressed video data

[0214] Output: Identification and location information of detected pests

[0215] Specific operation: The server analyzes the video data, detects pests using YOLOv5, and generates location information.

[0216] Server: Generates location information of pests and displays it on the management screen.

[0217] Input: Detected pest's identity and location

[0218] Output: Location information generated and displayed on the management screen

[0219] Specific operation: The server displays the location information of the pests on the management screen, allowing the user to check it in real time.

[0220] Step 4:

[0221] Acquisition and analysis of audio data

[0222] Device: A highly sensitive microphone captures surrounding audio data and transmits it to the server in real time.

[0223] Input: Real-time audio from the site

[0224] Output: Acquired audio data, transmission completion notification

[0225] Specific operation: The device picks up sound with a high-sensitivity microphone and sends the data to the server.

[0226] Server: Analyzes the captured audio data and uses voice recognition AI to identify the sounds of pests.

[0227] Input: Captured audio data

[0228] Output: Enemy call identification result, location information

[0229] Specific operation: The server analyzes the audio data and uses deep learning to identify the sounds of pests and generate location information.

[0230] Step 5:

[0231] Audio warning and pest repellent

[0232] Server: Based on the detected sounds, sends a command to the device to generate an alarm sound.

[0233] Input: Call identification result, location information

[0234] Output: Command to generate warning sound

[0235] Specific operation: The server generates an alarm command and sends it to the device.

[0236] Terminal: Receives an alert sound generation command from the server and plays the specified alert sound.

[0237] Input: Command to generate warning sound

[0238] Output: Warning sound played, distancing result

[0239] Specific action: The device plays an alarm sound to scare away pests.

[0240] Step 6:

[0241] Autonomous robots track and eradicate pests

[0242] Server: Sends the location information of detected pests to the autonomous robot.

[0243] Input: Location of detected pest

[0244] Output: Location information sending completion notification

[0245] Specific operation: The server sends location information to the autonomous robot.

[0246] Autonomous robot: Moves in real time based on received location information and tracks pests.

[0247] Input: Location

[0248] Output: Pest capture results, extermination report

[0249] Specific operation: The autonomous robot moves based on location information, tracks and captures pests.

[0250] Autonomous robot: Reports to the server once disinfection is complete.

[0251] Input: Disinfection completion information

[0252] Output: Report of disinfection completion to the server

[0253] Specific operation: The autonomous robot sends a report of the completion of eradication to the server.

[0254] Step 7:

[0255] Data analysis and understanding of behavioral patterns

[0256] Server: Collects behavioral data of pests and analyzes it using data analysis tools.

[0257] Input: Behavioral data obtained during removal

[0258] Output: Identification of behavioral patterns

[0259] Specific operation: The server analyzes the behavioral data and identifies behavioral patterns and movement routes.

[0260] Server: GPS trackers are attached to captured pests to track their location in real time.

[0261] Input: Captured pest information, GPS tracker

[0262] Output: GPS data, behavioral patterns

[0263] What it does: The server analyzes the GPS data and develops an effective removal method.

[0264] With detailed processing at each step, the system can detect, track, exterminate and analyze pests in an integrated manner.

[0265] (Application example 1)

[0266] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0267] Conventional pest control systems did not consistently detect, track, and exterminate pests, making it difficult to respond in a timely manner. Furthermore, when a pest invades a factory, it is difficult to understand its entry route and behavioral patterns, making it impossible to develop an efficient extermination method. Another issue is that there is no interface that allows users to check the current location of pests in real time, making it difficult to respond quickly.

[0268] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0269] In this invention, the server includes an image recognition means that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition means that acquires location information of an object identified by the image recognition means, a sound generation means that generates sound to repel the object identified by the image recognition means, an autonomous robot means that automatically moves to track and exterminate the object based on the location information of the object, a data analysis means that analyzes the object's biological information and identifies its behavioral pattern, and a user interface means that provides an interface that allows a user to check the video data acquired from the sensor or camera and the sound data detected by the sound recognition means in real time. This makes it possible to perform an integrated process from detection to extermination of pests, and to understand the entry routes and behavioral patterns of pests within a factory, thereby enabling quick and efficient extermination.

[0270] A "sensor" is a device that detects changes in the physical environment and transmits them as a signal.

[0271] A "camera" is a device that captures video or images and stores and transmits them as digital data.

[0272] "Image recognition means" refers to a device that includes an algorithm or program for analyzing image data obtained from a sensor or camera and identifying a specific object.

[0273] The "location information acquisition means" is a device that identifies the location of the object identified by the image recognition means, and acquires and transmits the location information.

[0274] The "audio generating means" is a device that generates audio based on preset conditions and plays it back.

[0275] An "autonomous robotic means" is a robot that has the ability to move automatically based on location information and track and eliminate identified targets.

[0276] "Data analysis means" is a system for analyzing collected data and identifying the behavioral patterns and ecological information of the subject.

[0277] The "user interface means" is a device that provides an interface that allows a user to check the video data and audio data obtained from the sensor or camera in real time.

[0278] "Speech recognition means" is a device that includes an algorithm or program for analyzing voice data acquired by a high-sensitivity microphone and identifying the voice of a specific target.

[0279] A "GPS tracker" is a device that obtains and tracks location information in real time.

[0280] This invention is a system for efficiently exterminating pests in factories. This system is realized by combining hardware such as sensors, cameras, high-sensitivity microphones, and autonomous robots with software such as image recognition AI, voice recognition AI, and data analysis AI.

[0281] Hardware Configuration

[0282] Sensors or cameras: Installed in various locations within the factory, they capture video and audio data in real time.

[0283] High-sensitivity microphone: Collects sounds within the factory and detects the cries of pests.

[0284] Autonomous robots: They move automatically based on the location of pests to track and exterminate them.

[0285] GPS tracker: Attached to pests captured by autonomous robots, it tracks their location in real time.

[0286] Software Configuration

[0287] Image recognition AI (e.g., TensorFlow): Analyzes video data obtained from sensors or cameras to detect pests.

[0288] Speech recognition AI (e.g., Google Speech Recognition): Analyzes audio data collected by a highly sensitive microphone and identifies the sounds of pests.

[0289] Data analysis AI (e.g., SciKit-Learn): Analyzes behavioral patterns and ecological information of pests and reflects this in extermination plans.

[0290] User interface: Provides an interface that allows users to view video and audio data in real time via smartphones, tablets, or smart glasses.

[0291] Specific operation of the system

[0292] The server receives video data from sensors or cameras and uses image recognition AI to detect pests. For example, if a camera installed in a factory captures a wild boar in the video, it analyzes the data and generates location information. This location information is sent in real time to an autonomous robot, which then begins tracking the pest.

[0293] The server also uses voice recognition AI to analyze audio data obtained from a highly sensitive microphone, and if it detects the cries of a pest, it obtains its location information. Based on this location information, a sound generator generates an alarm sound to keep the pest away.

[0294] Using the provided user interface, users can grasp the current situation in real time and take necessary measures. For example, they can check the footage from the surveillance cameras in the factory on their smartphones and take immediate action if a pest is detected.

[0295] Specific examples

[0296] Pest detection: "The model analyzed the video data from the camera and detected a wild boar. Its location is latitude 35.1234, longitude 137.5678. An instruction has been sent to the robot."

[0297] Roar detection: "A highly sensitive microphone has detected the sound of a wild boar roaring. An alarm sound will be played to keep the boar away from the factory."

[0298] User notification: "We have reviewed the footage from the surveillance camera and detected a pest. The robot will now move to the specified location and begin extermination."

[0299] These functions enable the rapid detection of pest intrusions and their efficient extermination, making this a very useful system for factories where real-time confirmation and rapid response are required.

[0300] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0301] Step 1:

[0302] The server acquires video data in real time from sensors or cameras installed within the factory.

[0303] Input: Video data from a sensor or camera

[0304] Output: Video data passed to image recognition AI

[0305] How it works: The cameras continuously capture video at various locations in the factory and send the data to a server.

[0306] Step 2:

[0307] The server inputs the acquired video data into image recognition AI and performs analysis to detect pests.

[0308] Input: Video data acquired in the previous step

[0309] Output: Pest identification and location

[0310] How it works: Image recognition AI detects features such as people and vehicles in video data and determines whether it contains any vermin. For example, if it detects a video containing a wild boar, it generates its location information.

[0311] Step 3:

[0312] The server obtains the location information of pests detected by image recognition AI and transmits it to the autonomous robot.

[0313] Input: Pest identification and location

[0314] Output: Movement instructions for the autonomous robot

[0315] Specific operation: Based on the obtained location information, the server issues instructions to the autonomous robot, causing it to move in order to capture the pest.

[0316] Step 4:

[0317] The autonomous robots move automatically based on the transmitted location information to track and eliminate pests.

[0318] Input: Location information and movement instructions from the server

[0319] Output: Report of pest capture and extermination completion

[0320] Specific behavior: The robot moves towards the designated location, continuously locates the pest using pest tracking sensors and cameras, and then performs extermination measures, such as capturing the pest with a net or using appropriate extermination methods.

[0321] Step 5:

[0322] The server collects real-time audio data from high-sensitivity microphones installed within the factory.

[0323] Input: Audio data from a high-sensitivity microphone

[0324] Output: Voice data passed to the voice recognition AI

[0325] How it works: Highly sensitive microphones collect audio from various locations within the factory and send the data to a server.

[0326] Step 6:

[0327] The server uses voice recognition AI to analyze the acquired audio data and detect the cries of specific pests.

[0328] Input: Audio data obtained in the previous step

[0329] Output: Sound detection results and location information of pests

[0330] How it works: The voice recognition AI analyzes unique sound patterns contained in the audio data to detect the calls of specific pests (such as wild boars). If detected, it generates location information.

[0331] Step 7:

[0332] The server generates and reproduces an alarm sound for keeping away harmful animals from the sound generating means based on the location information of the harmful animal's cry.

[0333] Input: Call detection results and location information of pests

[0334] Output: Generates and plays warning sounds

[0335] Specific behavior: The server generates a specified alarm sound and uses surrounding speakers to play sounds of frequencies that pests dislike, thereby keeping pests away.

[0336] Step 8:

[0337] The user uses the provided user interface to check the video data and audio data in real time.

[0338] Input: Real-time data (video and audio) from the server

[0339] Output: what is displayed in the user interface

[0340] Specific operation: Users operate the interface via smartphones, tablets, or smart glasses to monitor the situation in the factory in real time and take appropriate action if necessary.

[0341] Step 9:

[0342] The server uses data analysis AI to analyze the behavioral patterns and ecological information of pests and reflect this in extermination plans.

[0343] Input: Pest location and behavior data

[0344] Output: Behavioral pattern analysis results and eradication plan

[0345] How it works: The data analysis AI uses the collected data to identify the behavioral patterns and ecological information of pests, and then plans more effective extermination methods. The results are notified to the user and incorporated into the next extermination plan.

[0346] These steps enable consistent and efficient detection and extermination of pests, enabling real-time monitoring and rapid response.

[0347] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0348] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and the emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[0349] System Configuration

[0350] The system consists of the following main components:

[0351] 1. Sensor or camera

[0352] Multiple sensors or cameras installed on-site capture video and audio data of the environment in real time.

[0353] 2. Image Recognition Methods

[0354] It includes algorithms for analyzing acquired image data and identifying pests.

[0355] 3. Location information acquisition means

[0356] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[0357] 4. Voice Generation Method

[0358] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[0359] 5. Autonomous Robotic Means

[0360] It has the ability to receive location information from the server and track and exterminate pests.

[0361] 6. Data Analysis Methods

[0362] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[0363] 7. Emotion Engine

[0364] It includes technology for recognizing user emotions and adjusting system behavior.

[0365] Program processing explanation

[0366] Detecting pests using image recognition AI

[0367] Terminal: "Acquire video data from the surveillance camera in real time. Send the video data to the server at regular intervals."

[0368] Server: "The received video data is input into image recognition AI to identify harmful animals. For example, wild boars and deer."

[0369] Server: "Generate location information for identified pests, record it in a database, and display it on the management screen."

[0370] Keeping pests away with AI-powered audio alerts

[0371] Device: "Constantly monitors environmental sounds with a high-sensitivity microphone. The acquired audio data is sent to the server at regular intervals."

[0372] Server: "The received audio data is input into the speech recognition AI to identify the sound of a pest. For example, it identifies the sound of a wild boar."

[0373] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[0374] Device: "Plays an alarm sound of the specified frequency and volume to ward off pests."

[0375] Autonomous robots track and eradicate pests

[0376] Server: "Send the location of detected pests to the autonomous robot."

[0377] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[0378] Autonomous robots: "Once they arrive, they monitor the surroundings with sensors and cameras to identify pests. They then begin tracking and exterminating them."

[0379] Autonomous robot: "Captures pests and exterminates them reliably. Once extermination is complete, it sends a completion report to the server."

[0380] Understanding the ecology of pests through data analysis

[0381] Server: "Collects pest and extermination data collected from environmental sensors and cameras."

[0382] Server: "The collected data is fed into a data-mining AI to analyze the behavioral patterns and preferred environments of pests."

[0383] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[0384] User: "I'll put a GPS tracker on the captured vermin."

[0385] GPS Tracker: "Tracks the location of pests in real time and sends it to a server."

[0386] Server: "We analyze GPS data to identify pest movement patterns and plan the next eradication."

[0387] User support with emotion engine

[0388] Server: "Input the user's voice and video data into the emotion engine and analyze it."

[0389] Emotion Engine: "Recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety."

[0390] Server: "Automatically adjust system behavior and alert intensity and type based on the user's emotional state."

[0391] Specific examples

[0392] Example of pest detection using image recognition AI

[0393] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[0394] An example of keeping pests away using voice alert AI

[0395] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[0396] Server: "Send a command to generate an alarm to the device."

[0397] Terminal: "Play the specified alarm sound to keep pests away."

[0398] An example of tracking and exterminating pests using an autonomous robot

[0399] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[0400] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[0401] Autonomous robot: "Captures pests and traps them in a net. Extermination complete. Sends a completion report to the server."

[0402] Example of user support using emotion engine

[0403] Server: "Inputting user's audio and video data into the emotion engine."

[0404] Emotion engine: "Identify when the user is stressed. Adjust system behavior and reduce the intensity of alert sounds."

[0405] Server: "Notify user of response. Taking action based on emotional state."

[0406] This system not only enables efficient pest control, but also reduces the psychological burden on users.

[0407] The processing flow will be explained below.

[0408] Program processing flow

[0409] Detecting pests using image recognition AI

[0410] Step 1:

[0411] Terminal: "Activate the surveillance camera and capture video data in real time."

[0412] Step 2:

[0413] Terminal: "Sends acquired video data to the server at regular intervals."

[0414] Step 3:

[0415] Server: "Input the received video data into the image recognition AI."

[0416] Step 4:

[0417] Server: "Image recognition AI analyzes video data and identifies specific pests (e.g., wild boars and deer)."

[0418] Step 5:

[0419] Server: "Generate location information (latitude, longitude) for identified pests."

[0420] Step 6:

[0421] Server: "Record the generated location information in the database and display it on the management screen."

[0422] Keeping pests away with AI-powered audio alerts

[0423] Step 1:

[0424] Device: "Uses a highly sensitive microphone to constantly monitor the surrounding environment."

[0425] Step 2:

[0426] Device: "Send the acquired audio data to the server at regular intervals."

[0427] Step 3:

[0428] Server: "Input the received voice data into the voice recognition AI."

[0429] Step 4:

[0430] Server: "The voice recognition AI analyzes the audio data and identifies the sounds of specific pests (e.g., wild boars)."

[0431] Step 5:

[0432] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[0433] Step 6:

[0434] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[0435] Autonomous robots track and eradicate pests

[0436] Step 1:

[0437] Server: "Send the location of detected pests to the autonomous robot."

[0438] Step 2:

[0439] Autonomous robot: "Autonomously moves to the destination based on location information received from the server."

[0440] Step 3:

[0441] Autonomous robot: "After reaching its destination, it monitors its surroundings with sensors and cameras to reconfirm the location of the pest."

[0442] Step 4:

[0443] Autonomous robot: "Prepare means to capture pests (e.g., nets, tranquilizer guns)."

[0444] Step 5:

[0445] Autonomous robots: "Reliably capture and exterminate pests."

[0446] Step 6:

[0447] Autonomous robot: "Reports to the server that disinfection is complete."

[0448] Understanding the ecology of pests through data analysis

[0449] Step 1:

[0450] Server: "Stores pest and extermination data collected from environmental sensors and cameras in a database."

[0451] Step 2:

[0452] Server: "Feed the stored data into the data mining AI."

[0453] Step 3:

[0454] Server: "Data mining AI analyzes pest behavior patterns and migration routes."

[0455] Step 4:

[0456] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[0457] Step 5:

[0458] User: "We'll attach a GPS tracker to the captured pest."

[0459] Step 6:

[0460] GPS tracker: "Sends real-time location information of pests to a server."

[0461] Step 7:

[0462] Server: "Analyze GPS data to identify pest movement patterns."

[0463] Step 8:

[0464] Server: "Based on the identified movement patterns, create a plan for the next eradication and notify the user."

[0465] User support with emotion engine

[0466] Step 1:

[0467] Server: "Input the user's voice and video data into the emotion engine."

[0468] Step 2:

[0469] Emotion engine: "Analyzes audio and video data to identify the user's emotional state. For example, identifying stress or anxiety."

[0470] Step 3:

[0471] Server: "Adjust the system's behavior based on the emotion engine's analysis. For example, change the intensity or type of alarm sound."

[0472] Step 4:

[0473] Server: "Propose countermeasures based on the user's emotional state and notify the user when the countermeasures are implemented."

[0474] The above is a specific processing flow for carrying out the present invention, which not only enables efficient pest control but also reduces the psychological burden on the user.

[0475] Example 2

[0476] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0477] Conventional pest control systems have difficulty detecting and eliminating pests efficiently and sustainably. Furthermore, they lack functionality to reduce the mental stress and anxiety users feel when using the system. A means to accurately grasp the behavioral patterns of pests and efficiently eliminate them is needed, as well as functionality to provide psychological support to users.

[0478] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0479] In this invention, the server includes an image recognition means for acquiring image data using a sensor or a camera and analyzing the image data, a location information acquisition means for acquiring location information of a target identified by the image recognition means, a sound generation means for generating sound to repel the target identified by the image recognition means, an autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, a data analysis means for analyzing the target's ecological information and identifying its behavioral pattern, a sound recognition means for analyzing the target's sound and generating an alarm sound, and an emotion engine for recognizing the user's emotional state and adjusting the system's operation. This not only enables consistent and efficient detection, tracking, and extermination of pests, but also reduces the user's mental stress.

[0480] "Sensor or camera" means a device for acquiring real-time video and audio data of the environment.

[0481] "Image recognition means" refers to an algorithm or software that analyzes acquired image data and identifies specific objects or animals (e.g., vermin).

[0482] The "location information acquisition means" is a means for measuring the location information of the object identified by the image recognition means and providing it to the system.

[0483] The "sound generating means" is a device that generates sounds to repel targets, such as collecting environmental sounds using a highly sensitive microphone, and plays specific warning sounds.

[0484] "Autonomous robotic means" refers to a robot that moves automatically based on the location information of the target, and is equipped with an autonomous driving algorithm to track and exterminate the target.

[0485] "Data analysis methods" are methods for collecting ecological information about the subject and analyzing its behavioral patterns and preferred environments. Data mining and machine learning algorithms are used.

[0486] "Speech recognition means" is a means of analyzing voice data acquired by a high-sensitivity microphone and identifying specific sounds (for example, the cries of pests). It often uses a voice feature extraction algorithm.

[0487] The "Emotion Engine" is a technology that analyzes the user's voice and video data, recognizes the user's emotional state, and adjusts the system's behavior accordingly, suggesting actions to reduce stress and anxiety.

[0488] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and an emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[0489] The system consists of the following main components:

[0490] 1. Sensor or camera

[0491] Multiple sensors or cameras installed on-site capture real-time video and audio data of the environment. For example, infrared cameras and high-resolution cameras can be used.

[0492] 2. Image Recognition Methods

[0493] It includes algorithms for analyzing acquired image data and identifying pests. For example, it uses object detection models such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector). The server processes the image data sent from the device in real time and generates location information for identified pests.

[0494] 3. Location information acquisition means

[0495] The location information of the pests identified by the image recognition means is acquired and sent to a server. The location information is measured using GPS or a geographic information system (GIS).

[0496] 4. Voice Generation Method

[0497] It uses a highly sensitive microphone to pick up ambient sounds, and when it detects the sound of a pest, it generates a sound to scare the pest away, for example, playing the sound of a hunting dog or other warning sounds in a specific frequency band.

