system

Abstract

The system according to this embodiment aims to automate beekeeping management and achieve efficient and sustainable beekeeping. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an instruction unit, a measurement unit, and a harvesting unit. The collection unit collects data inside and outside the beehive using sensors and a camera. The analysis unit uses a generating AI to analyze the data collected by the collection unit. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the analysis unit. The measurement unit measures the amount of honey using a weight sensor. The harvesting unit automatically harvests honey based on the amount of honey measured by the measurement unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, beekeeping management is often carried out manually, and there is a problem that efficient and sustainable management is difficult.

[0005] The system according to the embodiment aims to automate beekeeping management and achieve efficient and sustainable beekeeping.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an instruction unit, a measurement unit, and a harvesting unit. The collection unit collects data from inside and outside the beehive using sensors and a camera. The analysis unit uses a generating AI to analyze the data collected by the collection unit. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the analysis unit. The measurement unit measures the amount of honey using a weight sensor. The harvesting unit automatically harvests the honey based on the amount of honey measured by the measurement unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate beekeeping management and achieve efficient and sustainable beekeeping. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated beekeeping system according to an embodiment of the present invention is a system that achieves the automation of beekeeping by utilizing an AI agent. This system uses sensors and cameras to monitor the inside and outside of the beehive in real time and automatically manages the health of the bees, the temperature and humidity inside the beehive, and the timing of honey harvesting. The collected data is analyzed by a generating AI and instructions are given to maintain the optimal environment. Furthermore, it is equipped with a mechanism that measures the amount of honey using a weight sensor and automatically harvests the honey. This enables efficient and sustainable beekeeping and significantly reduces the workload of beekeepers. For example, the entire beehive for beekeeping is designed. Since honeybees have the characteristic of grasping the honeycomb structure (hexagonal grid) that forms the basis of their comb, the base of the beehive is made of honeycomb structured resin. This honeycomb structure is sloped inside, and a tank is installed along the slope so that honey can be stored. A weight sensor is installed at the bottom of this tank to monitor the amount stored. In addition, temperature and humidity sensors and cameras are installed inside and outside the structure for continuous monitoring. This information is analyzed by feeding structured data in JSON format and unstructured data separately to a generating AI. In the event of an anomaly that could potentially destroy a beehive (for example, a wax moth outbreak or a giant hornet attack), the system immediately notifies the administrator to prompt action. This system aims to lower the barriers to entry into beekeeping and increase the number of beekeepers, not only those who keep bees as a business but also those who keep them as a hobby. Increasing the beekeeping population will also indirectly benefit the pollination of natural plants, thus leading to more direct and effective nature conservation activities compared to educational campaigns. As a result, this automated beekeeping system will enable efficient and sustainable beekeeping, significantly reducing the workload of beekeepers.

[0029] The automated beekeeping system according to this embodiment comprises a collection unit, an analysis unit, an instruction unit, a measurement unit, and a harvesting unit. The collection unit collects data inside and outside the beehive using sensors and cameras. The collection unit can, for example, collect temperature and humidity inside and outside the beehive in real time. The collection unit can also collect camera images inside and outside the beehive. For example, the collection unit measures the temperature and humidity inside the beehive with sensors and collects the data in real time. The collection unit can similarly collect temperature and humidity outside the beehive. Furthermore, the collection unit can collect camera images inside and outside the beehive to monitor the health status and any abnormalities of the bees. The analysis unit uses a generating AI to analyze the data collected by the collection unit. For example, the analysis unit provides the collected data to the generating AI in JSON format, and the generating AI analyzes it. Based on the collected data, the generating AI analyzes the health status of the bees and the environment inside the beehive. For example, the generating AI analyzes the temperature and humidity data inside the beehive and issues instructions to maintain an optimal environment. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the analysis unit. For example, the instruction unit issues instructions to adjust the temperature and humidity inside the beehive based on the analysis results of the generating AI. The instruction unit can control ventilation systems, humidifiers, etc., to maintain the temperature and humidity inside the beehive within the optimal range. The measurement unit measures the amount of honey using a weight sensor. For example, the measurement unit can measure the amount of honey stored in a tank inside the beehive in real time. The measurement unit can also compensate for the effects of temperature and humidity in order to accurately measure the amount of honey. The harvesting unit automatically harvests honey based on the amount of honey measured by the measurement unit. For example, the harvesting unit can automatically harvest honey when the measured amount of honey exceeds a certain standard. The harvesting unit can also employ methods to minimize the activity of bees during harvesting. As a result, the automated beekeeping system according to this embodiment can achieve automation and efficiency in beekeeping and significantly reduce the workload of beekeepers.