[0498] 5. Autonomous Robotic Means

[0499] The autonomous robot receives location information from a server and is capable of tracking and eliminating pests. It is equipped with self-driving technology and can reach its destination while avoiding obstacles. Once it arrives, it monitors its surroundings using cameras and sensors, and exterminates pests after identifying them. For example, it uses nets or traps.

[0500] 6. Data Analysis Methods

[0501] This is a system for collecting and analyzing the behavioral patterns and ecological information of pests. It uses data mining technology and machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the preferred environments and movement patterns of pests, which makes it possible to plan the next eradication.

[0502] 7. Emotion Engine

[0503] The user's voice and video data are input into the emotion engine for analysis. For example, technology analyzing voice tone and facial expressions is used to recognize the user's emotional state. Based on the analysis results, the system's operation and the intensity of warning sounds are automatically adjusted to reduce the user's mental stress.

[0504] Specific examples

[0505] Example of pest detection using image recognition AI

[0506] Terminal: Obtaining video data from a surveillance camera in real time. Sending the video data to the server at regular intervals.

[0507] Server: Receives video data from the camera and inputs it into image recognition AI (YOLO model). Identifies wild boars.

[0508] Server: Generates the location information of the identified wild boar (latitude 35.1234, longitude 137.5678) and displays it on the management screen.

[0509] An example of keeping pests away using voice alert AI

[0510] Device: High-sensitivity microphone picking up surrounding sounds. The sound of a wild boar has been identified.

[0511] Device: Send the determined location information (latitude 35.1234, longitude 137.5678) to the server.

[0512] Server: Based on the received data, the server sends a command to generate an alarm sound to the terminal.

[0513] Terminal: Plays the specified alarm sound to scare off wild boars.

[0514] An example of tracking and exterminating pests using an autonomous robot

[0515] Server: Send the boar's location information to the autonomous robot (latitude 35.1234, longitude 137.5678).

[0516] Autonomous robot: The robot moves automatically to the specified location.

[0517] Autonomous robot: After arriving, it uses sensors and cameras to identify wild boars and begins extermination. It captures them using a net.

[0518] Autonomous robot: After the removal is completed, it sends a completion report to the server.

[0519] Example of user support using emotion engine

[0520] Server: Inputting user's audio and video data into the emotion engine.

[0521] Emotion Engine: Identifies when a user is stressed and adjusts system behavior to reduce the intensity of alert sounds.

[0522] Server: Informs the user about system adjustments and takes actions based on their emotional state.

[0523] This system not only enables efficient and sustainable pest control, but also reduces the psychological burden on users.

[0524] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0525] Step 1:

[0526] Acquisition and transmission of image data

[0527] Terminal: Acquires real-time video data from surveillance cameras, such as infrared cameras and high-resolution cameras.

[0528] Input: Live video data of the environment.

[0529] Specific operation: The camera captures video of the environment and captures video frames at regular intervals (e.g., every second).

[0530] Terminal: The acquired video data is sent to the server at regular intervals.

[0531] Output: Video data sent to the server.

[0532] Step 2:

[0533] Vermin detection

[0534] Server: Inputs the received video data into the image recognition AI algorithm.

[0535] Input: Video data sent from the device.

[0536] What it does: The server uses an object detection model such as YOLO or SSD to identify objects in the frame.

[0537] Server: Identifies vermin (e.g., wild boars and deer) and generates their location information.

[0538] Output: Location of identified pests.

[0539] Step 3:

[0540] Sending location information of pests

[0541] Server: Sends location information to the autonomous robot using REST API or MQTT.

[0542] Input: The generated location information.

[0543] Specific operation: The server makes an API call or messaging to send the location information of the identified pest to the robot.

[0544] Output: Location information sent to the autonomous robot.

[0545] Step 4:

[0546] Autonomous robotic mobility and surveillance

[0547] Autonomous robot: Automatically moves to the destination based on the location information received from the server.

[0548] Input: Location information sent by the server.

[0549] Specific behavior: The robot uses an autonomous driving algorithm to reach a specified location while avoiding obstacles.

[0550] Autonomous robot: After arriving at its destination, it monitors the surrounding area with cameras and sensors to reconfirm the presence of pests.

[0551] Output: Confirmed pest presence information.

[0552] Step 5:

[0553] Performing and reporting removal

[0554] Autonomous robot: Performs specialized extermination actions towards identified pests.

[0555] Input: Confirmed pest presence information.

[0556] Specific behavior: The robot captures pests using nets and traps.

[0557] Autonomous robot: When the eradication is completed, it sends the eradication result data to the server as a completion report.

[0558] Output: Cleaning report sent to the server.

[0559] Step 6:

[0560] Acquisition and analysis of audio data

[0561] Device: High-sensitivity microphone constantly monitors surrounding sounds.

[0562] Input: Ambient sounds.

[0563] Specific operation: The microphone collects ambient sounds and stores the audio data in a buffer at regular intervals (for example, every second).

[0564] Device: Sends the saved audio data to the server.

[0565] Output: The audio data sent to the server.

[0566] Step 7:

[0567] Pest sound recognition and alarm sound generation

[0568] Server: Inputs the received voice data into the voice recognition AI.

[0569] Input: Audio data sent from the device.

[0570] Specific operation: The server uses an audio feature extraction algorithm to identify the sound of the pest.

[0571] Server: Based on the identified sounds of pests, it sends instructions to the device to generate alarm sounds to scare away the pests.

[0572] Output: Speech generation instructions.

[0573] Device: Plays an alarm sound at the specified frequency and volume according to the received instructions.

[0574] Output: An alarm sound is played.

[0575] Step 8:

[0576] Data analysis and eradication plan development

[0577] Server: Stores pest and extermination data collected from environmental sensors and cameras in a database.

[0578] Input: Data collected from sensors and cameras.

[0579] What it does: The server uses data mining techniques and machine learning algorithms to analyze the collected data.

[0580] Server: Analyzes pest behavior patterns and preferred environments.

[0581] Output: A removal plan based on the analysis results.

[0582] Step 9:

[0583] User support with emotion engine

[0584] Server: Inputs the user's voice and video data into the emotion engine.

[0585] Input: User audio and video data.

[0586] What it does: The emotion engine analyzes voice tone and facial expressions to identify the user's emotional state.

[0587] Emotion engine: Recognizes the user's emotional state based on the analysis results.

[0588] Output: The user's emotional state.

[0589] Server: Adjusts system behavior and alert sound intensity based on the user's emotional state.

[0590] Output: Adjusted system operation information.

[0591] Through this specific processing step, the system can maximize the effectiveness of pest control and reduce the psychological burden on the user.

[0592] (Application example 2)

[0593] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0594] Conventional pest control systems can detect and track pests, but they are unable to adjust their operation in response to the user's emotional state. This often leads to anxiety and stress during the pest control process. It is also difficult to utilize these systems for security purposes, such as identifying suspicious individuals or dangerous creatures. The purpose of this invention is to solve these problems and provide a more effective and user-friendly system.

[0595] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0596] In this invention, the server includes an image recognition unit that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition unit that acquires location information of an object identified by the image recognition unit, a voice generation unit that generates a voice to repel the object identified by the image recognition unit, an autonomous robot unit that automatically moves to track and exterminate the object based on the location information of the object, a data analysis unit that analyzes the object's ecological information and identifies its behavioral pattern, and an emotion recognition unit that analyzes the user's emotional state and adjusts the system's operation. This enables efficient and sustainable pest extermination and reduces the user's psychological burden. The system can also identify suspicious individuals and dangerous creatures, improving facility safety.

[0597] A "sensor" or "camera" is a device for acquiring image data or audio data, and is used for environmental monitoring.

[0598] "Image recognition means" is a technology that analyzes image data acquired by sensors or cameras and identifies specific objects.

[0599] The "location information acquisition means" is a technology for acquiring location information of an object identified by the image recognition means and transmitting the information to a server.

[0600] The "audio generating means" is a technology that generates an audio signal to repel a specified target and issues a warning sound to the target.

[0601] "Autonomous robot means" refers to robot technology that automatically moves based on location information received from a server, and tracks and exterminates targets.

[0602] "Data analysis means" refers to technology for analyzing the ecological information of a subject and identifying behavioral patterns.

[0603] "Emotion recognition means" is a technology for analyzing the user's emotional state and adjusting the system's behavior.

[0604] The "position information acquisition device" is a device that is attached to an object captured by an autonomous robot means and that tracks the position information of the object.

[0605] "Speech recognition means" is a technology that uses a highly sensitive microphone to detect and analyze the target voice.

[0606] A "suspicious person" refers to a person who poses a security risk and is an object identified by the system.

[0607] "Dangerous creatures" refer to animals that may pose a threat to the safety of the facility or surrounding area, and are objects that are detected by the system.

[0608] The invention is a pest control and security enhancement system that consists of the following major components:

[0609] 1. Real-time image acquisition

[0610] The camera on the device (smartphone) is turned on and video data from the site is acquired in real time. The video data is then sent to a cloud server (AWS, Google Cloud, etc.) at regular intervals.

[0611] 2. Image Recognition

[0612] The server inputs the received video data into image recognition AI (TensorFlow, OpenCV, etc.) to identify suspicious individuals or dangerous creatures. Location information of suspicious individuals or dangerous creatures is generated, recorded in a database, and displayed on the management screen.

[0613] 3. Audio alert

[0614] The device constantly monitors environmental sounds using a highly sensitive microphone and transmits audio data to a server at regular intervals.

[0615] The server inputs the received voice data into a voice recognition AI (Google Speech-to-Text, DeepSpeech, etc.) to identify the voices of pests or suspicious people.

[0616] The server transmits an alert sound generation command to the terminal based on the identified location information.

[0617] The device will play the specified warning sound to repel the target.

[0618] 4. Autonomous Robots

[0619] The server transmits the location information of the detected target to an autonomous robot (such as a DJI drone).

[0620] The autonomous robot automatically moves to the destination based on the received location information and monitors the surroundings with sensors and cameras. After confirming the target, it begins tracking and extermination. After extermination is complete, it sends a completion report to the server.

[0621] 5. Data Analysis

[0622] The server collects target and extermination data from environmental sensors and cameras, and inputs it into a data mining AI to analyze behavioral patterns and preferred environments.

[0623] Based on the analysis results, effective eradication and prevention measures are developed.

[0624] The user attaches a GPS tracker to the captured target, and the server tracks the target's location in real time. The server analyzes the GPS data, identifies the target's movement patterns, and plans the next extermination.

[0625] 6. Emotion recognition

[0626] The server inputs the user's voice and video data into an emotion recognition engine (IBM Watson, Azure Cognitive Services, etc.) for analysis.

[0627] The emotion recognition engine recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety.

[0628] The server automatically adjusts the system's behavior and the intensity and type of alert sounds based on the user's emotional state.

[0629] Specific examples

[0630] Example of target detection using image recognition

[0631] 1. Server: "Received video data from the camera and identified a suspicious individual. Location information: latitude 35.1234, longitude 137.5678. Display the detection results on the management screen."

[0632] Example of user support using emotion recognition

[0633] 2. Server: "Inputting user's voice and video data into the emotion recognition engine."

[0634] 3. Emotion recognition engine: "Identify when the user is stressed. Adjust the system's behavior and reduce the intensity of the warning sounds."

[0635] Example prompts

[0636] prompt:

[0637] "Analyze the video data received from the surveillance cameras and generate a code to identify suspicious individuals or dangerous creatures."

[0638] Generation example:

[0639] import cv2

[0640] Loading an image recognition model

[0641] model = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'graph.pbtxt')

[0642] def detect_objects(image):

[0643] Preprocessing of input images

[0644] image_blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300),

[0645] mean=(104.0, 177.0, 123.0))

[0646] model.setInput(image_blob)

[0647] detections = model.forward()

[0648] return detections

[0649] Capture camera data

[0650] cap = cv2.VideoCapture(0)

[0651] while True:

[0652] ret, frame = cap.read()

[0653] if not ret:

[0654] break

[0655] detections = detect_objects(frame)

[0656] Processing the findings

[0657] for i in range(detections.shape[2]):

[0658] confidence = detections[0, 0, i, 2]

[0659] if confidence > 0.7:

[0660] Obtaining the bounding box of an object

[0661] box = detections[0, 0, i, 3:7] np.array([frame.shape[1], frame.shape[0],

[0662] frame.shape[1], frame.shape[0]])

[0663] (x, y, x1, y1) = box.astype("int")

[0664] cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)

[0665] cv2.imshow('Frame', frame)

[0666] if cv2.waitKey(1) & 0xFF == ord('q'):

[0667] break

[0668] cap.release()

[0669] cv2.destroyAllWindows()

[0670] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0671] Step 1:

[0672] Real-time video data acquisition

[0673] The device (smartphone) activates the camera and captures video data in real time. This video data is sent to a cloud server at regular intervals. Specifically, the camera sensor captures environmental information and saves the video data in a specific folder on the device. The saved data is then uploaded to the cloud server.

[0674] Input: Video data from the camera sensor

[0675] Output: Video data sent to the cloud server

[0676] Step 2:

[0677] Analyzing image data and identifying objects

[0678] The server inputs the received video data into an image recognition AI to identify suspicious individuals or dangerous creatures. Specifically, it uses an image recognition model (e.g., TensorFlow, OpenCV) to detect specific patterns based on pixel information in the video. The model also generates location information for the target object and records this information in a database.

[0679] Input: Video data stored on a cloud server

[0680] Output: Location information of identified objects and their associated data

[0681] Step 3:

[0682] Environmental sound monitoring and audio data acquisition

[0683] The device constantly monitors environmental sounds using a highly sensitive microphone and transmits the audio data to a server at regular intervals. Specifically, the microphone captures the environmental sounds and the audio data is stored on the device. The stored data is then uploaded to a cloud server.

[0684] Input: Environmental sound data from a high-sensitivity microphone

[0685] Output: Audio data sent to the cloud server

[0686] Step 4:

[0687] Analyzing voice data and identifying objects

[0688] The server inputs the received audio data into a voice recognition AI to identify the voices of pests or suspicious individuals. Specifically, it uses a voice recognition model (e.g., Google Speech-to-Text, DeepSpeech) to analyze sound wave patterns and recognize specific sounds. The model also simultaneously generates location information for the target object and records this information in a database.

[0689] Input: Audio data stored on a cloud server

[0690] Output: Location information of identified objects and their associated data

[0691] Step 5:

[0692] Generates warning sounds

[0693] The server sends an alarm sound generation command to the device based on the identified location information. The device executes the received alarm sound generation command and plays the specified alarm sound. Specifically, the server generates the alarm sound using a speech synthesis engine and sends the command to the device. The device plays the alarm sound using its built-in speaker.

[0694] Input: Location information of identified object and command to generate warning sound

[0695] Output: Generated warning sound

[0696] Step 6:

[0697] Autonomous Robot Control

[0698] The server sends the location information of the detected object to the autonomous robot. Based on the location information received from the server, the autonomous robot automatically moves to its destination and monitors its surroundings with sensors and cameras. Specifically, the server generates robot control commands and sends them to the robot's control system. The robot begins operation, and after reaching the designated area, it uses sensors to identify the object, and then tracks and exterminates it.

[0699] Input: Position information of identified objects and robot control commands

[0700] Output: Object tracking and removal

[0701] Step 7:

[0702] Data analysis and behavioral pattern identification

[0703] The server collects object and extermination data collected from environmental sensors, cameras, and audio data, and inputs it into a data-mining AI to analyze behavioral patterns and preferred environments. Specifically, the AI ​​model extracts the object's past behavioral data from a database and identifies its movement patterns and behavioral characteristics. The analysis results are used to develop new extermination plans.

[0704] Input: Collected object and removal data

[0705] Output: Behavioral patterns and new eradication plan

[0706] Step 8:

[0707] User Emotion Recognition and Support

[0708] The server inputs the user's voice and video data into an emotion recognition engine for analysis. The emotion recognition engine recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety. Specifically, the server compares the user's past emotional data with their current data and recommends the optimal action. The system then makes automatic adjustments based on this.

[0709] Input: User's audio and video data

[0710] Output: User's emotional state and adjust the system accordingly

[0711] Through the above steps, the present invention can effectively exterminate pests and improve security, while also reducing the psychological burden on users.

[0712] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0713] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0714] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0715] [Second embodiment]

[0716] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0717] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0718] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0719] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0720] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0721] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0722] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0723] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0724] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0725] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0726] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0727] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0728] The present invention is a system for efficient and sustainable pest control that combines image recognition, voice recognition, autonomous robots, and data analytics to consistently detect, track, and eliminate pests.

[0729] System Configuration

[0730] The system consists of the following main components:

[0731] 1. Sensor or camera

[0732] Multiple sensors or cameras are installed in agricultural fields or protected areas of crops to capture real-time video and audio data of the environment.

[0733] 2. Image Recognition Methods

[0734] It includes algorithms for analyzing image data obtained from sensors or cameras and identifying pests.

[0735] 3. Location information acquisition means

[0736] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[0737] 4. Voice Generation Method

[0738] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[0739] 5. Autonomous Robotic Means

[0740] It has the ability to receive location information from the server and track and exterminate pests.

[0741] 6. Data Analysis Methods

[0742] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[0743] Program processing explanation

[0744] Detecting pests using image recognition AI

[0745] Server: Receives video data sent from sensors or cameras. Uses image recognition AI to detect specific pests in the video data. For example, if surveillance cameras are installed around a field and a wild boar appears in the video, it will be identified and its location will be generated.

[0746] Server: Records the location information of identified pests and displays it on the management screen, allowing for monitoring of their entry into crop protection areas and immediate response.

[0747] Keeping pests away with AI-powered audio alerts

[0748] Device: Monitors surrounding sounds with a highly sensitive microphone and captures audio data in real time.

[0749] Server: Using voice recognition AI, identifies the sounds of pests from the acquired audio data. If a specific pest sound is recognized, it generates its location information and sends a command to the device to generate an alarm sound.

[0750] Device: Plays a specified alarm sound to scare pests away from the extermination area. For example, a specific frequency of sound can be emitted to scare pests away from the area.

[0751] Autonomous robots track and eradicate pests

[0752] Server: Sends the location information of detected pests to the autonomous robot.

[0753] Autonomous robot: Based on location information, it moves in real time and tracks pests. It continuously identifies the location of pests using sensors and cameras, and continues to track them until extermination is complete.

[0754] Autonomous robots: They capture and exterminate pests, for example by using a net to capture them while tracking them or by using other extermination methods, and then send a completion report to a server after the extermination is complete.

[0755] Understanding the ecology of pests through data analysis

[0756] Server: Collects data on pest behavior and analyzes it using data mining AI. This allows the behavioral patterns and movement routes of pests to be identified and reflected in extermination plans.

[0757] Server: Captured pests are fitted with GPS trackers to track their location in real time, allowing for analysis of their movement patterns and the development of more effective extermination methods.

[0758] Specific examples

[0759] Example of pest detection using image recognition AI

[0760] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[0761] An example of keeping pests away using voice alert AI

[0762] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[0763] Server: "Send alarm command to device."

[0764] Terminal: "Play specified alarm sound. Keeps pests away."

[0765] An example of tracking and exterminating pests using an autonomous robot

[0766] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[0767] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[0768] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[0769] Example of understanding behavioral patterns through data analysis

[0770] Server: "We are currently analyzing extermination data using data mining AI. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[0771] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[0772] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[0773] In this way, a system is provided in which each element works together to efficiently exterminate harmful animals.

[0774] The processing flow will be explained below.

[0775] Program processing flow

[0776] Detecting pests using image recognition AI

[0777] Step 1:

[0778] Terminal: "Acquire video data from surveillance cameras in real time."

[0779] Step 2:

[0780] Terminal: "Send the acquired video data to the server at regular intervals."

[0781] Step 3:

[0782] Server: "Input the received video data into the image recognition AI."

[0783] Step 4:

[0784] Server: "Image recognition AI analyzes video data to identify pests. For example, it can identify the shapes of wild boars and deer."

[0785] Step 5:

[0786] Server: "Generate location information (latitude, longitude) for identified pests."

[0787] Step 6:

[0788] Server: "Record the generated location information in the database and display it on the management screen."

[0789] Keeping pests away with AI-powered audio alerts

[0790] Step 1:

[0791] Device: "High-sensitivity microphone constantly monitors environmental sounds."

[0792] Step 2:

[0793] Terminal: "Send the acquired audio data to the server at regular intervals."

[0794] Step 3:

[0795] Server: "Input the received voice data into the voice recognition AI."

[0796] Step 4:

[0797] Server: "The voice recognition AI analyzes the audio data to identify the sounds of pests. For example, it can identify the sounds of wild boars."

[0798] Step 5:

[0799] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[0800] Step 6:

[0801] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[0802] Autonomous robots track and eradicate pests

[0803] Step 1:

[0804] Server: "Send the location of detected pests to the autonomous robot."

[0805] Step 2:

[0806] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[0807] Step 3:

[0808] Autonomous robots: "Once they arrive, they monitor the surrounding area using sensors and cameras to locate pests."