[0030] The data collection unit uses sensors and cameras to collect data from inside and outside the beehive. Specifically, it can collect temperature and humidity inside and outside the beehive in real time. Since temperature and humidity inside the beehive greatly affect the health and activity of honeybees, it is important to collect this data accurately. The data collection unit uses temperature and humidity sensors installed inside the beehive to acquire environmental data inside the beehive in real time. This allows for constant monitoring of temperature and humidity fluctuations inside the beehive and immediate response if an abnormality occurs. The data collection unit can also collect temperature and humidity outside the beehive. Environmental data outside the beehive is important as reference information when adjusting the environment inside the beehive. For example, if the outside temperature is high, measures to lower the temperature inside the beehive will be necessary. Furthermore, the data collection unit can collect camera images inside and outside the beehive to monitor the health and abnormalities of the honeybees. The cameras capture the movements and behavior of honeybees inside the beehive and collect them as image data. This allows for early detection of abnormal behavior and signs of disease in honeybees. The collected data is transmitted to a central database and used for analysis by the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and instruction units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses a generating AI to analyze data collected by the collection unit. Specifically, the collected data is provided to the generating AI in JSON format, and the generating AI analyzes it. Based on the collected data, the generating AI analyzes the health of the bees and the environment inside the hive. For example, the generating AI analyzes data on temperature and humidity inside the hive and issues instructions to maintain an optimal environment. Based on past data and statistical information, the generating AI can predict the behavior patterns of bees and environmental fluctuations. This allows the analysis unit to quickly and accurately analyze the collected data and provide information to optimize the environment inside the hive. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if the temperature inside the hive rises sharply or the activity of bees decreases abnormally, the generating AI can detect the anomaly and issue instructions to take appropriate measures. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The instruction unit issues instructions to maintain an optimal environment based on the analysis results obtained by the analysis unit. Specifically, it issues instructions to adjust the temperature and humidity inside the hive based on the analysis results of the generating AI. The instruction unit can control ventilation systems, humidifiers, etc., to maintain the temperature and humidity inside the hive within an optimal range. For example, if the temperature inside the hive is too high, it will issue instructions to activate the ventilation system to lower the temperature. Also, if the humidity inside the hive is too low, it will issue instructions to activate the humidifier to raise the humidity. In this way, the instruction unit can always maintain the environment inside the hive in an optimal state. Furthermore, the instruction unit can also issue instructions to maintain the health of the bees. For example, if the activity of the bees is decreasing, it can issue instructions to improve the environment inside the hive. In this way, the instruction unit can provide information to maintain the health of the bees and optimize the environment inside the hive.

[0033] The measurement unit measures the amount of honey using a weight sensor. Specifically, it can measure the amount of honey stored in the tank inside the beehive in real time. The measurement unit can also correct for the effects of temperature and humidity in order to accurately measure the amount of honey. For example, if the temperature inside the beehive is high, the viscosity of the honey may decrease and the weight may fluctuate, so temperature correction ensures accurate measurement. In addition, the measurement unit can instruct the harvesting unit to automatically start harvesting when the amount of honey exceeds a certain standard. This allows the measurement unit to accurately measure the amount of honey and optimize the timing of harvesting. Furthermore, the measurement unit can provide the collected data to the analysis unit, which can then provide information to understand the honey production status. In this way, the measurement unit can play an important role in improving the efficiency of honey production.