[0809] Step 4:

[0810] Autonomous robots: "Prepare means (nets, tranquilizer guns, etc.) to track and capture pests."

[0811] Step 5:

[0812] Autonomous robots: "Capture and reliably eliminate pests."

[0813] Step 6:

[0814] Autonomous robot: "Reports to the server that disinfection is complete."

[0815] Understanding the ecology of pests through data analysis

[0816] Step 1:

[0817] Server: "Stores pest and extermination data collected from sensors and cameras in a database."

[0818] Step 2:

[0819] Server: "The collected data is fed into a data mining AI."

[0820] Step 3:

[0821] Server: "Data mining AI will analyze pest behavior patterns and preferred environments."

[0822] Step 4:

[0823] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[0824] Step 5:

[0825] User: "I'll put a GPS tracker on the captured vermin."

[0826] Step 6:

[0827] GPS tracker: "Sends real-time location information of pests to a server."

[0828] Step 7:

[0829] Server: "Analyze GPS data to understand pest movement patterns."

[0830] Step 8:

[0831] Server: "Plan the next cleanup and notify the user."

[0832] The above is a specific processing flow for carrying out the present invention, which makes it possible to provide a system that realizes efficient and sustainable pest control.

[0833] Example 1

[0834] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0835] Pests cause serious damage to agricultural crops, and traditional extermination methods are difficult to implement efficiently and sustainably. Furthermore, there are insufficient means to monitor pest intrusions in real time and respond quickly, which means the damage tends to spread. To solve these problems, a system is needed that can detect, track, and exterminate pests in an integrated manner, while analyzing their behavioral patterns to prevent future intrusions.

[0836] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0837] In this invention, the server includes image recognition means for acquiring image data using a sensor or camera and analyzing the image data, location information acquisition means for acquiring location information of a target identified by the image recognition means, sound generation means for generating sound to repel the target identified by the image recognition means, autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, data analysis means for analyzing the target's biological information and identifying its behavioral pattern, communication means for transmitting the acquired video and audio data to the server in real time, and command generation means for generating an alarm sound to be played and transmitting it to a terminal. This makes it possible to perform a consistent process from detecting, tracking, and exterminating pests to analyzing their behavior.

[0838] "Sensor or camera" means a device for acquiring still or video images of the environment and capable of collecting data in real time.

[0839] "Image recognition means" includes algorithms and technologies for analyzing acquired image data and identifying and specifying specific objects (e.g., pests) from within the data.

[0840] The "location information acquisition means" is a means for measuring the location of an object identified by the image recognition means and generating and acquiring that information.

[0841] "Sound generation means" refers to a device or technology that generates warning sounds or the like based on identified sound data in order to keep a specific target away.

[0842] "Autonomous robot means" refers to a type of robot that has the ability to move automatically based on location information sent from a server and track and eliminate specific targets.

[0843] "Data analysis means" refers to systems or technologies that collect and analyze behavioral data of subjects to extract specific patterns and information.

[0844] "Communication means" refers to technology or equipment for transmitting acquired video and audio data to a server in real time, and has the ability to exchange data via a network.

[0845] The "command generation means" is a mechanism that generates commands to instruct the terminal to execute an alarm or other processing based on the data analyzed by the server.

[0846] The present invention relates to a system for efficiently and sustainably eradicating pests in protected areas of agricultural crops, which is composed of a combination of sensors, cameras, image recognition means, autonomous robots, data analysis means, communication means, and sound generation means.

[0847] Hardware and software used

[0848] The specific hardware used in this system includes high-resolution cameras, high-sensitivity microphones, cloud servers, autonomous robots, and GPS trackers. Image recognition is performed using image recognition AI algorithms such as YOLOv5. Voice recognition is performed using deep learning-based voice recognition AI.

[0849] System Operation Overview

[0850] 1. Sensor or camera placement and data acquisition

[0851] The sensors or cameras are installed in agricultural fields and protected areas of agricultural crops to capture real-time video and audio data of the surrounding area. In particular, all-weather, high-resolution cameras and highly sensitive microphones are used to monitor pests that target crops.

[0852] 2. Analysis of video data

[0853] The acquired video data is sent to a server. The server receives the video data in real time and uses image recognition techniques (such as YOLOv5) to detect pests. Once a detected pest is identified, its location information is automatically generated and displayed on the management screen.

[0854] 3. Analysis of audio data and generation of warning sounds

[0855] The device uses a highly sensitive microphone to collect surrounding sounds and transmits them to a server in real time. The server then uses a voice recognition tool to analyze the audio data and detect the sounds of specific pests. Based on the detected sounds, a command to generate an alarm sound is sent to the device and played back. This frightens pests away from the extermination area.

[0856] 4. Autonomous robots for tracking and eliminating pests

[0857] The server sends the location information of the detected pests to the autonomous robot. The autonomous robot moves in real time based on the received location information and tracks the pests. During tracking, it continuously identifies the location of the pests using sensors and cameras, and carries out extermination using nets, etc. Once extermination is complete, a completion report is sent to the server.

[0858] 5. Data analysis and behavioral pattern understanding

[0859] The server collects data on the behavior of pests and analyzes it using data analysis tools. This allows the behavioral patterns and movement routes of pests to be identified and reflected in the next extermination plan. In addition, GPS trackers are attached to captured pests to track their location in real time. This allows for the development of even more effective extermination methods.

[0860] Specific examples

[0861] Example of pest detection using image recognition AI

[0862] Server: "Receiving video data from camera 1. Identified a wild boar. Generate location information: latitude 35.1234, longitude 137.5678, and display it on the management screen."

[0863] An example of keeping pests away using voice alert AI

[0864] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[0865] Server: "Send a command to generate an alarm to the device."

[0866] Terminal: "Play specified alarm sound. Keeps pests away."

[0867] An example of tracking and exterminating pests using an autonomous robot

[0868] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[0869] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[0870] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[0871] Example of understanding behavioral patterns through data analysis

[0872] Server: "We are currently analyzing the extermination data using data analysis tools. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[0873] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[0874] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[0875] Prompt Sentence Examples

[0876] "One night in October, an IoT sensor detected the movement of a wild boar in a protected area for agricultural crops. Image recognition AI analyzed the footage to identify the boar and generate location information. An autonomous robot then used the location information to track the boar and capture it with a net. Please describe this entire process."

[0877] The system of the present invention provides a comprehensive solution for efficient and sustainable pest control, allowing users to monitor and control in real time through an administrative interface.

[0878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0879] Step 1:

[0880] Initial setup and sensor preparation

[0881] Server: Checks the setting information of sensors and cameras, and verifies that each device is working properly. It distributes IP addresses and setting information to each device to ensure communication is established.

[0882] Input: Sensor and camera settings, device ID

[0883] Output: Device operation check result, device setting completion notification

[0884] Specific operation: The server sends the configuration information to each sensor, and once normal operation is confirmed, it receives a "configuration complete" notification.

[0885] Terminal: Check the installation location of sensors and cameras and place them appropriately. The installation locations should be set in agricultural land and crop protection areas.

[0886] Input: Location information, physical layout of sensors and cameras

[0887] Output: Placement completion notification

[0888] What it does: The device physically places cameras and sensors in farmland or protected areas and fixes them at appropriate angles.

[0889] Users: Monitor the operation of sensors and cameras through the admin panel and check for any issues.

[0890] Input: Sensor / camera status information on the management screen

[0891] Output: Results of normal operation of each device

[0892] What happens: The user checks the admin panel and confirms that all devices are working properly.

[0893] Step 2:

[0894] Acquisition and transmission of video data

[0895] Sensors / cameras: Capture real-time video data from the area where they are installed, with a particular focus on pest monitoring.

[0896] Input: Real-time video and audio from the scene

[0897] Output: Get raw data

[0898] Specific operation: Sensors and cameras continuously record and acquire video and audio within a specified range.

[0899] Terminal: Compresses the captured video data and sends it to the server, improving data transfer efficiency.

[0900] Input: Raw data (video and audio)

[0901] Output: Compressed data, notification of completion

[0902] Specific operation: The device compresses the video and audio data and uploads it to the server.

[0903] Step 3:

[0904] Image recognition for detecting pests

[0905] Server: Analyzes the received video data and runs image recognition AI to detect specific pests (e.g., wild boars).

[0906] Input: Compressed video data

[0907] Output: Identification and location information of detected pests

[0908] Specific operation: The server analyzes the video data, detects pests using YOLOv5, and generates location information.

[0909] Server: Generates location information of pests and displays it on the management screen.

[0910] Input: Detected pest's identity and location

[0911] Output: Location information generated and displayed on the management screen

[0912] Specific operation: The server displays the location information of the pests on the management screen, allowing the user to check it in real time.

[0913] Step 4:

[0914] Acquisition and analysis of audio data

[0915] Device: A highly sensitive microphone captures surrounding audio data and transmits it to the server in real time.

[0916] Input: Real-time audio from the site

[0917] Output: Acquired audio data, transmission completion notification

[0918] Specific operation: The device picks up sound with a high-sensitivity microphone and sends the data to the server.

[0919] Server: Analyzes the captured audio data and uses voice recognition AI to identify the sounds of pests.

[0920] Input: Captured audio data

[0921] Output: Enemy call identification result, location information

[0922] Specific operation: The server analyzes the audio data and uses deep learning to identify the sounds of pests and generate location information.

[0923] Step 5:

[0924] Audio warning and pest repellent

[0925] Server: Based on the detected sounds, sends a command to the device to generate an alarm sound.

[0926] Input: Call identification result, location information

[0927] Output: Command to generate warning sound

[0928] Specific operation: The server generates an alarm command and sends it to the device.

[0929] Terminal: Receives an alert sound generation command from the server and plays the specified alert sound.

[0930] Input: Command to generate warning sound

[0931] Output: Warning sound played, distancing result

[0932] Specific action: The device plays an alarm sound to scare away pests.

[0933] Step 6:

[0934] Autonomous robots track and eradicate pests

[0935] Server: Sends the location information of detected pests to the autonomous robot.

[0936] Input: Location of detected pest

[0937] Output: Location information sending completion notification

[0938] Specific operation: The server sends location information to the autonomous robot.

[0939] Autonomous robot: Moves in real time based on received location information and tracks pests.

[0940] Input: Location

[0941] Output: Pest capture results, extermination report

[0942] Specific operation: The autonomous robot moves based on location information, tracks and captures pests.

[0943] Autonomous robot: Reports to the server once disinfection is complete.

[0944] Input: Disinfection completion information

[0945] Output: Report of disinfection completion to the server

[0946] Specific operation: The autonomous robot sends a report of the completion of eradication to the server.

[0947] Step 7:

[0948] Data analysis and understanding of behavioral patterns

[0949] Server: Collects behavioral data of pests and analyzes it using data analysis tools.

[0950] Input: Behavioral data obtained during removal

[0951] Output: Identification of behavioral patterns

[0952] Specific operation: The server analyzes the behavioral data and identifies behavioral patterns and movement routes.

[0953] Server: GPS trackers are attached to captured pests to track their location in real time.

[0954] Input: Captured pest information, GPS tracker

[0955] Output: GPS data, behavioral patterns

[0956] What it does: The server analyzes the GPS data and develops an effective removal method.

[0957] With detailed processing at each step, the system can detect, track, exterminate and analyze pests in an integrated manner.

[0958] (Application example 1)

[0959] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0960] Conventional pest control systems did not consistently detect, track, and exterminate pests, making it difficult to respond in a timely manner. Furthermore, when a pest invades a factory, it is difficult to understand its entry route and behavioral patterns, making it impossible to develop an efficient extermination method. Another issue is that there is no interface that allows users to check the current location of pests in real time, making it difficult to respond quickly.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0962] In this invention, the server includes an image recognition means that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition means that acquires location information of an object identified by the image recognition means, a sound generation means that generates sound to repel the object identified by the image recognition means, an autonomous robot means that automatically moves to track and exterminate the object based on the location information of the object, a data analysis means that analyzes the object's biological information and identifies its behavioral pattern, and a user interface means that provides an interface that allows a user to check the video data acquired from the sensor or camera and the sound data detected by the sound recognition means in real time. This makes it possible to perform an integrated process from detection to extermination of pests, and to understand the entry routes and behavioral patterns of pests within a factory, thereby enabling quick and efficient extermination.

[0963] A "sensor" is a device that detects changes in the physical environment and transmits them as a signal.

[0964] A "camera" is a device that captures video or images and stores and transmits them as digital data.

[0965] "Image recognition means" refers to a device that includes an algorithm or program for analyzing image data obtained from a sensor or camera and identifying a specific object.

[0966] The "location information acquisition means" is a device that identifies the location of the object identified by the image recognition means, and acquires and transmits the location information.

[0967] The "audio generating means" is a device that generates audio based on preset conditions and plays it back.

[0968] An "autonomous robotic means" is a robot that has the ability to move automatically based on location information and track and eliminate identified targets.

[0969] "Data analysis means" is a system for analyzing collected data and identifying the behavioral patterns and ecological information of the subject.

[0970] The "user interface means" is a device that provides an interface that allows a user to check the video data and audio data obtained from the sensor or camera in real time.

[0971] "Speech recognition means" is a device that includes an algorithm or program for analyzing voice data acquired by a high-sensitivity microphone and identifying the voice of a specific target.

[0972] A "GPS tracker" is a device that obtains and tracks location information in real time.

[0973] This invention is a system for efficiently exterminating pests in factories. This system is realized by combining hardware such as sensors, cameras, high-sensitivity microphones, and autonomous robots with software such as image recognition AI, voice recognition AI, and data analysis AI.

[0974] Hardware Configuration

[0975] Sensors or cameras: Installed in various locations within the factory, they capture video and audio data in real time.

[0976] High-sensitivity microphone: Collects sounds within the factory and detects the cries of pests.

[0977] Autonomous robots: They move automatically based on the location of pests to track and exterminate them.

[0978] GPS tracker: Attached to pests captured by autonomous robots, it tracks their location in real time.

[0979] Software Configuration

[0980] Image recognition AI (e.g., TensorFlow): Analyzes video data obtained from sensors or cameras to detect pests.

[0981] Speech recognition AI (e.g., Google Speech Recognition): Analyzes audio data collected by a highly sensitive microphone and identifies the sounds of pests.

[0982] Data analysis AI (e.g., SciKit-Learn): Analyzes behavioral patterns and ecological information of pests and reflects this in extermination plans.

[0983] User interface: Provides an interface that allows users to view video and audio data in real time via smartphones, tablets, or smart glasses.

[0984] Specific operation of the system

[0985] The server receives video data from sensors or cameras and uses image recognition AI to detect pests. For example, if a camera installed in a factory captures a wild boar in the video, it analyzes the data and generates location information. This location information is sent in real time to an autonomous robot, which then begins tracking the pest.

[0986] The server also uses voice recognition AI to analyze audio data obtained from a highly sensitive microphone, and if it detects the cries of a pest, it obtains its location information. Based on this location information, a sound generator generates an alarm sound to keep the pest away.

[0987] Using the provided user interface, users can grasp the current situation in real time and take necessary measures. For example, they can check the footage from the surveillance cameras in the factory on their smartphones and take immediate action if a pest is detected.

[0988] Specific examples

[0989] Pest detection: "The model analyzed the video data from the camera and detected a wild boar. Its location is latitude 35.1234, longitude 137.5678. An instruction has been sent to the robot."

[0990] Roar detection: "A highly sensitive microphone has detected the sound of a wild boar roaring. An alarm sound will be played to keep the boar away from the factory."

[0991] User notification: "We have reviewed the footage from the surveillance camera and detected a pest. The robot will now move to the specified location and begin extermination."

[0992] These functions enable the rapid detection of pest intrusions and their efficient extermination, making this a very useful system for factories where real-time confirmation and rapid response are required.

[0993] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0994] Step 1:

[0995] The server acquires video data in real time from sensors or cameras installed within the factory.

[0996] Input: Video data from a sensor or camera

[0997] Output: Video data passed to image recognition AI

[0998] How it works: The cameras continuously capture video at various locations in the factory and send the data to a server.

[0999] Step 2:

[1000] The server inputs the acquired video data into image recognition AI and performs analysis to detect pests.

[1001] Input: Video data acquired in the previous step

[1002] Output: Pest identification and location

[1003] How it works: Image recognition AI detects features such as people and vehicles in video data and determines whether it contains any vermin. For example, if it detects a video containing a wild boar, it generates its location information.

[1004] Step 3:

[1005] The server obtains the location information of pests detected by image recognition AI and transmits it to the autonomous robot.

[1006] Input: Pest identification and location

[1007] Output: Movement instructions for the autonomous robot

[1008] Specific operation: Based on the obtained location information, the server issues instructions to the autonomous robot, causing it to move in order to capture the pest.

[1009] Step 4:

[1010] The autonomous robots move automatically based on the transmitted location information to track and eliminate pests.

[1011] Input: Location information and movement instructions from the server

[1012] Output: Report of pest capture and extermination completion

[1013] Specific behavior: The robot moves towards the designated location, continuously locates the pest using pest tracking sensors and cameras, and then performs extermination measures, such as capturing the pest with a net or using appropriate extermination methods.

[1014] Step 5:

[1015] The server collects real-time audio data from high-sensitivity microphones installed within the factory.

[1016] Input: Audio data from a high-sensitivity microphone

[1017] Output: Voice data passed to the voice recognition AI

[1018] How it works: Highly sensitive microphones collect audio from various locations within the factory and send the data to a server.

[1019] Step 6:

[1020] The server uses voice recognition AI to analyze the acquired audio data and detect the cries of specific pests.

[1021] Input: Audio data obtained in the previous step

[1022] Output: Sound detection results and location information of pests

[1023] How it works: The voice recognition AI analyzes unique sound patterns contained in the audio data to detect the calls of specific pests (such as wild boars). If detected, it generates location information.

[1024] Step 7:

[1025] The server generates and reproduces an alarm sound for keeping away harmful animals from the sound generating means based on the location information of the harmful animal's cry.

[1026] Input: Call detection results and location information of pests

[1027] Output: Generates and plays warning sounds

[1028] Specific behavior: The server generates a specified alarm sound and uses surrounding speakers to play sounds of frequencies that pests dislike, thereby keeping pests away.

[1029] Step 8:

[1030] The user uses the provided user interface to check the video data and audio data in real time.

[1031] Input: Real-time data (video and audio) from the server

[1032] Output: what is displayed in the user interface

[1033] Specific operation: Users operate the interface via smartphones, tablets, or smart glasses to monitor the situation in the factory in real time and take appropriate action if necessary.

[1034] Step 9:

[1035] The server uses data analysis AI to analyze the behavioral patterns and ecological information of pests and reflect this in extermination plans.

[1036] Input: Pest location and behavior data

[1037] Output: Behavioral pattern analysis results and eradication plan

[1038] How it works: The data analysis AI uses the collected data to identify the behavioral patterns and ecological information of pests, and then plans more effective extermination methods. The results are notified to the user and incorporated into the next extermination plan.

[1039] These steps enable consistent and efficient detection and extermination of pests, enabling real-time monitoring and rapid response.

[1040] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1041] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and the emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[1042] System Configuration

[1043] The system consists of the following main components:

[1044] 1. Sensor or camera

[1045] Multiple sensors or cameras installed on-site capture video and audio data of the environment in real time.

[1046] 2. Image Recognition Methods

[1047] It includes algorithms for analyzing acquired image data and identifying pests.

[1048] 3. Location information acquisition means

[1049] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[1050] 4. Voice Generation Method

[1051] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[1052] 5. Autonomous Robotic Means

[1053] It has the ability to receive location information from the server and track and exterminate pests.

[1054] 6. Data Analysis Methods

[1055] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[1056] 7. Emotion Engine

[1057] It includes technology for recognizing user emotions and adjusting system behavior.

[1058] Program processing explanation

[1059] Detecting pests using image recognition AI

[1060] Terminal: "Acquire video data from the surveillance camera in real time. Send the video data to the server at regular intervals."

[1061] Server: "The received video data is input into image recognition AI to identify harmful animals. For example, wild boars and deer."

[1062] Server: "Generate location information for identified pests, record it in a database, and display it on the management screen."

[1063] Keeping pests away with AI-powered audio alerts

[1064] Device: "Constantly monitors environmental sounds with a high-sensitivity microphone. The acquired audio data is sent to the server at regular intervals."

[1065] Server: "The received audio data is input into the speech recognition AI to identify the sound of a pest. For example, it identifies the sound of a wild boar."

[1066] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[1067] Device: "Plays an alarm sound of the specified frequency and volume to ward off pests."

[1068] Autonomous robots track and eradicate pests

[1069] Server: "Send the location of detected pests to the autonomous robot."

[1070] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[1071] Autonomous robots: "Once they arrive, they monitor the surroundings with sensors and cameras to identify pests. They then begin tracking and exterminating them."

[1072] Autonomous robot: "Captures pests and exterminates them reliably. Once extermination is complete, it sends a completion report to the server."

[1073] Understanding the ecology of pests through data analysis

[1074] Server: "Collects pest and extermination data collected from environmental sensors and cameras."

[1075] Server: "The collected data is fed into a data-mining AI to analyze the behavioral patterns and preferred environments of pests."

[1076] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[1077] User: "I'll put a GPS tracker on the captured vermin."

[1078] GPS Tracker: "Tracks the location of pests in real time and sends it to a server."

[1079] Server: "We analyze GPS data to identify pest movement patterns and plan the next eradication."

[1080] User support with emotion engine

[1081] Server: "Input the user's voice and video data into the emotion engine and analyze it."

[1082] Emotion Engine: "Recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety."

[1083] Server: "Automatically adjust system behavior and alert intensity and type based on the user's emotional state."

[1084] Specific examples

[1085] Example of pest detection using image recognition AI

[1086] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[1087] An example of keeping pests away using voice alert AI

[1088] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[1089] Server: "Send a command to generate an alarm to the device."

[1090] Terminal: "Play the specified alarm sound to keep pests away."

[1091] An example of tracking and exterminating pests using an autonomous robot

[1092] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[1093] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[1094] Autonomous robot: "Captures pests and traps them in a net. Extermination complete. Sends a completion report to the server."

[1095] Example of user support using emotion engine

[1096] Server: "Inputting user's audio and video data into the emotion engine."

[1097] Emotion engine: "Identify when the user is stressed. Adjust system behavior and reduce the intensity of alert sounds."

[1098] Server: "Notify user of response. Taking action based on emotional state."

[1099] This system not only enables efficient pest control, but also reduces the psychological burden on users.

[1100] The processing flow will be explained below.

[1101] Program processing flow

[1102] Detecting pests using image recognition AI

[1103] Step 1:

[1104] Terminal: "Activate the surveillance camera and capture video data in real time."

[1105] Step 2:

[1106] Terminal: "Sends acquired video data to the server at regular intervals."

[1107] Step 3:

[1108] Server: "Input the received video data into the image recognition AI."

[1109] Step 4:

[1110] Server: "Image recognition AI analyzes video data and identifies specific pests (e.g., wild boars and deer)."

[1111] Step 5:

[1112] Server: "Generate location information (latitude, longitude) for identified pests."

[1113] Step 6:

[1114] Server: "Record the generated location information in the database and display it on the management screen."

[1115] Keeping pests away with AI-powered audio alerts

[1116] Step 1:

[1117] Device: "Uses a highly sensitive microphone to constantly monitor the surrounding environment."

[1118] Step 2:

[1119] Device: "Send the acquired audio data to the server at regular intervals."

[1120] Step 3:

[1121] Server: "Input the received voice data into the voice recognition AI."

[1122] Step 4:

[1123] Server: "The voice recognition AI analyzes the audio data and identifies the sounds of specific pests (e.g., wild boars)."

[1124] Step 5:

[1125] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[1126] Step 6:

[1127] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[1128] Autonomous robots track and eradicate pests

[1129] Step 1:

[1130] Server: "Send the location of detected pests to the autonomous robot."

[1131] Step 2:

[1132] Autonomous robot: "Autonomously moves to the destination based on location information received from the server."

[1133] Step 3:

[1134] Autonomous robot: "After reaching its destination, it monitors its surroundings with sensors and cameras to reconfirm the location of the pest."

[1135] Step 4:

[1136] Autonomous robot: "Prepare means to capture pests (e.g., nets, tranquilizer guns)."

[1137] Step 5:

[1138] Autonomous robots: "Reliably capture and exterminate pests."

[1139] Step 6:

[1140] Autonomous robot: "Reports to the server that disinfection is complete."

[1141] Understanding the ecology of pests through data analysis

[1142] Step 1:

[1143] Server: "Stores pest and extermination data collected from environmental sensors and cameras in a database."

[1144] Step 2:

[1145] Server: "Feed the stored data into the data mining AI."

[1146] Step 3:

[1147] Server: "Data mining AI analyzes pest behavior patterns and migration routes."

[1148] Step 4:

[1149] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[1150] Step 5:

[1151] User: "We'll attach a GPS tracker to the captured pest."

[1152] Step 6:

[1153] GPS tracker: "Sends real-time location information of pests to a server."

[1154] Step 7:

[1155] Server: "Analyze GPS data to identify pest movement patterns."

[1156] Step 8:

[1157] Server: "Based on the identified movement patterns, create a plan for the next eradication and notify the user."

[1158] User support with emotion engine

[1159] Step 1:

[1160] Server: "Input the user's voice and video data into the emotion engine."

[1161] Step 2:

[1162] Emotion engine: "Analyzes audio and video data to identify the user's emotional state. For example, identifying stress or anxiety."

[1163] Step 3:

[1164] Server: "Adjust the system's behavior based on the emotion engine's analysis. For example, change the intensity or type of alarm sound."

[1165] Step 4:

[1166] Server: "Propose countermeasures based on the user's emotional state and notify the user when the countermeasures are implemented."

[1167] The above is a specific processing flow for carrying out the present invention, which not only enables efficient pest control but also reduces the psychological burden on the user.

[1168] Example 2

[1169] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1170] Conventional pest control systems have difficulty detecting and eliminating pests efficiently and sustainably. Furthermore, they lack functionality to reduce the mental stress and anxiety users feel when using the system. A means to accurately grasp the behavioral patterns of pests and efficiently eliminate them is needed, as well as functionality to provide psychological support to users.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1172] In this invention, the server includes an image recognition means for acquiring image data using a sensor or a camera and analyzing the image data, a location information acquisition means for acquiring location information of a target identified by the image recognition means, a sound generation means for generating sound to repel the target identified by the image recognition means, an autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, a data analysis means for analyzing the target's ecological information and identifying its behavioral pattern, a sound recognition means for analyzing the target's sound and generating an alarm sound, and an emotion engine for recognizing the user's emotional state and adjusting the system's operation. This not only enables consistent and efficient detection, tracking, and extermination of pests, but also reduces the user's mental stress.

[1173] "Sensor or camera" means a device for acquiring real-time video and audio data of the environment.

[1174] "Image recognition means" refers to an algorithm or software that analyzes acquired image data and identifies specific objects or animals (e.g., vermin).

[1175] The "location information acquisition means" is a means for measuring the location information of the object identified by the image recognition means and providing it to the system.

[1176] The "sound generating means" is a device that generates sounds to repel targets, such as collecting environmental sounds using a highly sensitive microphone, and plays specific warning sounds.

[1177] "Autonomous robotic means" refers to a robot that moves automatically based on the location information of the target, and is equipped with an autonomous driving algorithm to track and exterminate the target.

[1178] "Data analysis methods" are methods for collecting ecological information about the subject and analyzing its behavioral patterns and preferred environments. Data mining and machine learning algorithms are used.

[1179] "Speech recognition means" is a means of analyzing voice data acquired by a high-sensitivity microphone and identifying specific sounds (for example, the cries of pests). It often uses a voice feature extraction algorithm.

[1180] The "Emotion Engine" is a technology that analyzes the user's voice and video data, recognizes the user's emotional state, and adjusts the system's behavior accordingly, suggesting actions to reduce stress and anxiety.

[1181] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and an emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[1182] The system consists of the following main components:

[1183] 1. Sensor or camera

[1184] Multiple sensors or cameras installed on-site capture real-time video and audio data of the environment. For example, infrared cameras and high-resolution cameras can be used.

[1185] 2. Image Recognition Methods

[1186] It includes algorithms for analyzing acquired image data and identifying pests. For example, it uses object detection models such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector). The server processes the image data sent from the device in real time and generates location information for identified pests.

[1187] 3. Location information acquisition means

[1188] The location information of the pests identified by the image recognition means is acquired and sent to a server. The location information is measured using GPS or a geographic information system (GIS).

[1189] 4. Voice Generation Method

[1190] It uses a highly sensitive microphone to pick up ambient sounds, and when it detects the sound of a pest, it generates a sound to scare the pest away, for example, playing the sound of a hunting dog or other warning sounds in a specific frequency band.

[1191] 5. Autonomous Robotic Means

[1192] The autonomous robot receives location information from a server and is capable of tracking and eliminating pests. It is equipped with self-driving technology and can reach its destination while avoiding obstacles. Once it arrives, it monitors its surroundings using cameras and sensors, and exterminates pests after identifying them. For example, it uses nets or traps.

[1193] 6. Data Analysis Methods

[1194] This is a system for collecting and analyzing the behavioral patterns and ecological information of pests. It uses data mining technology and machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the preferred environments and movement patterns of pests, which makes it possible to plan the next eradication.

[1195] 7. Emotion Engine

[1196] The user's voice and video data are input into the emotion engine for analysis. For example, technology analyzing voice tone and facial expressions is used to recognize the user's emotional state. Based on the analysis results, the system's operation and the intensity of warning sounds are automatically adjusted to reduce the user's mental stress.

[1197] Specific examples

[1198] Example of pest detection using image recognition AI

[1199] Terminal: Obtaining video data from a surveillance camera in real time. Sending the video data to the server at regular intervals.

[1200] Server: Receives video data from the camera and inputs it into image recognition AI (YOLO model). Identifies wild boars.

[1201] Server: Generates the location information of the identified wild boar (latitude 35.1234, longitude 137.5678) and displays it on the management screen.

[1202] An example of keeping pests away using voice alert AI

[1203] Device: High-sensitivity microphone picking up surrounding sounds. The sound of a wild boar has been identified.

[1204] Device: Send the determined location information (latitude 35.1234, longitude 137.5678) to the server.

[1205] Server: Based on the received data, the server sends a command to generate an alarm sound to the terminal.

[1206] Terminal: Plays the specified alarm sound to scare off wild boars.

[1207] An example of tracking and exterminating pests using an autonomous robot

[1208] Server: Send the boar's location information to the autonomous robot (latitude 35.1234, longitude 137.5678).

[1209] Autonomous robot: The robot moves automatically to the specified location.

[1210] Autonomous robot: After arriving, it uses sensors and cameras to identify wild boars and begins extermination. It then captures them using a net.

[1211] Autonomous robot: After the removal is completed, it sends a completion report to the server.

[1212] Example of user support using emotion engine

[1213] Server: Inputting user's audio and video data into the emotion engine.

[1214] Emotion Engine: Identifies when a user is stressed and adjusts system behavior to reduce the intensity of alert sounds.

[1215] Server: Informs the user about system adjustments and takes actions based on their emotional state.

[1216] This system not only enables efficient and sustainable pest control, but also reduces the psychological burden on users.

[1217] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1218] Step 1:

[1219] Acquisition and transmission of image data

[1220] Terminal: Acquires real-time video data from surveillance cameras, such as infrared cameras and high-resolution cameras.

[1221] Input: Live video data of the environment.

[1222] Specific operation: The camera captures video of the environment and captures video frames at regular intervals (e.g., every second).

[1223] Terminal: The acquired video data is sent to the server at regular intervals.

[1224] Output: Video data sent to the server.

[1225] Step 2:

[1226] Vermin detection

[1227] Server: Inputs the received video data into the image recognition AI algorithm.

[1228] Input: Video data sent from the device.

[1229] What it does: The server uses an object detection model such as YOLO or SSD to identify objects in the frame.

[1230] Server: Identifies vermin (e.g., wild boars and deer) and generates their location information.

[1231] Output: Location of identified pests.

[1232] Step 3:

[1233] Sending location information of pests

[1234] Server: Sends location information to the autonomous robot using REST API or MQTT.

[1235] Input: The generated location information.

[1236] Specific operation: The server makes an API call or messaging to send the location information of the identified pest to the robot.

[1237] Output: Location information sent to the autonomous robot.

[1238] Step 4:

[1239] Autonomous robotic mobility and surveillance

[1240] Autonomous robot: Automatically moves to the destination based on the location information received from the server.

[1241] Input: Location information sent by the server.

[1242] Specific behavior: The robot uses an autonomous driving algorithm to reach a specified location while avoiding obstacles.

[1243] Autonomous robot: After arriving at its destination, it monitors the surrounding area with cameras and sensors to reconfirm the presence of pests.

[1244] Output: Confirmed pest presence information.

[1245] Step 5:

[1246] Performing and reporting removal

[1247] Autonomous robot: Performs specialized extermination actions towards identified pests.

[1248] Input: Confirmed pest presence information.

[1249] Specific behavior: The robot captures pests using nets and traps.

[1250] Autonomous robot: When the eradication is completed, it sends the eradication result data to the server as a completion report.

[1251] Output: Cleaning report sent to the server.

[1252] Step 6:

[1253] Acquisition and analysis of audio data

[1254] Device: High-sensitivity microphone constantly monitors surrounding sounds.

[1255] Input: Ambient sounds.

[1256] Specific operation: The microphone collects ambient sounds and stores the audio data in a buffer at regular intervals (for example, every second).

[1257] Device: Sends the saved audio data to the server.

[1258] Output: The audio data sent to the server.

[1259] Step 7:

[1260] Pest sound recognition and alarm sound generation

[1261] Server: Inputs the received voice data into the voice recognition AI.

[1262] Input: Audio data sent from the device.

[1263] Specific operation: The server uses an audio feature extraction algorithm to identify the sound of the pest.

[1264] Server: Based on the identified sounds of pests, it sends instructions to the device to generate alarm sounds to scare away the pests.

[1265] Output: Speech generation instructions.

[1266] Device: Plays an alarm sound at the specified frequency and volume according to the received instructions.

[1267] Output: An alarm sound is played.

[1268] Step 8:

[1269] Data analysis and eradication plan development

[1270] Server: Stores pest and extermination data collected from environmental sensors and cameras in a database.

[1271] Input: Data collected from sensors and cameras.

[1272] What it does: The server uses data mining techniques and machine learning algorithms to analyze the collected data.

[1273] Server: Analyzes pest behavior patterns and preferred environments.

[1274] Output: A removal plan based on the analysis results.

[1275] Step 9:

[1276] User support with emotion engine

[1277] Server: Inputs the user's voice and video data into the emotion engine.

[1278] Input: User audio and video data.

[1279] What it does: The emotion engine analyzes voice tone and facial expressions to identify the user's emotional state.

[1280] Emotion engine: Recognizes the user's emotional state based on the analysis results.

[1281] Output: The user's emotional state.

[1282] Server: Adjusts system behavior and alert sound intensity based on the user's emotional state.

[1283] Output: Adjusted system operation information.

[1284] Through this specific processing step, the system can maximize the effectiveness of pest control and reduce the psychological burden on the user.

[1285] (Application example 2)

[1286] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1287] Conventional pest control systems can detect and track pests, but they are unable to adjust their operation in response to the user's emotional state. This often leads to anxiety and stress during the pest control process. It is also difficult to utilize these systems for security purposes, such as identifying suspicious individuals or dangerous creatures. The purpose of this invention is to solve these problems and provide a more effective and user-friendly system.

[1288] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1289] In this invention, the server includes an image recognition unit that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition unit that acquires location information of an object identified by the image recognition unit, a voice generation unit that generates a voice to repel the object identified by the image recognition unit, an autonomous robot unit that automatically moves to track and exterminate the object based on the location information of the object, a data analysis unit that analyzes the object's ecological information and identifies its behavioral pattern, and an emotion recognition unit that analyzes the user's emotional state and adjusts the system's operation. This enables efficient and sustainable pest extermination and reduces the user's psychological burden. The system can also identify suspicious individuals and dangerous creatures, improving facility safety.

[1290] A "sensor" or "camera" is a device for acquiring image data or audio data, and is used for environmental monitoring.

[1291] "Image recognition means" is a technology that analyzes image data acquired by sensors or cameras and identifies specific objects.

[1292] The "location information acquisition means" is a technology for acquiring location information of an object identified by the image recognition means and transmitting the information to a server.

[1293] The "audio generating means" is a technology that generates an audio signal to repel a specified target and issues a warning sound to the target.

[1294] "Autonomous robot means" refers to robot technology that automatically moves based on location information received from a server, and tracks and exterminates targets.

[1295] "Data analysis means" refers to technology for analyzing the ecological information of a subject and identifying behavioral patterns.

[1296] "Emotion recognition means" is a technology for analyzing the user's emotional state and adjusting the system's behavior.

[1297] The "position information acquisition device" is a device that is attached to an object captured by an autonomous robot means and that tracks the position information of the object.

[1298] "Speech recognition means" is a technology that uses a highly sensitive microphone to detect and analyze the target voice.

[1299] A "suspicious person" refers to a person who poses a security risk and is an object identified by the system.

[1300] "Dangerous creatures" refer to animals that may pose a threat to the safety of the facility or surrounding area, and are objects that are detected by the system.

[1301] The invention is a pest control and security enhancement system that consists of the following major components:

[1302] 1. Real-time image acquisition

[1303] The camera on the device (smartphone) is turned on and video data from the site is acquired in real time. The video data is then sent to a cloud server (AWS, Google Cloud, etc.) at regular intervals.

[1304] 2. Image Recognition

[1305] The server inputs the received video data into image recognition AI (TensorFlow, OpenCV, etc.) to identify suspicious individuals or dangerous creatures. Location information of suspicious individuals or dangerous creatures is generated, recorded in a database, and displayed on the management screen.

[1306] 3. Audio alert

[1307] The device constantly monitors environmental sounds using a highly sensitive microphone and transmits audio data to a server at regular intervals.

[1308] The server inputs the received voice data into a voice recognition AI (Google Speech-to-Text, DeepSpeech, etc.) to identify the voices of pests or suspicious people.

[1309] The server transmits an alert sound generation command to the terminal based on the identified location information.

[1310] The device will play the specified warning sound to repel the target.

[1311] 4. Autonomous Robots

[1312] The server transmits the location information of the detected target to an autonomous robot (such as a DJI drone).

[1313] The autonomous robot automatically moves to the destination based on the received location information and monitors the surroundings with sensors and cameras. After confirming the target, it begins tracking and extermination. After extermination is complete, it sends a completion report to the server.

[1314] 5. Data Analysis

[1315] The server collects target and extermination data from environmental sensors and cameras, and inputs it into a data mining AI to analyze behavioral patterns and preferred environments.

[1316] Based on the analysis results, effective eradication and prevention measures are developed.

[1317] The user attaches a GPS tracker to the captured target, and the server tracks the target's location in real time. The server analyzes the GPS data, identifies the target's movement patterns, and plans the next extermination.

[1318] 6. Emotion recognition

[1319] The server inputs the user's voice and video data into an emotion recognition engine (IBM Watson, Azure Cognitive Services, etc.) for analysis.

[1320] The emotion recognition engine recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety.

[1321] The server automatically adjusts the system's behavior and the intensity and type of alert sounds based on the user's emotional state.

[1322] Specific examples

[1323] Example of target detection using image recognition

[1324] 1. Server: "Received video data from the camera and identified a suspicious individual. Location information: latitude 35.1234, longitude 137.5678. Display the detection results on the management screen."

[1325] Example of user support using emotion recognition

[1326] 2. Server: "Inputting user's voice and video data into the emotion recognition engine."

[1327] 3. Emotion recognition engine: "Identify when the user is stressed. Adjust the system's behavior and reduce the intensity of the warning sounds."

[1328] Example prompts

[1329] prompt:

[1330] "Analyze the video data received from the surveillance cameras and generate a code to identify suspicious individuals or dangerous creatures."

[1331] Generation example:

[1332] import cv2

[1333] Loading an image recognition model

[1334] model = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'graph.pbtxt')

[1335] def detect_objects(image):

[1336] Preprocessing of input images

[1337] image_blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300),

[1338] mean=(104.0, 177.0, 123.0))

[1339] model.setInput(image_blob)

[1340] detections = model.forward()

[1341] return detections

[1342] Capture camera data

[1343] cap = cv2.VideoCapture(0)

[1344] while True:

[1345] ret, frame = cap.read()

[1346] if not ret:

[1347] break

[1348] detections = detect_objects(frame)

[1349] Processing the findings

[1350] for i in range(detections.shape[2]):

[1351] confidence = detections[0, 0, i, 2]

[1352] if confidence > 0.7:

[1353] Obtaining the bounding box of an object

[1354] box = detections[0, 0, i, 3:7] np.array([frame.shape[1], frame.shape[0],

[1355] frame.shape[1], frame.shape[0]])

[1356] (x, y, x1, y1) = box.astype("int")

[1357] cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)

[1358] cv2.imshow('Frame', frame)

[1359] if cv2.waitKey(1) & 0xFF == ord('q'):

[1360] break

[1361] cap.release()

[1362] cv2.destroyAllWindows()

[1363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1364] Step 1:

[1365] Real-time video data acquisition

[1366] The device (smartphone) activates the camera and captures video data in real time. This video data is sent to a cloud server at regular intervals. Specifically, the camera sensor captures environmental information and saves the video data in a specific folder on the device. The saved data is then uploaded to the cloud server.

[1367] Input: Video data from the camera sensor

[1368] Output: Video data sent to the cloud server

[1369] Step 2:

[1370] Analyzing image data and identifying objects

[1371] The server inputs the received video data into an image recognition AI to identify suspicious individuals or dangerous creatures. Specifically, it uses an image recognition model (e.g., TensorFlow, OpenCV) to detect specific patterns based on pixel information in the video. The model also generates location information for the target object and records this information in a database.

[1372] Input: Video data stored on a cloud server

[1373] Output: Location information of identified objects and their associated data

[1374] Step 3:

[1375] Environmental sound monitoring and audio data acquisition

[1376] The device constantly monitors environmental sounds using a highly sensitive microphone and transmits the audio data to a server at regular intervals. Specifically, the microphone captures the environmental sounds and the audio data is stored on the device. The stored data is then uploaded to a cloud server.

[1377] Input: Environmental sound data from a high-sensitivity microphone

[1378] Output: Audio data sent to the cloud server

[1379] Step 4:

[1380] Analyzing voice data and identifying objects

[1381] The server inputs the received audio data into a voice recognition AI to identify the voices of pests or suspicious individuals. Specifically, it uses a voice recognition model (e.g., Google Speech-to-Text, DeepSpeech) to analyze sound wave patterns and recognize specific sounds. The model also simultaneously generates location information for the target object and records this information in a database.

[1382] Input: Audio data stored on a cloud server

[1383] Output: Location information of identified objects and their associated data

[1384] Step 5:

[1385] Generates warning sounds

[1386] The server sends an alarm sound generation command to the device based on the identified location information. The device executes the received alarm sound generation command and plays the specified alarm sound. Specifically, the server generates the alarm sound using a speech synthesis engine and sends the command to the device. The device plays the alarm sound using its built-in speaker.

[1387] Input: Location information of identified object and command to generate warning sound

[1388] Output: Generated warning sound

[1389] Step 6:

[1390] Autonomous Robot Control

[1391] The server sends the location information of the detected object to the autonomous robot. Based on the location information received from the server, the autonomous robot automatically moves to its destination and monitors its surroundings with sensors and cameras. Specifically, the server generates robot control commands and sends them to the robot's control system. The robot begins operation, and after reaching the designated area, it uses sensors to identify the object, and then tracks and exterminates it.

[1392] Input: Position information of identified objects and robot control commands

[1393] Output: Object tracking and removal

[1394] Step 7:

[1395] Data analysis and behavioral pattern identification

[1396] The server collects object and extermination data collected from environmental sensors, cameras, and audio data, and inputs it into a data-mining AI to analyze behavioral patterns and preferred environments. Specifically, the AI ​​model extracts the object's past behavioral data from a database and identifies its movement patterns and behavioral characteristics. The analysis results are used to develop new extermination plans.

[1397] Input: Collected object and removal data

[1398] Output: Behavioral patterns and new eradication plan

[1399] Step 8:

[1400] User Emotion Recognition and Support

[1401] The server inputs the user's voice and video data into an emotion recognition engine for analysis. The emotion recognition engine recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety. Specifically, the server compares the user's past emotional data with their current data and recommends the optimal action. The system then makes automatic adjustments based on this.

[1402] Input: User's audio and video data

[1403] Output: User's emotional state and adjust the system accordingly

[1404] Through the above steps, the present invention can effectively exterminate pests and improve security, while also reducing the psychological burden on users.

[1405] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1406] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1407] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1408] [Third embodiment]

[1409] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1410] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1411] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1412] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1413] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1414] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1415] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1416] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1417] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1418] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1419] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1420] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1421] The present invention is a system for efficient and sustainable pest control that combines image recognition, voice recognition, autonomous robots, and data analytics to consistently detect, track, and eliminate pests.

[1422] System Configuration

[1423] The system consists of the following main components:

[1424] 1. Sensor or camera

[1425] Multiple sensors or cameras are installed in agricultural fields or protected areas of crops to capture real-time video and audio data of the environment.

[1426] 2. Image Recognition Methods

[1427] It includes algorithms for analyzing image data obtained from sensors or cameras and identifying pests.

[1428] 3. Location information acquisition means

[1429] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[1430] 4. Voice Generation Method

[1431] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[1432] 5. Autonomous Robotic Means

[1433] It has the ability to receive location information from the server and track and exterminate pests.

[1434] 6. Data Analysis Methods

[1435] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[1436] Program processing explanation

[1437] Detecting pests using image recognition AI

[1438] Server: Receives video data sent from sensors or cameras. Uses image recognition AI to detect specific pests in the video data. For example, if surveillance cameras are installed around a field and a wild boar appears in the video, it will be identified and its location will be generated.

[1439] Server: Records the location information of identified pests and displays it on the management screen, allowing for monitoring of their entry into crop protection areas and immediate response.

[1440] Keeping pests away with AI-powered audio alerts

[1441] Device: Monitors surrounding sounds with a highly sensitive microphone and captures audio data in real time.

[1442] Server: Using voice recognition AI, identifies the sounds of pests from the acquired audio data. If a specific pest sound is recognized, it generates its location information and sends a command to the device to generate an alarm sound.

[1443] Device: Plays a specified alarm sound to scare pests away from the extermination area. For example, a specific frequency of sound can be emitted to scare pests away from the area.

[1444] Autonomous robots track and eradicate pests

[1445] Server: Sends the location information of detected pests to the autonomous robot.

[1446] Autonomous robot: Based on location information, it moves in real time and tracks pests. It continuously identifies the location of pests using sensors and cameras, and continues to track them until extermination is complete.

[1447] Autonomous robots: They capture and exterminate pests, for example by using a net to capture them while tracking them or by using other extermination methods, and then send a completion report to a server after the extermination is complete.

[1448] Understanding the ecology of pests through data analysis

[1449] Server: Collects data on pest behavior and analyzes it using data mining AI. This allows the behavioral patterns and movement routes of pests to be identified and reflected in extermination plans.

[1450] Server: Captured pests are fitted with GPS trackers to track their location in real time, allowing for analysis of their movement patterns and the development of more effective extermination methods.

[1451] Specific examples

[1452] Example of pest detection using image recognition AI

[1453] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[1454] An example of keeping pests away using voice alert AI

[1455] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[1456] Server: "Send alarm command to device."

[1457] Terminal: "Play specified alarm sound. Keeps pests away."

[1458] An example of tracking and exterminating pests using an autonomous robot

[1459] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[1460] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[1461] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[1462] Example of understanding behavioral patterns through data analysis

[1463] Server: "We are currently analyzing extermination data using data mining AI. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[1464] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[1465] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[1466] In this way, a system is provided in which each element works together to efficiently exterminate harmful animals.

[1467] The processing flow will be explained below.

[1468] Program processing flow

[1469] Detecting pests using image recognition AI

[1470] Step 1:

[1471] Terminal: "Acquire video data from surveillance cameras in real time."

[1472] Step 2:

[1473] Terminal: "Send the acquired video data to the server at regular intervals."

[1474] Step 3:

[1475] Server: "Input the received video data into the image recognition AI."

[1476] Step 4:

[1477] Server: "Image recognition AI analyzes video data to identify pests. For example, it can identify the shapes of wild boars and deer."

[1478] Step 5:

[1479] Server: "Generate location information (latitude, longitude) for identified pests."

[1480] Step 6:

[1481] Server: "Record the generated location information in the database and display it on the management screen."

[1482] Keeping pests away with AI-powered audio alerts

[1483] Step 1:

[1484] Device: "High-sensitivity microphone constantly monitors environmental sounds."

[1485] Step 2:

[1486] Terminal: "Send the acquired audio data to the server at regular intervals."

[1487] Step 3:

[1488] Server: "Input the received voice data into the voice recognition AI."

[1489] Step 4:

[1490] Server: "The voice recognition AI analyzes the audio data to identify the sounds of pests. For example, it can identify the sounds of wild boars."

[1491] Step 5:

[1492] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[1493] Step 6:

[1494] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[1495] Autonomous robots track and eradicate pests

[1496] Step 1:

[1497] Server: "Send the location of detected pests to the autonomous robot."

[1498] Step 2:

[1499] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[1500] Step 3:

[1501] Autonomous robots: "Once they arrive, they monitor the surrounding area using sensors and cameras to locate pests."

[1502] Step 4:

[1503] Autonomous robots: "Prepare means (nets, tranquilizer guns, etc.) to track and capture pests."

[1504] Step 5:

[1505] Autonomous robots: "Capture and reliably eliminate pests."

[1506] Step 6:

[1507] Autonomous robot: "Reports to the server that disinfection is complete."

[1508] Understanding the ecology of pests through data analysis

[1509] Step 1:

[1510] Server: "Stores pest and extermination data collected from sensors and cameras in a database."

[1511] Step 2:

[1512] Server: "The collected data is fed into a data mining AI."

[1513] Step 3:

[1514] Server: "Data mining AI will analyze pest behavior patterns and preferred environments."

[1515] Step 4:

[1516] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[1517] Step 5:

[1518] User: "I'll put a GPS tracker on the captured vermin."

[1519] Step 6:

[1520] GPS tracker: "Sends real-time location information of pests to a server."

[1521] Step 7:

[1522] Server: "Analyze GPS data to understand pest movement patterns."

[1523] Step 8:

[1524] Server: "Plan the next cleanup and notify the user."

[1525] The above is a specific processing flow for carrying out the present invention, which makes it possible to provide a system that realizes efficient and sustainable pest control.

[1526] Example 1

[1527] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1528] Pests cause serious damage to agricultural crops, and traditional extermination methods are difficult to implement efficiently and sustainably. Furthermore, there are insufficient means to monitor pest intrusions in real time and respond quickly, which means the damage tends to spread. To solve these problems, a system is needed that can detect, track, and exterminate pests in an integrated manner, while analyzing their behavioral patterns to prevent future intrusions.

[1529] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1530] In this invention, the server includes image recognition means for acquiring image data using a sensor or camera and analyzing the image data, location information acquisition means for acquiring location information of a target identified by the image recognition means, sound generation means for generating sound to repel the target identified by the image recognition means, autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, data analysis means for analyzing the target's biological information and identifying its behavioral pattern, communication means for transmitting the acquired video and audio data to the server in real time, and command generation means for generating an alarm sound to be played and transmitting it to a terminal. This makes it possible to perform a consistent process from detecting, tracking, and exterminating pests to analyzing their behavior.

[1531] "Sensor or camera" means a device for acquiring still or video images of the environment and capable of collecting data in real time.

[1532] "Image recognition means" includes algorithms and technologies for analyzing acquired image data and identifying and specifying specific objects (e.g., pests) from within the data.

[1533] The "location information acquisition means" is a means for measuring the location of an object identified by the image recognition means and generating and acquiring that information.

[1534] "Sound generation means" refers to a device or technology that generates warning sounds or the like based on identified sound data in order to keep a specific target away.

[1535] "Autonomous robot means" refers to a type of robot that has the ability to move automatically based on location information sent from a server and track and eliminate specific targets.

[1536] "Data analysis means" refers to systems or technologies that collect and analyze behavioral data of subjects to extract specific patterns and information.

[1537] "Communication means" refers to technology or equipment for transmitting acquired video and audio data to a server in real time, and has the ability to exchange data via a network.

[1538] The "command generation means" is a mechanism that generates commands to instruct the terminal to execute an alarm or other processing based on the data analyzed by the server.

[1539] The present invention relates to a system for efficiently and sustainably eradicating pests in protected areas of agricultural crops, which is composed of a combination of sensors, cameras, image recognition means, autonomous robots, data analysis means, communication means, and sound generation means.

[1540] Hardware and software used

[1541] The specific hardware used in this system includes high-resolution cameras, high-sensitivity microphones, cloud servers, autonomous robots, and GPS trackers. Image recognition is performed using image recognition AI algorithms such as YOLOv5. Voice recognition is performed using deep learning-based voice recognition AI.

[1542] System Operation Overview

[1543] 1. Sensor or camera placement and data acquisition

[1544] The sensors or cameras are installed in agricultural fields and protected areas of agricultural crops to capture real-time video and audio data of the surrounding area. In particular, all-weather, high-resolution cameras and highly sensitive microphones are used to monitor pests that target crops.

[1545] 2. Analysis of video data

[1546] The acquired video data is sent to a server. The server receives the video data in real time and uses image recognition techniques (such as YOLOv5) to detect pests. Once a detected pest is identified, its location information is automatically generated and displayed on the management screen.

[1547] 3. Analysis of audio data and generation of warning sounds

[1548] The device uses a highly sensitive microphone to collect surrounding sounds and transmits them to a server in real time. The server then uses a voice recognition tool to analyze the audio data and detect the sounds of specific pests. Based on the detected sounds, a command to generate an alarm sound is sent to the device and played back. This frightens pests away from the extermination area.

[1549] 4. Autonomous robots for tracking and eliminating pests

[1550] The server sends the location information of the detected pests to the autonomous robot. The autonomous robot moves in real time based on the received location information and tracks the pests. During tracking, it continuously identifies the location of the pests using sensors and cameras, and carries out extermination using nets, etc. Once extermination is complete, a completion report is sent to the server.

[1551] 5. Data analysis and behavioral pattern understanding

[1552] The server collects data on the behavior of pests and analyzes it using data analysis tools. This allows the behavioral patterns and movement routes of pests to be identified and reflected in the next extermination plan. In addition, GPS trackers are attached to captured pests to track their location in real time. This allows for the development of even more effective extermination methods.

[1553] Specific examples

[1554] Example of pest detection using image recognition AI

[1555] Server: "Receiving video data from camera 1. Identified a wild boar. Generate location information: latitude 35.1234, longitude 137.5678, and display it on the management screen."

[1556] An example of keeping pests away using voice alert AI

[1557] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[1558] Server: "Send a command to generate an alarm to the device."

[1559] Terminal: "Play specified alarm sound. Keeps pests away."

[1560] An example of tracking and exterminating pests using an autonomous robot

[1561] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[1562] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[1563] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[1564] Example of understanding behavioral patterns through data analysis

[1565] Server: "We are currently analyzing the extermination data using data analysis tools. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[1566] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[1567] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[1568] Prompt Sentence Examples

[1569] "One night in October, an IoT sensor detected the movement of a wild boar in a protected area for agricultural crops. Image recognition AI analyzed the footage to identify the boar and generate location information. An autonomous robot then used the location information to track the boar and capture it with a net. Please describe this entire process."

[1570] The system of the present invention provides a comprehensive solution for efficient and sustainable pest control, allowing users to monitor and control in real time through an administrative interface.

[1571] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1572] Step 1:

[1573] Initial setup and sensor preparation

[1574] Server: Checks the setting information of sensors and cameras, and verifies that each device is working properly. It distributes IP addresses and setting information to each device to ensure communication is established.

[1575] Input: Sensor and camera settings, device ID

[1576] Output: Device operation check result, device setting completion notification

[1577] Specific operation: The server sends the configuration information to each sensor, and once normal operation is confirmed, it receives a "configuration complete" notification.

[1578] Terminal: Check the installation location of sensors and cameras and place them appropriately. The installation locations should be set in agricultural land and crop protection areas.

[1579] Input: Location information, physical layout of sensors and cameras

[1580] Output: Placement completion notification

[1581] What it does: The device physically places cameras and sensors in farmland or protected areas and fixes them at appropriate angles.

[1582] Users: Monitor the operation of sensors and cameras through the admin panel and check for any issues.

[1583] Input: Sensor / camera status information on the management screen

[1584] Output: Results of normal operation of each device

[1585] What happens: The user checks the admin panel and confirms that all devices are working properly.

[1586] Step 2:

[1587] Acquisition and transmission of video data

[1588] Sensors / cameras: Capture real-time video data from the area where they are installed, with a particular focus on pest monitoring.

[1589] Input: Real-time video and audio from the scene

[1590] Output: Get raw data

[1591] Specific operation: Sensors and cameras continuously record and acquire video and audio within a specified range.

[1592] Terminal: Compresses the captured video data and sends it to the server, improving data transfer efficiency.

[1593] Input: Raw data (video and audio)

[1594] Output: Compressed data, notification of completion

[1595] Specific operation: The device compresses the video and audio data and uploads it to the server.

[1596] Step 3:

[1597] Image recognition for detecting pests

[1598] Server: Analyzes the received video data and runs image recognition AI to detect specific pests (e.g., wild boars).

[1599] Input: Compressed video data

[1600] Output: Identification and location information of detected pests

[1601] Specific operation: The server analyzes the video data, detects pests using YOLOv5, and generates location information.

[1602] Server: Generates location information of pests and displays it on the management screen.

[1603] Input: Detected pest's identity and location

[1604] Output: Location information generated and displayed on the management screen

[1605] Specific operation: The server displays the location information of the pests on the management screen, allowing the user to check it in real time.

[1606] Step 4:

[1607] Acquisition and analysis of audio data

[1608] Device: A highly sensitive microphone captures surrounding audio data and transmits it to the server in real time.

[1609] Input: Real-time audio from the site

[1610] Output: Acquired audio data, transmission completion notification

[1611] Specific operation: The device picks up sound with a high-sensitivity microphone and sends the data to the server.

[1612] Server: Analyzes the captured audio data and uses voice recognition AI to identify the sounds of pests.

[1613] Input: Captured audio data

[1614] Output: Enemy call identification result, location information

[1615] Specific operation: The server analyzes the audio data and uses deep learning to identify the sounds of pests and generate location information.

[1616] Step 5:

[1617] Audio warning and pest repellent

[1618] Server: Based on the detected sounds, sends a command to the device to generate an alarm sound.

[1619] Input: Call identification result, location information

[1620] Output: Command to generate warning sound

[1621] Specific operation: The server generates an alarm command and sends it to the device.

[1622] Terminal: Receives an alert sound generation command from the server and plays the specified alert sound.

[1623] Input: Command to generate warning sound

[1624] Output: Warning sound played, distancing result

[1625] Specific action: The device plays an alarm sound to scare away pests.

[1626] Step 6:

[1627] Autonomous robots track and eradicate pests

[1628] Server: Sends the location information of detected pests to the autonomous robot.

[1629] Input: Location of detected pest

[1630] Output: Location information sending completion notification

[1631] Specific operation: The server sends location information to the autonomous robot.

[1632] Autonomous robot: Moves in real time based on received location information and tracks pests.

[1633] Input: Location

[1634] Output: Pest capture results, extermination report

[1635] Specific operation: The autonomous robot moves based on location information, tracks and captures pests.

[1636] Autonomous robot: Reports to the server once disinfection is complete.

[1637] Input: Disinfection completion information

[1638] Output: Report of disinfection completion to the server

[1639] Specific operation: The autonomous robot sends a report of the completion of eradication to the server.

[1640] Step 7:

[1641] Data analysis and understanding of behavioral patterns

[1642] Server: Collects behavioral data of pests and analyzes it using data analysis tools.

[1643] Input: Behavioral data obtained during removal

[1644] Output: Identification of behavioral patterns

[1645] Specific operation: The server analyzes the behavioral data and identifies behavioral patterns and movement routes.

[1646] Server: GPS trackers are attached to captured pests to track their location in real time.

[1647] Input: Captured pest information, GPS tracker

[1648] Output: GPS data, behavioral patterns

[1649] What it does: The server analyzes the GPS data and develops an effective removal method.

[1650] With detailed processing at each step, the system can detect, track, exterminate and analyze pests in an integrated manner.

[1651] (Application example 1)

[1652] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1653] Conventional pest control systems did not consistently detect, track, and exterminate pests, making it difficult to respond in a timely manner. Furthermore, when a pest invades a factory, it is difficult to understand its entry route and behavioral patterns, making it impossible to develop an efficient extermination method. Another issue is that there is no interface that allows users to check the current location of pests in real time, making it difficult to respond quickly.

[1654] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1655] In this invention, the server includes an image recognition means that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition means that acquires location information of an object identified by the image recognition means, a sound generation means that generates sound to repel the object identified by the image recognition means, an autonomous robot means that automatically moves to track and exterminate the object based on the location information of the object, a data analysis means that analyzes the object's biological information and identifies its behavioral pattern, and a user interface means that provides an interface that allows a user to check the video data acquired from the sensor or camera and the sound data detected by the sound recognition means in real time. This makes it possible to perform an integrated process from detection to extermination of pests, and to understand the entry routes and behavioral patterns of pests within a factory, thereby enabling quick and efficient extermination.

[1656] A "sensor" is a device that detects changes in the physical environment and transmits them as a signal.

[1657] A "camera" is a device that captures video or images and stores and transmits them as digital data.

[1658] "Image recognition means" refers to a device that includes an algorithm or program for analyzing image data obtained from a sensor or camera and identifying a specific object.

[1659] The "location information acquisition means" is a device that identifies the location of the object identified by the image recognition means, and acquires and transmits the location information.

[1660] The "audio generating means" is a device that generates audio based on preset conditions and plays it back.

[1661] An "autonomous robotic means" is a robot that has the ability to move automatically based on location information and track and eliminate identified targets.

[1662] "Data analysis means" is a system for analyzing collected data and identifying the behavioral patterns and ecological information of the subject.

[1663] The "user interface means" is a device that provides an interface that allows a user to check the video data and audio data obtained from the sensor or camera in real time.

[1664] "Speech recognition means" is a device that includes an algorithm or program for analyzing voice data acquired by a high-sensitivity microphone and identifying the voice of a specific target.

[1665] A "GPS tracker" is a device that obtains and tracks location information in real time.

[1666] This invention is a system for efficiently exterminating pests in factories. This system is realized by combining hardware such as sensors, cameras, high-sensitivity microphones, and autonomous robots with software such as image recognition AI, voice recognition AI, and data analysis AI.

[1667] Hardware Configuration

[1668] Sensors or cameras: Installed in various locations within the factory, they capture video and audio data in real time.

[1669] High-sensitivity microphone: Collects sounds within the factory and detects the cries of pests.

[1670] Autonomous robots: They move automatically based on the location of pests to track and exterminate them.

[1671] GPS tracker: Attached to pests captured by autonomous robots, it tracks their location in real time.

[1672] Software Configuration

[1673] Image recognition AI (e.g., TensorFlow): Analyzes video data obtained from sensors or cameras to detect pests.

[1674] Speech recognition AI (e.g., Google Speech Recognition): Analyzes audio data collected by a highly sensitive microphone and identifies the sounds of pests.

[1675] Data analysis AI (e.g., SciKit-Learn): Analyzes behavioral patterns and ecological information of pests and reflects this in extermination plans.

[1676] User interface: Provides an interface that allows users to view video and audio data in real time via smartphones, tablets, or smart glasses.

[1677] Specific operation of the system

[1678] The server receives video data from sensors or cameras and uses image recognition AI to detect pests. For example, if a camera installed in a factory captures a wild boar in the video, it analyzes the data and generates location information. This location information is sent in real time to an autonomous robot, which then begins tracking the pest.

[1679] The server also uses voice recognition AI to analyze audio data obtained from a highly sensitive microphone, and if it detects the cries of a pest, it obtains its location information. Based on this location information, a sound generator generates an alarm sound to keep the pest away.

[1680] Using the provided user interface, users can grasp the current situation in real time and take necessary measures. For example, they can check the footage from the surveillance cameras in the factory on their smartphones and take immediate action if a pest is detected.

[1681] Specific examples

[1682] Pest detection: "The model analyzed the video data from the camera and detected a wild boar. Its location is latitude 35.1234, longitude 137.5678. An instruction has been sent to the robot."

[1683] Roar detection: "A highly sensitive microphone has detected the sound of a wild boar roaring. An alarm sound will be played to keep the boar away from the factory."

[1684] User notification: "We have reviewed the footage from the surveillance camera and detected a pest. The robot will now move to the specified location and begin extermination."

[1685] These functions enable the rapid detection of pest intrusions and their efficient extermination, making this a very useful system for factories where real-time confirmation and rapid response are required.

[1686] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1687] Step 1:

[1688] The server acquires video data in real time from sensors or cameras installed within the factory.

[1689] Input: Video data from a sensor or camera

[1690] Output: Video data passed to image recognition AI

[1691] How it works: The cameras continuously capture video at various locations in the factory and send the data to a server.

[1692] Step 2:

[1693] The server inputs the acquired video data into image recognition AI and performs analysis to detect pests.

[1694] Input: Video data acquired in the previous step

[1695] Output: Pest identification and location

[1696] How it works: Image recognition AI detects features such as people and vehicles in video data and determines whether it contains any vermin. For example, if it detects a video containing a wild boar, it generates its location information.

[1697] Step 3:

[1698] The server obtains the location information of pests detected by image recognition AI and transmits it to the autonomous robot.

[1699] Input: Pest identification and location

[1700] Output: Movement instructions for the autonomous robot

[1701] Specific operation: Based on the obtained location information, the server issues instructions to the autonomous robot, causing it to move in order to capture the pest.

[1702] Step 4:

[1703] The autonomous robots move automatically based on the transmitted location information to track and eliminate pests.

[1704] Input: Location information and movement instructions from the server

[1705] Output: Report of pest capture and extermination completion

[1706] Specific behavior: The robot moves towards the designated location, continuously locates the pest using pest tracking sensors and cameras, and then performs extermination measures, such as capturing the pest with a net or using appropriate extermination methods.

[1707] Step 5:

[1708] The server collects real-time audio data from high-sensitivity microphones installed within the factory.

[1709] Input: Audio data from a high-sensitivity microphone

[1710] Output: Voice data passed to the voice recognition AI

[1711] How it works: Highly sensitive microphones collect audio from various locations within the factory and send the data to a server.

[1712] Step 6:

[1713] The server uses voice recognition AI to analyze the acquired audio data and detect the cries of specific pests.

[1714] Input: Audio data obtained in the previous step

[1715] Output: Sound detection results and location information of pests

[1716] How it works: The voice recognition AI analyzes unique sound patterns contained in the audio data to detect the calls of specific pests (such as wild boars). If detected, it generates location information.

[1717] Step 7:

[1718] The server generates and reproduces an alarm sound for keeping away harmful animals from the sound generating means based on the location information of the harmful animal's cry.

[1719] Input: Call detection results and location information of pests

[1720] Output: Generates and plays warning sounds

[1721] Specific behavior: The server generates a specified alarm sound and uses surrounding speakers to play sounds of frequencies that pests dislike, thereby keeping pests away.

[1722] Step 8:

[1723] The user uses the provided user interface to check the video data and audio data in real time.

[1724] Input: Real-time data (video and audio) from the server

[1725] Output: what is displayed in the user interface

[1726] Specific operation: Users operate the interface via smartphones, tablets, or smart glasses to monitor the situation in the factory in real time and take appropriate action if necessary.

[1727] Step 9:

[1728] The server uses data analysis AI to analyze the behavioral patterns and ecological information of pests and reflect this in extermination plans.

[1729] Input: Pest location and behavior data

[1730] Output: Behavioral pattern analysis results and eradication plan

[1731] How it works: The data analysis AI uses the collected data to identify the behavioral patterns and ecological information of pests, and then plans more effective extermination methods. The results are notified to the user and incorporated into the next extermination plan.

[1732] These steps enable consistent and efficient detection and extermination of pests, enabling real-time monitoring and rapid response.

[1733] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1734] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and the emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[1735] System Configuration

[1736] The system consists of the following main components:

[1737] 1. Sensor or camera

[1738] Multiple sensors or cameras installed on-site capture video and audio data of the environment in real time.

[1739] 2. Image Recognition Methods

[1740] It includes algorithms for analyzing acquired image data and identifying pests.

[1741] 3. Location information acquisition means

[1742] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[1743] 4. Voice Generation Method

[1744] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[1745] 5. Autonomous Robotic Means

[1746] It has the ability to receive location information from the server and track and exterminate pests.

[1747] 6. Data Analysis Methods

[1748] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[1749] 7. Emotion Engine

[1750] It includes technology for recognizing user emotions and adjusting system behavior.

[1751] Program processing explanation

[1752] Detecting pests using image recognition AI

[1753] Terminal: "Acquire video data from the surveillance camera in real time. Send the video data to the server at regular intervals."

[1754] Server: "The received video data is input into image recognition AI to identify harmful animals. For example, wild boars and deer."

[1755] Server: "Generate location information for identified pests, record it in a database, and display it on the management screen."

[1756] Keeping pests away with AI-powered audio alerts

[1757] Device: "Constantly monitors environmental sounds with a high-sensitivity microphone. The acquired audio data is sent to the server at regular intervals."

[1758] Server: "The received audio data is input into the speech recognition AI to identify the sound of a pest. For example, it identifies the sound of a wild boar."

[1759] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[1760] Device: "Plays an alarm sound of the specified frequency and volume to ward off pests."

[1761] Autonomous robots track and eradicate pests

[1762] Server: "Send the location of detected pests to the autonomous robot."

[1763] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[1764] Autonomous robots: "Once they arrive, they monitor the surroundings with sensors and cameras to identify pests. They then begin tracking and exterminating them."

[1765] Autonomous robot: "Captures pests and exterminates them reliably. Once extermination is complete, it sends a completion report to the server."

[1766] Understanding the ecology of pests through data analysis

[1767] Server: "Collects pest and extermination data collected from environmental sensors and cameras."

[1768] Server: "The collected data is fed into a data-mining AI to analyze the behavioral patterns and preferred environments of pests."

[1769] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[1770] User: "I'll put a GPS tracker on the captured vermin."

[1771] GPS Tracker: "Tracks the location of pests in real time and sends it to a server."

[1772] Server: "We analyze GPS data to identify pest movement patterns and plan the next eradication."

[1773] User support with emotion engine

[1774] Server: "Input the user's voice and video data into the emotion engine and analyze it."

[1775] Emotion Engine: "Recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety."

[1776] Server: "Automatically adjust system behavior and alert intensity and type based on the user's emotional state."

[1777] Specific examples

[1778] Example of pest detection using image recognition AI

[1779] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[1780] An example of keeping pests away using voice alert AI

[1781] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[1782] Server: "Send a command to generate an alarm to the device."

[1783] Terminal: "Play the specified alarm sound to keep pests away."

[1784] An example of tracking and exterminating pests using an autonomous robot

[1785] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[1786] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[1787] Autonomous robot: "Captures pests and traps them in a net. Extermination complete. Sends a completion report to the server."

[1788] Example of user support using emotion engine

[1789] Server: "Inputting user's audio and video data into the emotion engine."

[1790] Emotion engine: "Identify when the user is stressed. Adjust system behavior and reduce the intensity of alert sounds."

[1791] Server: "Notify user of response. Taking action based on emotional state."

[1792] This system not only enables efficient pest control, but also reduces the psychological burden on users.

[1793] The processing flow will be explained below.

[1794] Program processing flow

[1795] Detecting pests using image recognition AI

[1796] Step 1:

[1797] Terminal: "Activate the surveillance camera and capture video data in real time."

[1798] Step 2:

[1799] Terminal: "Sends acquired video data to the server at regular intervals."

[1800] Step 3:

[1801] Server: "Input the received video data into the image recognition AI."

[1802] Step 4:

[1803] Server: "Image recognition AI analyzes video data and identifies specific pests (e.g., wild boars and deer)."

[1804] Step 5:

[1805] Server: "Generate location information (latitude, longitude) for identified pests."

[1806] Step 6:

[1807] Server: "Record the generated location information in the database and display it on the management screen."

[1808] Keeping pests away with AI-powered audio alerts

[1809] Step 1:

[1810] Device: "Uses a highly sensitive microphone to constantly monitor the surrounding environment."

[1811] Step 2:

[1812] Device: "Send the acquired audio data to the server at regular intervals."

[1813] Step 3:

[1814] Server: "Input the received voice data into the voice recognition AI."

[1815] Step 4:

[1816] Server: "The voice recognition AI analyzes the audio data and identifies the sounds of specific pests (e.g., wild boars)."

[1817] Step 5:

[1818] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[1819] Step 6:

[1820] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[1821] Autonomous robots track and eradicate pests

[1822] Step 1:

[1823] Server: "Send the location of detected pests to the autonomous robot."

[1824] Step 2:

[1825] Autonomous robot: "Autonomously moves to the destination based on location information received from the server."

[1826] Step 3:

[1827] Autonomous robot: "After reaching its destination, it monitors its surroundings with sensors and cameras to reconfirm the location of the pest."

[1828] Step 4:

[1829] Autonomous robot: "Prepare means to capture pests (e.g., nets, tranquilizer guns)."

[1830] Step 5:

[1831] Autonomous robots: "Reliably capture and exterminate pests."

[1832] Step 6:

[1833] Autonomous robot: "Reports to the server that disinfection is complete."

[1834] Understanding the ecology of pests through data analysis

[1835] Step 1:

[1836] Server: "Stores pest and extermination data collected from environmental sensors and cameras in a database."

[1837] Step 2:

[1838] Server: "Feed the stored data into the data mining AI."

[1839] Step 3:

[1840] Server: "Data mining AI analyzes pest behavior patterns and migration routes."

[1841] Step 4:

[1842] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[1843] Step 5:

[1844] User: "We'll attach a GPS tracker to the captured pest."

[1845] Step 6:

[1846] GPS tracker: "Sends real-time location information of pests to a server."

[1847] Step 7:

[1848] Server: "Analyze GPS data to identify pest movement patterns."

[1849] Step 8:

[1850] Server: "Based on the identified movement patterns, create a plan for the next eradication and notify the user."

[1851] User support with emotion engine

[1852] Step 1:

[1853] Server: "Input the user's voice and video data into the emotion engine."

[1854] Step 2:

[1855] Emotion engine: "Analyzes audio and video data to identify the user's emotional state. For example, identifying stress or anxiety."

[1856] Step 3:

[1857] Server: "Adjust the system's behavior based on the emotion engine's analysis. For example, change the intensity or type of alarm sound."

[1858] Step 4:

[1859] Server: "Propose countermeasures based on the user's emotional state and notify the user when the countermeasures are implemented."

[1860] The above is a specific processing flow for carrying out the present invention, which not only enables efficient pest control but also reduces the psychological burden on the user.

[1861] Example 2

[1862] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1863] Conventional pest control systems have difficulty detecting and eliminating pests efficiently and sustainably. Furthermore, they lack functionality to reduce the mental stress and anxiety users feel when using the system. A means to accurately grasp the behavioral patterns of pests and efficiently eliminate them is needed, as well as functionality to provide psychological support to users.

[1864] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1865] In this invention, the server includes an image recognition means for acquiring image data using a sensor or a camera and analyzing the image data, a location information acquisition means for acquiring location information of a target identified by the image recognition means, a sound generation means for generating sound to repel the target identified by the image recognition means, an autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, a data analysis means for analyzing the target's ecological information and identifying its behavioral pattern, a sound recognition means for analyzing the target's sound and generating an alarm sound, and an emotion engine for recognizing the user's emotional state and adjusting the system's operation. This not only enables consistent and efficient detection, tracking, and extermination of pests, but also reduces the user's mental stress.

[1866] "Sensor or camera" means a device for acquiring real-time video and audio data of the environment.

[1867] "Image recognition means" refers to an algorithm or software that analyzes acquired image data and identifies specific objects or animals (e.g., vermin).

[1868] The "location information acquisition means" is a means for measuring the location information of the object identified by the image recognition means and providing it to the system.

[1869] The "sound generating means" is a device that generates sounds to repel targets, such as collecting environmental sounds using a highly sensitive microphone, and plays specific warning sounds.

[1870] "Autonomous robotic means" refers to a robot that moves automatically based on the location information of the target, and is equipped with an autonomous driving algorithm to track and exterminate the target.

[1871] "Data analysis methods" are methods for collecting ecological information about the subject and analyzing its behavioral patterns and preferred environments. Data mining and machine learning algorithms are used.

[1872] "Speech recognition means" is a means of analyzing voice data acquired by a high-sensitivity microphone and identifying specific sounds (for example, the cries of pests). It often uses a voice feature extraction algorithm.

[1873] The "Emotion Engine" is a technology that analyzes the user's voice and video data, recognizes the user's emotional state, and adjusts the system's behavior accordingly, suggesting actions to reduce stress and anxiety.

[1874] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and an emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[1875] The system consists of the following main components:

[1876] 1. Sensor or camera

[1877] Multiple sensors or cameras installed on-site capture real-time video and audio data of the environment. For example, infrared cameras and high-resolution cameras can be used.

[1878] 2. Image Recognition Methods

[1879] It includes algorithms for analyzing acquired image data and identifying pests. For example, it uses object detection models such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector). The server processes the image data sent from the device in real time and generates location information for identified pests.

[1880] 3. Location information acquisition means

[1881] The location information of the pests identified by the image recognition means is acquired and sent to a server. The location information is measured using GPS or a geographic information system (GIS).

[1882] 4. Voice Generation Method

[1883] It uses a highly sensitive microphone to pick up ambient sounds, and when it detects the sound of a pest, it generates a sound to scare the pest away, for example, playing the sound of a hunting dog or other warning sounds in a specific frequency band.

[1884] 5. Autonomous Robotic Means

[1885] The autonomous robot receives location information from a server and is capable of tracking and eliminating pests. It is equipped with self-driving technology and can reach its destination while avoiding obstacles. Once it arrives, it monitors its surroundings using cameras and sensors, and exterminates pests after identifying them. For example, it uses nets or traps.

[1886] 6. Data Analysis Methods

[1887] This is a system for collecting and analyzing the behavioral patterns and ecological information of pests. It uses data mining technology and machine learning algorithms (e.g., TensorFlow or PyTorch) to analyze the preferred environments and movement patterns of pests, which makes it possible to plan the next eradication.

[1888] 7. Emotion Engine

[1889] The user's voice and video data are input into the emotion engine for analysis. For example, technology analyzing voice tone and facial expressions is used to recognize the user's emotional state. Based on the analysis results, the system's operation and the intensity of warning sounds are automatically adjusted to reduce the user's mental stress.

[1890] Specific examples

[1891] Example of pest detection using image recognition AI

[1892] Terminal: Obtaining video data from a surveillance camera in real time. Sending the video data to the server at regular intervals.

[1893] Server: Receives video data from the camera and inputs it into image recognition AI (YOLO model). Identifies wild boars.

[1894] Server: Generates the location information of the identified wild boar (latitude 35.1234, longitude 137.5678) and displays it on the management screen.

[1895] An example of keeping pests away using voice alert AI

[1896] Device: High-sensitivity microphone picking up surrounding sounds. The sound of a wild boar has been identified.

[1897] Device: Send the determined location information (latitude 35.1234, longitude 137.5678) to the server.

[1898] Server: Based on the received data, the server sends a command to generate an alarm sound to the terminal.

[1899] Terminal: Plays the specified alarm sound to scare off wild boars.

[1900] An example of tracking and exterminating pests using an autonomous robot

[1901] Server: Send the boar's location information to the autonomous robot (latitude 35.1234, longitude 137.5678).

[1902] Autonomous robot: The robot moves automatically to the specified location.

[1903] Autonomous robot: After arriving, it uses sensors and cameras to identify wild boars and begins extermination. It then captures them using a net.

[1904] Autonomous robot: After the removal is completed, it sends a completion report to the server.

[1905] Example of user support using emotion engine

[1906] Server: Inputting user's audio and video data into the emotion engine.

[1907] Emotion Engine: Identifies when a user is stressed and adjusts system behavior to reduce the intensity of alert sounds.

[1908] Server: Informs the user about system adjustments and takes actions based on their emotional state.

[1909] This system not only enables efficient and sustainable pest control, but also reduces the psychological burden on users.

[1910] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1911] Step 1:

[1912] Acquisition and transmission of image data

[1913] Terminal: Acquires real-time video data from surveillance cameras, such as infrared cameras and high-resolution cameras.

[1914] Input: Live video data of the environment.

[1915] Specific operation: The camera captures video of the environment and captures video frames at regular intervals (e.g., every second).

[1916] Terminal: The acquired video data is sent to the server at regular intervals.

[1917] Output: Video data sent to the server.

[1918] Step 2:

[1919] Vermin detection

[1920] Server: Inputs the received video data into the image recognition AI algorithm.

[1921] Input: Video data sent from the device.

[1922] What it does: The server uses an object detection model such as YOLO or SSD to identify objects in the frame.

[1923] Server: Identifies vermin (e.g., wild boars and deer) and generates their location information.

[1924] Output: Location of identified pests.

[1925] Step 3:

[1926] Sending location information of pests

[1927] Server: Sends location information to the autonomous robot using REST API or MQTT.

[1928] Input: The generated location information.

[1929] Specific operation: The server makes an API call or messaging to send the location information of the identified pest to the robot.

[1930] Output: Location information sent to the autonomous robot.

[1931] Step 4:

[1932] Autonomous robotic mobility and surveillance

[1933] Autonomous robot: Automatically moves to the destination based on the location information received from the server.

[1934] Input: Location information sent by the server.

[1935] Specific behavior: The robot uses an autonomous driving algorithm to reach a specified location while avoiding obstacles.

[1936] Autonomous robot: After arriving at its destination, it monitors the surrounding area with cameras and sensors to reconfirm the presence of pests.

[1937] Output: Confirmed pest presence information.

[1938] Step 5:

[1939] Performing and reporting removal

[1940] Autonomous robot: Performs specialized extermination actions towards identified pests.

[1941] Input: Confirmed pest presence information.

[1942] Specific behavior: The robot captures pests using nets and traps.

[1943] Autonomous robot: When the eradication is completed, it sends the eradication result data to the server as a completion report.

[1944] Output: Cleaning report sent to the server.

[1945] Step 6:

[1946] Acquisition and analysis of audio data

[1947] Device: High-sensitivity microphone constantly monitors surrounding sounds.

[1948] Input: Ambient sounds.

[1949] Specific operation: The microphone collects ambient sounds and stores the audio data in a buffer at regular intervals (for example, every second).

[1950] Device: Sends the saved audio data to the server.

[1951] Output: The audio data sent to the server.

[1952] Step 7:

[1953] Pest sound recognition and alarm sound generation

[1954] Server: Inputs the received voice data into the voice recognition AI.

[1955] Input: Audio data sent from the device.

[1956] Specific operation: The server uses an audio feature extraction algorithm to identify the sound of the pest.

[1957] Server: Based on the identified sounds of pests, it sends instructions to the device to generate alarm sounds to scare away the pests.

[1958] Output: Speech generation instructions.

[1959] Device: Plays an alarm sound at the specified frequency and volume according to the received instructions.

[1960] Output: An alarm sound is played.

[1961] Step 8:

[1962] Data analysis and eradication plan development

[1963] Server: Stores pest and extermination data collected from environmental sensors and cameras in a database.

[1964] Input: Data collected from sensors and cameras.

[1965] What it does: The server uses data mining techniques and machine learning algorithms to analyze the collected data.

[1966] Server: Analyzes pest behavior patterns and preferred environments.

[1967] Output: A removal plan based on the analysis results.

[1968] Step 9:

[1969] User support with emotion engine

[1970] Server: Inputs the user's voice and video data into the emotion engine.

[1971] Input: User audio and video data.

[1972] What it does: The emotion engine analyzes voice tone and facial expressions to identify the user's emotional state.

[1973] Emotion engine: Recognizes the user's emotional state based on the analysis results.

[1974] Output: The user's emotional state.

[1975] Server: Adjusts system behavior and alert sound intensity based on the user's emotional state.

[1976] Output: Adjusted system operation information.

[1977] Through this specific processing step, the system can maximize the effectiveness of pest control and reduce the psychological burden on the user.

[1978] (Application example 2)

[1979] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1980] Conventional pest control systems can detect and track pests, but they are unable to adjust their operation in response to the user's emotional state. This often leads to anxiety and stress during the pest control process. It is also difficult to utilize these systems for security purposes, such as identifying suspicious individuals or dangerous creatures. The purpose of this invention is to solve these problems and provide a more effective and user-friendly system.

[1981] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1982] In this invention, the server includes an image recognition unit that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition unit that acquires location information of an object identified by the image recognition unit, a voice generation unit that generates a voice to repel the object identified by the image recognition unit, an autonomous robot unit that automatically moves to track and exterminate the object based on the location information of the object, a data analysis unit that analyzes the object's ecological information and identifies its behavioral pattern, and an emotion recognition unit that analyzes the user's emotional state and adjusts the system's operation. This enables efficient and sustainable pest extermination and reduces the user's psychological burden. The system can also identify suspicious individuals and dangerous creatures, improving facility safety.

[1983] A "sensor" or "camera" is a device for acquiring image data or audio data, and is used for environmental monitoring.

[1984] "Image recognition means" is a technology that analyzes image data acquired by sensors or cameras and identifies specific objects.

[1985] The "location information acquisition means" is a technology for acquiring location information of an object identified by the image recognition means and transmitting the information to a server.

[1986] The "audio generating means" is a technology that generates an audio signal to repel a specified target and issues a warning sound to the target.

[1987] "Autonomous robot means" refers to robot technology that automatically moves based on location information received from a server, and tracks and exterminates targets.

[1988] "Data analysis means" refers to technology for analyzing the ecological information of a subject and identifying behavioral patterns.

[1989] "Emotion recognition means" is a technology for analyzing the user's emotional state and adjusting the system's behavior.

[1990] The "position information acquisition device" is a device that is attached to an object captured by an autonomous robot means and that tracks the position information of the object.

[1991] "Speech recognition means" is a technology that uses a highly sensitive microphone to detect and analyze the target voice.

[1992] A "suspicious person" refers to a person who poses a security risk and is an object identified by the system.

[1993] "Dangerous creatures" refer to animals that may pose a threat to the safety of the facility or surrounding area, and are objects that are detected by the system.

[1994] The invention is a pest control and security enhancement system that consists of the following major components:

[1995] 1. Real-time image acquisition

[1996] The camera on the device (smartphone) is turned on and video data from the site is acquired in real time. The video data is then sent to a cloud server (AWS, Google Cloud, etc.) at regular intervals.

[1997] 2. Image Recognition

[1998] The server inputs the received video data into image recognition AI (TensorFlow, OpenCV, etc.) to identify suspicious individuals or dangerous creatures. Location information of suspicious individuals or dangerous creatures is generated, recorded in a database, and displayed on the management screen.

[1999] 3. Audio alert

[2000] The device constantly monitors environmental sounds using a highly sensitive microphone and transmits audio data to a server at regular intervals.

[2001] The server inputs the received voice data into a voice recognition AI (Google Speech-to-Text, DeepSpeech, etc.) to identify the voices of pests or suspicious people.

[2002] The server transmits an alert sound generation command to the terminal based on the identified location information.

[2003] The device will play the specified warning sound to repel the target.

[2004] 4. Autonomous Robots

[2005] The server transmits the location information of the detected target to an autonomous robot (such as a DJI drone).

[2006] The autonomous robot automatically moves to the destination based on the received location information and monitors the surroundings with sensors and cameras. After confirming the target, it begins tracking and extermination. After extermination is complete, it sends a completion report to the server.

[2007] 5. Data Analysis

[2008] The server collects target and extermination data from environmental sensors and cameras, and inputs it into a data mining AI to analyze behavioral patterns and preferred environments.

[2009] Based on the analysis results, effective eradication and prevention measures are developed.

[2010] The user attaches a GPS tracker to the captured target, and the server tracks the target's location in real time. The server analyzes the GPS data, identifies the target's movement patterns, and plans the next extermination.

[2011] 6. Emotion recognition

[2012] The server inputs the user's voice and video data into an emotion recognition engine (IBM Watson, Azure Cognitive Services, etc.) for analysis.

[2013] The emotion recognition engine recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety.

[2014] The server automatically adjusts the system's behavior and the intensity and type of alert sounds based on the user's emotional state.

[2015] Specific examples

[2016] Example of target detection using image recognition

[2017] 1. Server: "Received video data from the camera and identified a suspicious individual. Location information: latitude 35.1234, longitude 137.5678. Display the detection results on the management screen."

[2018] Example of user support using emotion recognition

[2019] 2. Server: "Inputting user's voice and video data into the emotion recognition engine."

[2020] 3. Emotion recognition engine: "Identify when the user is stressed. Adjust the system's behavior and reduce the intensity of the warning sounds."

[2021] Example prompts

[2022] prompt:

[2023] "Analyze the video data received from the surveillance cameras and generate a code to identify suspicious individuals or dangerous creatures."

[2024] Generation example:

[2025] import cv2

[2026] Loading an image recognition model

[2027] model = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'graph.pbtxt')

[2028] def detect_objects(image):

[2029] Preprocessing of input images

[2030] image_blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300),

[2031] mean=(104.0, 177.0, 123.0))

[2032] model.setInput(image_blob)

[2033] detections = model.forward()

[2034] return detections

[2035] Capture camera data

[2036] cap = cv2.VideoCapture(0)

[2037] while True:

[2038] ret, frame = cap.read()

[2039] if not ret:

[2040] break

[2041] detections = detect_objects(frame)

[2042] Processing the findings

[2043] for i in range(detections.shape[2]):

[2044] confidence = detections[0, 0, i, 2]

[2045] if confidence > 0.7:

[2046] Obtaining the bounding box of an object

[2047] box = detections[0, 0, i, 3:7] np.array([frame.shape[1], frame.shape[0],

[2048] frame.shape[1], frame.shape[0]])

[2049] (x, y, x1, y1) = box.astype("int")

[2050] cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)

[2051] cv2.imshow('Frame', frame)

[2052] if cv2.waitKey(1) & 0xFF == ord('q'):

[2053] break

[2054] cap.release()

[2055] cv2.destroyAllWindows()

[2056] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2057] Step 1:

[2058] Real-time video data acquisition

[2059] The device (smartphone) activates the camera and captures video data in real time. This video data is sent to a cloud server at regular intervals. Specifically, the camera sensor captures environmental information and saves the video data in a specific folder on the device. The saved data is then uploaded to the cloud server.

[2060] Input: Video data from the camera sensor

[2061] Output: Video data sent to the cloud server

[2062] Step 2:

[2063] Analyzing image data and identifying objects

[2064] The server inputs the received video data into an image recognition AI to identify suspicious individuals or dangerous creatures. Specifically, it uses an image recognition model (e.g., TensorFlow, OpenCV) to detect specific patterns based on pixel information in the video. The model also generates location information for the target object and records this information in a database.

[2065] Input: Video data stored on a cloud server

[2066] Output: Location information of identified objects and their associated data

[2067] Step 3:

[2068] Environmental sound monitoring and audio data acquisition

[2069] The device constantly monitors environmental sounds using a highly sensitive microphone and transmits the audio data to a server at regular intervals. Specifically, the microphone captures the environmental sounds and the audio data is stored on the device. The stored data is then uploaded to a cloud server.

[2070] Input: Environmental sound data from a high-sensitivity microphone

[2071] Output: Audio data sent to the cloud server

[2072] Step 4:

[2073] Analyzing voice data and identifying objects

[2074] The server inputs the received audio data into a voice recognition AI to identify the voices of pests or suspicious individuals. Specifically, it uses a voice recognition model (e.g., Google Speech-to-Text, DeepSpeech) to analyze sound wave patterns and recognize specific sounds. The model also simultaneously generates location information for the target object and records this information in a database.

[2075] Input: Audio data stored on a cloud server

[2076] Output: Location information of identified objects and their associated data

[2077] Step 5:

[2078] Generates warning sounds

[2079] The server sends an alarm sound generation command to the device based on the identified location information. The device executes the received alarm sound generation command and plays the specified alarm sound. Specifically, the server generates the alarm sound using a speech synthesis engine and sends the command to the device. The device plays the alarm sound using its built-in speaker.

[2080] Input: Location information of identified object and command to generate warning sound

[2081] Output: Generated warning sound

[2082] Step 6:

[2083] Autonomous Robot Control

[2084] The server sends the location information of the detected object to the autonomous robot. Based on the location information received from the server, the autonomous robot automatically moves to its destination and monitors its surroundings with sensors and cameras. Specifically, the server generates robot control commands and sends them to the robot's control system. The robot begins operation, and after reaching the designated area, it uses sensors to identify the object, and then tracks and exterminates it.

[2085] Input: Position information of identified objects and robot control commands

[2086] Output: Object tracking and removal

[2087] Step 7:

[2088] Data analysis and behavioral pattern identification

[2089] The server collects object and extermination data collected from environmental sensors, cameras, and audio data, and inputs it into a data-mining AI to analyze behavioral patterns and preferred environments. Specifically, the AI ​​model extracts the object's past behavioral data from a database and identifies its movement patterns and behavioral characteristics. The analysis results are used to develop new extermination plans.

[2090] Input: Collected object and removal data

[2091] Output: Behavioral patterns and new eradication plan

[2092] Step 8:

[2093] User Emotion Recognition and Support

[2094] The server inputs the user's voice and video data into an emotion recognition engine for analysis. The emotion recognition engine recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety. Specifically, the server compares the user's past emotional data with their current data and recommends the optimal action. The system then makes automatic adjustments based on this.

[2095] Input: User's audio and video data

[2096] Output: User's emotional state and adjust the system accordingly

[2097] Through the above steps, the present invention can effectively exterminate pests and improve security, while also reducing the psychological burden on users.

[2098] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2100] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2101] [Fourth embodiment]

[2102] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2103] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2105] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2106] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2109] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2110] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2111] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2113] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2114] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2115] The present invention is a system for efficient and sustainable pest control that combines image recognition, voice recognition, autonomous robots, and data analytics to consistently detect, track, and eliminate pests.

[2116] System Configuration

[2117] The system consists of the following main components:

[2118] 1. Sensor or camera

[2119] Multiple sensors or cameras are installed in agricultural fields or protected areas of crops to capture real-time video and audio data of the environment.

[2120] 2. Image Recognition Methods

[2121] It includes algorithms for analyzing image data obtained from sensors or cameras and identifying pests.

[2122] 3. Location information acquisition means

[2123] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[2124] 4. Voice Generation Method

[2125] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[2126] 5. Autonomous Robotic Means

[2127] It has the ability to receive location information from the server and track and exterminate pests.

[2128] 6. Data Analysis Methods

[2129] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[2130] Program processing explanation

[2131] Detecting pests using image recognition AI

[2132] Server: Receives video data sent from sensors or cameras. Uses image recognition AI to detect specific pests in the video data. For example, if surveillance cameras are installed around a field and a wild boar appears in the video, it will be identified and its location will be generated.

[2133] Server: Records the location information of identified pests and displays it on the management screen, allowing for monitoring of their entry into crop protection areas and immediate response.

[2134] Keeping pests away with AI-powered audio alerts

[2135] Device: Monitors surrounding sounds with a highly sensitive microphone and captures audio data in real time.

[2136] Server: Using voice recognition AI, identifies the sounds of pests from the acquired audio data. If a specific pest sound is recognized, it generates its location information and sends a command to the device to generate an alarm sound.

[2137] Device: Plays a specified alarm sound to scare pests away from the extermination area. For example, a specific frequency of sound can be emitted to scare pests away from the area.

[2138] Autonomous robots track and eradicate pests

[2139] Server: Sends the location information of detected pests to the autonomous robot.

[2140] Autonomous robot: Based on location information, it moves in real time and tracks pests. It continuously identifies the location of pests using sensors and cameras, and continues to track them until extermination is complete.

[2141] Autonomous robots: They capture and exterminate pests, for example by using a net to capture them while tracking them or by using other extermination methods, and then send a completion report to a server after the extermination is complete.

[2142] Understanding the ecology of pests through data analysis

[2143] Server: Collects data on pest behavior and analyzes it using data mining AI. This allows the behavioral patterns and movement routes of pests to be identified and reflected in extermination plans.

[2144] Server: Captured pests are fitted with GPS trackers to track their location in real time, allowing for analysis of their movement patterns and the development of more effective extermination methods.

[2145] Specific examples

[2146] Example of pest detection using image recognition AI

[2147] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[2148] An example of keeping pests away using voice alert AI

[2149] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[2150] Server: "Send alarm command to device."

[2151] Terminal: "Play specified alarm sound. Keeps pests away."

[2152] An example of tracking and exterminating pests using an autonomous robot

[2153] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[2154] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[2155] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[2156] Example of understanding behavioral patterns through data analysis

[2157] Server: "We are currently analyzing extermination data using data mining AI. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[2158] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[2159] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[2160] In this way, a system is provided in which each element works together to efficiently exterminate harmful animals.

[2161] The processing flow will be explained below.

[2162] Program processing flow

[2163] Detecting pests using image recognition AI

[2164] Step 1:

[2165] Terminal: "Acquire video data from surveillance cameras in real time."

[2166] Step 2:

[2167] Terminal: "Send the acquired video data to the server at regular intervals."

[2168] Step 3:

[2169] Server: "Input the received video data into the image recognition AI."

[2170] Step 4:

[2171] Server: "Image recognition AI analyzes video data to identify pests. For example, it can identify the shapes of wild boars and deer."

[2172] Step 5:

[2173] Server: "Generate location information (latitude, longitude) for identified pests."

[2174] Step 6:

[2175] Server: "Record the generated location information in the database and display it on the management screen."

[2176] Keeping pests away with AI-powered audio alerts

[2177] Step 1:

[2178] Device: "High-sensitivity microphone constantly monitors environmental sounds."

[2179] Step 2:

[2180] Terminal: "Send the acquired audio data to the server at regular intervals."

[2181] Step 3:

[2182] Server: "Input the received voice data into the voice recognition AI."

[2183] Step 4:

[2184] Server: "The voice recognition AI analyzes the audio data to identify the sounds of pests. For example, it can identify the sounds of wild boars."

[2185] Step 5:

[2186] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[2187] Step 6:

[2188] Device: "Plays an alarm sound of the specified frequency and volume to keep pests away."

[2189] Autonomous robots track and eradicate pests

[2190] Step 1:

[2191] Server: "Send the location of detected pests to the autonomous robot."

[2192] Step 2:

[2193] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[2194] Step 3:

[2195] Autonomous robots: "Once they arrive, they monitor the surrounding area using sensors and cameras to locate pests."

[2196] Step 4:

[2197] Autonomous robots: "Prepare means (nets, tranquilizer guns, etc.) to track and capture pests."

[2198] Step 5:

[2199] Autonomous robots: "Capture and reliably eliminate pests."

[2200] Step 6:

[2201] Autonomous robot: "Reports to the server that disinfection is complete."

[2202] Understanding the ecology of pests through data analysis

[2203] Step 1:

[2204] Server: "Stores pest and extermination data collected from sensors and cameras in a database."

[2205] Step 2:

[2206] Server: "The collected data is fed into a data mining AI."

[2207] Step 3:

[2208] Server: "Data mining AI will analyze pest behavior patterns and preferred environments."

[2209] Step 4:

[2210] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[2211] Step 5:

[2212] User: "I'll put a GPS tracker on the captured vermin."

[2213] Step 6:

[2214] GPS tracker: "Sends real-time location information of pests to a server."

[2215] Step 7:

[2216] Server: "Analyze GPS data to understand pest movement patterns."

[2217] Step 8:

[2218] Server: "Plan the next cleanup and notify the user."

[2219] The above is a specific processing flow for carrying out the present invention, which makes it possible to provide a system that realizes efficient and sustainable pest control.

[2220] Example 1

[2221] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2222] Pests cause serious damage to agricultural crops, and traditional extermination methods are difficult to implement efficiently and sustainably. Furthermore, there are insufficient means to monitor pest intrusions in real time and respond quickly, which means the damage tends to spread. To solve these problems, a system is needed that can detect, track, and exterminate pests in an integrated manner, while analyzing their behavioral patterns to prevent future intrusions.

[2223] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2224] In this invention, the server includes image recognition means for acquiring image data using a sensor or camera and analyzing the image data, location information acquisition means for acquiring location information of a target identified by the image recognition means, sound generation means for generating sound to repel the target identified by the image recognition means, autonomous robot means for automatically moving to track and exterminate the target based on the location information of the target, data analysis means for analyzing the target's biological information and identifying its behavioral pattern, communication means for transmitting the acquired video and audio data to the server in real time, and command generation means for generating an alarm sound to be played and transmitting it to a terminal. This makes it possible to perform a consistent process from detecting, tracking, and exterminating pests to analyzing their behavior.

[2225] "Sensor or camera" means a device for acquiring still or video images of the environment and capable of collecting data in real time.

[2226] "Image recognition means" includes algorithms and technologies for analyzing acquired image data and identifying and specifying specific objects (e.g., pests) from within the data.

[2227] The "location information acquisition means" is a means for measuring the location of an object identified by the image recognition means and generating and acquiring that information.

[2228] "Sound generation means" refers to a device or technology that generates warning sounds or the like based on identified sound data in order to keep a specific target away.

[2229] "Autonomous robot means" refers to a type of robot that has the ability to move automatically based on location information sent from a server and track and eliminate specific targets.

[2230] "Data analysis means" refers to systems or technologies that collect and analyze behavioral data of subjects to extract specific patterns and information.

[2231] "Communication means" refers to technology or equipment for transmitting acquired video and audio data to a server in real time, and has the ability to exchange data via a network.

[2232] The "command generation means" is a mechanism that generates commands to instruct the terminal to execute an alarm or other processing based on the data analyzed by the server.

[2233] The present invention relates to a system for efficiently and sustainably eradicating pests in protected areas of agricultural crops, which is composed of a combination of sensors, cameras, image recognition means, autonomous robots, data analysis means, communication means, and sound generation means.

[2234] Hardware and software used

[2235] The specific hardware used in this system includes high-resolution cameras, high-sensitivity microphones, cloud servers, autonomous robots, and GPS trackers. Image recognition is performed using image recognition AI algorithms such as YOLOv5. Voice recognition is performed using deep learning-based voice recognition AI.

[2236] System Operation Overview

[2237] 1. Sensor or camera placement and data acquisition

[2238] The sensors or cameras are installed in agricultural fields and protected areas of agricultural crops to capture real-time video and audio data of the surrounding area. In particular, all-weather, high-resolution cameras and highly sensitive microphones are used to monitor pests that target crops.

[2239] 2. Analysis of video data

[2240] The acquired video data is sent to a server. The server receives the video data in real time and uses image recognition techniques (such as YOLOv5) to detect pests. Once a detected pest is identified, its location information is automatically generated and displayed on the management screen.

[2241] 3. Analysis of audio data and generation of warning sounds

[2242] The device uses a highly sensitive microphone to collect surrounding sounds and transmits them to a server in real time. The server then uses a voice recognition tool to analyze the audio data and detect the sounds of specific pests. Based on the detected sounds, a command to generate an alarm sound is sent to the device and played back. This frightens pests away from the extermination area.

[2243] 4. Autonomous robots for tracking and eliminating pests

[2244] The server sends the location information of the detected pests to the autonomous robot. The autonomous robot moves in real time based on the received location information and tracks the pests. During tracking, it continuously identifies the location of the pests using sensors and cameras, and carries out extermination using nets, etc. Once extermination is complete, a completion report is sent to the server.

[2245] 5. Data analysis and behavioral pattern understanding

[2246] The server collects data on the behavior of pests and analyzes it using data analysis tools. This allows the behavioral patterns and movement routes of pests to be identified and reflected in the next extermination plan. In addition, GPS trackers are attached to captured pests to track their location in real time. This allows for the development of even more effective extermination methods.

[2247] Specific examples

[2248] Example of pest detection using image recognition AI

[2249] Server: "Receiving video data from camera 1. Identified a wild boar. Generate location information: latitude 35.1234, longitude 137.5678, and display it on the management screen."

[2250] An example of keeping pests away using voice alert AI

[2251] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[2252] Server: "Send a command to generate an alarm to the device."

[2253] Terminal: "Play specified alarm sound. Keeps pests away."

[2254] An example of tracking and exterminating pests using an autonomous robot

[2255] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[2256] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[2257] Autonomous robot: "Capture the wild boar and capture it with a net. Eliminate it. Send a completion report to the server."

[2258] Example of understanding behavioral patterns through data analysis

[2259] Server: "We are currently analyzing the extermination data using data analysis tools. Analysis results: Wild boars are active at night. Their preferred environment is low-lying, marshy areas."

[2260] Server: "We've attached a GPS tracker to the captured boar. We've begun collecting location information."

[2261] Server: "Analyzes GPS data to identify pest movement patterns. Plans the next extermination and notifies the user."

[2262] Prompt Sentence Examples

[2263] "One night in October, an IoT sensor detected the movement of a wild boar in a protected area for agricultural crops. Image recognition AI analyzed the footage to identify the boar and generate location information. An autonomous robot then used the location information to track the boar and capture it with a net. Please describe this entire process."

[2264] The system of the present invention provides a comprehensive solution for efficient and sustainable pest control, allowing users to monitor and control in real time through an administrative interface.

[2265] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2266] Step 1:

[2267] Initial setup and sensor preparation

[2268] Server: Checks the setting information of sensors and cameras, and verifies that each device is working properly. It distributes IP addresses and setting information to each device to ensure communication is established.

[2269] Input: Sensor and camera settings, device ID

[2270] Output: Device operation check result, device setting completion notification

[2271] Specific operation: The server sends the configuration information to each sensor, and once normal operation is confirmed, it receives a "configuration complete" notification.

[2272] Terminal: Check the installation location of sensors and cameras and place them appropriately. The installation locations should be set in agricultural land and crop protection areas.

[2273] Input: Location information, physical layout of sensors and cameras

[2274] Output: Placement completion notification

[2275] What it does: The device physically places cameras and sensors in farmland or protected areas and fixes them at appropriate angles.

[2276] Users: Monitor the operation of sensors and cameras through the admin panel and check for any issues.

[2277] Input: Sensor / camera status information on the management screen

[2278] Output: Results of normal operation of each device

[2279] What happens: The user checks the admin panel and confirms that all devices are working properly.

[2280] Step 2:

[2281] Acquisition and transmission of video data

[2282] Sensors / cameras: Capture real-time video data from the area where they are installed, with a particular focus on pest monitoring.

[2283] Input: Real-time video and audio from the scene

[2284] Output: Get raw data

[2285] Specific operation: Sensors and cameras continuously record and acquire video and audio within a specified range.

[2286] Terminal: Compresses the captured video data and sends it to the server, improving data transfer efficiency.

[2287] Input: Raw data (video and audio)

[2288] Output: Compressed data, notification of completion

[2289] Specific operation: The device compresses the video and audio data and uploads it to the server.

[2290] Step 3:

[2291] Image recognition for detecting pests

[2292] Server: Analyzes the received video data and runs image recognition AI to detect specific pests (e.g., wild boars).

[2293] Input: Compressed video data

[2294] Output: Identification and location information of detected pests

[2295] Specific operation: The server analyzes the video data, detects pests using YOLOv5, and generates location information.

[2296] Server: Generates location information of pests and displays it on the management screen.

[2297] Input: Detected pest's identity and location

[2298] Output: Location information generated and displayed on the management screen

[2299] Specific operation: The server displays the location information of the pests on the management screen, allowing the user to check it in real time.

[2300] Step 4:

[2301] Acquisition and analysis of audio data

[2302] Device: A highly sensitive microphone captures surrounding audio data and transmits it to the server in real time.

[2303] Input: Real-time audio from the site

[2304] Output: Acquired audio data, transmission completion notification

[2305] Specific operation: The device picks up sound with a high-sensitivity microphone and sends the data to the server.

[2306] Server: Analyzes the captured audio data and uses voice recognition AI to identify the sounds of pests.

[2307] Input: Captured audio data

[2308] Output: Enemy call identification result, location information

[2309] Specific operation: The server analyzes the audio data and uses deep learning to identify the sounds of pests and generate location information.

[2310] Step 5:

[2311] Audio warning and pest repellent

[2312] Server: Based on the detected sounds, sends a command to the device to generate an alarm sound.

[2313] Input: Call identification result, location information

[2314] Output: Command to generate warning sound

[2315] Specific operation: The server generates an alarm command and sends it to the device.

[2316] Terminal: Receives an alert sound generation command from the server and plays the specified alert sound.

[2317] Input: Command to generate warning sound

[2318] Output: Warning sound played, distancing result

[2319] Specific action: The device plays an alarm sound to scare away pests.

[2320] Step 6:

[2321] Autonomous robots track and eradicate pests

[2322] Server: Sends the location information of detected pests to the autonomous robot.

[2323] Input: Location of detected pest

[2324] Output: Location information sending completion notification

[2325] Specific operation: The server sends location information to the autonomous robot.

[2326] Autonomous robot: Moves in real time based on received location information and tracks pests.

[2327] Input: Location

[2328] Output: Pest capture results, extermination report

[2329] Specific operation: The autonomous robot moves based on location information, tracks and captures pests.

[2330] Autonomous robot: Reports to the server once disinfection is complete.

[2331] Input: Disinfection completion information

[2332] Output: Report of disinfection completion to the server

[2333] Specific operation: The autonomous robot sends a report of the completion of eradication to the server.

[2334] Step 7:

[2335] Data analysis and understanding of behavioral patterns

[2336] Server: Collects behavioral data of pests and analyzes it using data analysis tools.

[2337] Input: Behavioral data obtained during removal

[2338] Output: Identification of behavioral patterns

[2339] Specific operation: The server analyzes the behavioral data and identifies behavioral patterns and movement routes.

[2340] Server: GPS trackers are attached to captured pests to track their location in real time.

[2341] Input: Captured pest information, GPS tracker

[2342] Output: GPS data, behavioral patterns

[2343] What it does: The server analyzes the GPS data and develops an effective removal method.

[2344] With detailed processing at each step, the system can detect, track, exterminate and analyze pests in an integrated manner.

[2345] (Application example 1)

[2346] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2347] Conventional pest control systems did not consistently detect, track, and exterminate pests, making it difficult to respond in a timely manner. Furthermore, when a pest invades a factory, it is difficult to understand its entry route and behavioral patterns, making it impossible to develop an efficient extermination method. Another issue is that there is no interface that allows users to check the current location of pests in real time, making it difficult to respond quickly.

[2348] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2349] In this invention, the server includes an image recognition means that acquires image data using a sensor or camera and analyzes the image data, a location information acquisition means that acquires location information of an object identified by the image recognition means, a sound generation means that generates sound to repel the object identified by the image recognition means, an autonomous robot means that automatically moves to track and exterminate the object based on the location information of the object, a data analysis means that analyzes the object's biological information and identifies its behavioral pattern, and a user interface means that provides an interface that allows a user to check the video data acquired from the sensor or camera and the sound data detected by the sound recognition means in real time. This makes it possible to perform an integrated process from detection to extermination of pests, and to understand the entry routes and behavioral patterns of pests within a factory, thereby enabling quick and efficient extermination.

[2350] A "sensor" is a device that detects changes in the physical environment and transmits them as a signal.

[2351] A "camera" is a device that captures video or images and stores and transmits them as digital data.

[2352] "Image recognition means" refers to a device that includes an algorithm or program for analyzing image data obtained from a sensor or camera and identifying a specific object.

[2353] The "location information acquisition means" is a device that identifies the location of the object identified by the image recognition means, and acquires and transmits the location information.

[2354] The "audio generating means" is a device that generates audio based on preset conditions and plays it back.

[2355] An "autonomous robotic means" is a robot that has the ability to move automatically based on location information and track and eliminate identified targets.

[2356] "Data analysis means" is a system for analyzing collected data and identifying the behavioral patterns and ecological information of the subject.

[2357] The "user interface means" is a device that provides an interface that allows a user to check the video data and audio data obtained from the sensor or camera in real time.

[2358] "Speech recognition means" is a device that includes an algorithm or program for analyzing voice data acquired by a high-sensitivity microphone and identifying the voice of a specific target.

[2359] A "GPS tracker" is a device that obtains and tracks location information in real time.

[2360] This invention is a system for efficiently exterminating pests in factories. This system is realized by combining hardware such as sensors, cameras, high-sensitivity microphones, and autonomous robots with software such as image recognition AI, voice recognition AI, and data analysis AI.

[2361] Hardware Configuration

[2362] Sensors or cameras: Installed in various locations within the factory, they capture video and audio data in real time.

[2363] High-sensitivity microphone: Collects sounds within the factory and detects the cries of pests.

[2364] Autonomous robots: They move automatically based on the location of pests to track and exterminate them.

[2365] GPS tracker: Attached to pests captured by autonomous robots, it tracks their location in real time.

[2366] Software Configuration

[2367] Image recognition AI (e.g., TensorFlow): Analyzes video data obtained from sensors or cameras to detect pests.

[2368] Speech recognition AI (e.g., Google Speech Recognition): Analyzes audio data collected by a highly sensitive microphone and identifies the sounds of pests.

[2369] Data analysis AI (e.g., SciKit-Learn): Analyzes behavioral patterns and ecological information of pests and reflects this in extermination plans.

[2370] User interface: Provides an interface that allows users to view video and audio data in real time via smartphones, tablets, or smart glasses.

[2371] Specific operation of the system

[2372] The server receives video data from sensors or cameras and uses image recognition AI to detect pests. For example, if a camera installed in a factory captures a wild boar in the video, it analyzes the data and generates location information. This location information is sent in real time to an autonomous robot, which then begins tracking the pest.

[2373] The server also uses voice recognition AI to analyze audio data obtained from a highly sensitive microphone, and if it detects the cries of a pest, it obtains its location information. Based on this location information, a sound generator generates an alarm sound to keep the pest away.

[2374] Using the provided user interface, users can grasp the current situation in real time and take necessary measures. For example, they can check the footage from the surveillance cameras in the factory on their smartphones and take immediate action if a pest is detected.

[2375] Specific examples

[2376] Pest detection: "The model analyzed the video data from the camera and detected a wild boar. Its location is latitude 35.1234, longitude 137.5678. An instruction has been sent to the robot."

[2377] Roar detection: "A highly sensitive microphone has detected the sound of a wild boar roaring. An alarm sound will be played to keep the boar away from the factory."

[2378] User notification: "We have reviewed the footage from the surveillance camera and detected a pest. The robot will now move to the specified location and begin extermination."

[2379] These functions enable the rapid detection of pest intrusions and their efficient extermination, making this a very useful system for factories where real-time confirmation and rapid response are required.

[2380] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2381] Step 1:

[2382] The server acquires video data in real time from sensors or cameras installed within the factory.

[2383] Input: Video data from a sensor or camera

[2384] Output: Video data passed to image recognition AI

[2385] How it works: The cameras continuously capture video at various locations in the factory and send the data to a server.

[2386] Step 2:

[2387] The server inputs the acquired video data into image recognition AI and performs analysis to detect pests.

[2388] Input: Video data acquired in the previous step

[2389] Output: Pest identification and location

[2390] How it works: Image recognition AI detects features such as people and vehicles in video data and determines whether it contains any vermin. For example, if it detects a video containing a wild boar, it generates its location information.

[2391] Step 3:

[2392] The server obtains the location information of pests detected by image recognition AI and transmits it to the autonomous robot.

[2393] Input: Pest identification and location

[2394] Output: Movement instructions for the autonomous robot

[2395] Specific operation: Based on the obtained location information, the server issues instructions to the autonomous robot, causing it to move in order to capture the pest.

[2396] Step 4:

[2397] The autonomous robots move automatically based on the transmitted location information to track and eliminate pests.

[2398] Input: Location information and movement instructions from the server

[2399] Output: Report of pest capture and extermination completion

[2400] Specific behavior: The robot moves towards the designated location, continuously locates the pest using pest tracking sensors and cameras, and then performs extermination measures, such as capturing the pest with a net or using appropriate extermination methods.

[2401] Step 5:

[2402] The server collects real-time audio data from high-sensitivity microphones installed within the factory.

[2403] Input: Audio data from a high-sensitivity microphone

[2404] Output: Voice data passed to the voice recognition AI

[2405] How it works: Highly sensitive microphones collect audio from various locations within the factory and send the data to a server.

[2406] Step 6:

[2407] The server uses voice recognition AI to analyze the acquired audio data and detect the cries of specific pests.

[2408] Input: Audio data obtained in the previous step

[2409] Output: Sound detection results and location information of pests

[2410] How it works: The voice recognition AI analyzes unique sound patterns contained in the audio data to detect the calls of specific pests (such as wild boars). If detected, it generates location information.

[2411] Step 7:

[2412] The server generates and reproduces an alarm sound for keeping away harmful animals from the sound generating means based on the location information of the harmful animal's cry.

[2413] Input: Call detection results and location information of pests

[2414] Output: Generates and plays warning sounds

[2415] Specific behavior: The server generates a specified alarm sound and uses surrounding speakers to play sounds of frequencies that pests dislike, thereby keeping pests away.

[2416] Step 8:

[2417] The user uses the provided user interface to check the video data and audio data in real time.

[2418] Input: Real-time data (video and audio) from the server

[2419] Output: what is displayed in the user interface

[2420] Specific operation: Users operate the interface via smartphones, tablets, or smart glasses to monitor the situation in the factory in real time and take appropriate action if necessary.

[2421] Step 9:

[2422] The server uses data analysis AI to analyze the behavioral patterns and ecological information of pests and reflect this in extermination plans.

[2423] Input: Pest location and behavior data

[2424] Output: Behavioral pattern analysis results and eradication plan

[2425] How it works: The data analysis AI uses the collected data to identify the behavioral patterns and ecological information of pests, and then plans more effective extermination methods. The results are notified to the user and incorporated into the next extermination plan.

[2426] These steps enable consistent and efficient detection and extermination of pests, enabling real-time monitoring and rapid response.

[2427] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2428] The present invention adds an emotion engine that recognizes the user's emotions to a system for efficient and sustainable pest control. By combining image recognition, voice recognition, autonomous robots, data analysis, and the emotion engine, the system can consistently detect, track, and exterminate pests while also reducing the user's mental stress.

[2429] System Configuration

[2430] The system consists of the following main components:

[2431] 1. Sensor or camera

[2432] Multiple sensors or cameras installed on-site capture video and audio data of the environment in real time.

[2433] 2. Image Recognition Methods

[2434] It includes algorithms for analyzing acquired image data and identifying pests.

[2435] 3. Location information acquisition means

[2436] The location information of the vermin identified by the image recognition means is acquired and transmitted to a server.

[2437] 4. Voice Generation Method

[2438] It uses a highly sensitive microphone to pick up surrounding sounds, and when it recognizes the sound of a pest, it generates a sound to scare the pest away.

[2439] 5. Autonomous Robotic Means

[2440] It has the ability to receive location information from the server and track and exterminate pests.

[2441] 6. Data Analysis Methods

[2442] This is a system for collecting and analyzing behavioral patterns and ecological information of pests.

[2443] 7. Emotion Engine

[2444] It includes technology for recognizing user emotions and adjusting system behavior.

[2445] Program processing explanation

[2446] Detecting pests using image recognition AI

[2447] Terminal: "Acquire video data from the surveillance camera in real time. Send the video data to the server at regular intervals."

[2448] Server: "The received video data is input into image recognition AI to identify harmful animals. For example, wild boars and deer."

[2449] Server: "Generate location information for identified pests, record it in a database, and display it on the management screen."

[2450] Keeping pests away with AI-powered audio alerts

[2451] Device: "Constantly monitors environmental sounds with a high-sensitivity microphone. The acquired audio data is sent to the server at regular intervals."

[2452] Server: "The received audio data is input into the speech recognition AI to identify the sound of a pest. For example, it identifies the sound of a wild boar."

[2453] Server: "Generate location information for identified pests and send an alarm sound generation command to the device."

[2454] Device: "Plays an alarm sound of the specified frequency and volume to ward off pests."

[2455] Autonomous robots track and eradicate pests

[2456] Server: "Send the location of detected pests to the autonomous robot."

[2457] Autonomous robot: "Automatically moves to a destination based on location information received from a server."

[2458] Autonomous robots: "Once they arrive, they monitor the surroundings with sensors and cameras to identify pests. They then begin tracking and exterminating them."

[2459] Autonomous robot: "Captures pests and exterminates them reliably. Once extermination is complete, it sends a completion report to the server."

[2460] Understanding the ecology of pests through data analysis

[2461] Server: "Collects pest and extermination data collected from environmental sensors and cameras."

[2462] Server: "The collected data is fed into a data-mining AI to analyze the behavioral patterns and preferred environments of pests."

[2463] Server: "Based on the analysis results, we will develop effective removal and prevention measures."

[2464] User: "I'll put a GPS tracker on the captured vermin."

[2465] GPS Tracker: "Tracks the location of pests in real time and sends it to a server."

[2466] Server: "We analyze GPS data to identify pest movement patterns and plan the next eradication."

[2467] User support with emotion engine

[2468] Server: "Input the user's voice and video data into the emotion engine and analyze it."

[2469] Emotion Engine: "Recognizes the user's emotional state based on the analysis results and suggests actions to reduce stress and anxiety."

[2470] Server: "Automatically adjust system behavior and alert intensity and type based on the user's emotional state."

[2471] Specific examples

[2472] Example of pest detection using image recognition AI

[2473] Server: "Receiving video data from camera 1. A wild boar has been identified. Location information: latitude 35.1234, longitude 137.5678. Display the detection result on the management screen."

[2474] An example of keeping pests away using voice alert AI

[2475] Device: "Detects sounds of pests with a highly sensitive microphone. Identifies the sound of a wild boar. Sends location information: latitude 35.1234, longitude 137.5678 to the server."

[2476] Server: "Send a command to generate an alarm to the device."

[2477] Terminal: "Play the specified alarm sound to keep pests away."

[2478] An example of tracking and exterminating pests using an autonomous robot

[2479] Server: "Sending boar's location to robot. Location: Latitude 35.1234, Longitude 137.5678."

[2480] Autonomous robot: "Moving to designated location. Sensors have detected wild boar. Commencing tracking."

[2481] Autonomous robot: "Captures pests and traps them in a net. Extermination complete. Sends a completion report to the server."

[2482] Example of user support using emotion engine

[2483] Server: "Inputting user's audio and video data into the emotion engine."

[2484] Emotion engine: "Identify when the user is stressed. Adjust system behavior and reduce the intensity of alert sounds."

[2485] Server: "Notify user of response. Taking action based on emotional state."

[2486] This system not only enables efficient pest control, but also reduces the psychological burden on users.

[2487] The processing flow will be explained below.

[2488] Program processing flow

[2489] Detecting pests using image recognition AI

[2490] Step 1:

[2491] Terminal: "Activate the surveillance camera and capture video data in real time."

[2492] Step 2:

[2493] Terminal...

Claims

1. image recognition means for acquiring image data using a sensor or a camera and analyzing the image data; a position information acquisition means for acquiring position information of the object identified by the image recognition means; a sound generating means for generating a sound to move away the target identified by the image recognition means; an autonomous robot means that automatically moves to track and eliminate the target based on the position information of the target; a data analysis means for analyzing the biological information of the subject and identifying a behavioral pattern; A system including:

2. 10. The system of claim 1, wherein said autonomous robotic means further comprises means for attaching a GPS tracker to a captured object.

3. 2. The system according to claim 1, wherein the voice generating means includes a voice recognition means for detecting the target voice using a high-sensitivity microphone and analyzing the voice.

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