[0034] The harvesting unit automatically harvests honey based on the amount measured by the measuring unit. Specifically, it can automatically harvest honey when the measured amount exceeds a certain standard. The harvesting unit can also employ methods to minimize the activity of bees during harvesting. For example, the harvesting unit can minimize the impact on bees by harvesting during times when bees are not inside the hive. In addition, the harvesting unit can employ a quiet design and vibration-reducing mechanism to avoid disturbing bees during harvesting. This reduces stress on bees and allows for efficient honey harvesting. Furthermore, the harvesting unit can automatically transfer the harvested honey to a tank for storage. This automates the entire process from honey harvesting to storage, significantly reducing the workload of beekeepers. The harvesting unit can also store the harvested honey at appropriate temperatures and humidity to maintain its quality. This allows the harvesting unit to efficiently harvest while preserving honey quality.

[0035] The data collection unit can collect temperature and humidity inside and outside the hive in real time. For example, the data collection unit measures the temperature and humidity inside the hive with sensors and collects the data in real time. The data collection unit can similarly collect temperature and humidity outside the hive. This allows for the maintenance of an optimal environment by collecting temperature and humidity inside and outside the hive in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds or minutes.

[0036] The collection unit can collect camera images from both inside and outside the beehive. For example, the collection unit can collect camera images from inside the beehive to monitor the health and abnormalities of the bees. The collection unit can similarly collect camera images from outside the beehive. This allows for monitoring the health and abnormalities of the bees by collecting camera images from both inside and outside the beehive. The specific types and resolutions of the camera images include, but are not limited to, still images, videos, and a range of resolutions.

[0037] The analysis unit provides the collected data to the generating AI in JSON format, which then analyzes it. For example, the analysis unit provides the collected data to the generating AI in JSON format, which then analyzes it. Based on the collected data, the generating AI analyzes the health of the bees and the environment inside the hive. This allows for efficient data analysis by providing the collected data to the generating AI in JSON format. The specific structure and content of the JSON format include, for example, key-value pairs and a hierarchical data structure, but are not limited to these examples.

[0038] The control unit can issue instructions to adjust the temperature and humidity inside the beehive based on the analysis results of the generating AI. For example, the control unit can control ventilation systems, humidifiers, etc., to maintain the temperature and humidity inside the beehive within an optimal range. This allows for the maintenance of an optimal environment by adjusting the temperature and humidity based on the analysis results of the generating AI. Specific methods and criteria for adjusting the temperature and humidity include, but are not limited to, the devices used and the range of adjustment.

[0039] The measurement unit can measure the amount of honey in real time. For example, it can measure the amount of honey stored in a tank inside a beehive in real time. The measurement unit can also compensate for the effects of temperature and humidity in order to accurately measure the amount of honey. This allows for the optimization of harvest timing by measuring the amount of honey in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds or minutes.

[0040] The harvesting unit can automatically harvest honey based on the measured amount of honey. For example, the harvesting unit automatically harvests honey when the measured amount of honey exceeds a certain standard. The harvesting unit can also employ methods to minimize the activity of bees during harvesting. This allows for efficient harvesting by automatically harvesting honey based on the measured amount. Specific methods and standards for automatic harvesting include, but are not limited to, the timing of harvesting and the equipment used.

[0041] The control unit can issue instructions to send a notification to the administrator when an anomaly occurs. For example, the control unit can issue instructions to send a notification to the administrator when an anomaly occurs. The control unit can describe the details of the anomaly to encourage a prompt response. This enables a quick response by notifying the administrator when an anomaly occurs. Specific definitions and criteria for an anomaly include, but are not limited to, examples such as temperature or humidity being outside the normal range or abnormal bee behavior.

[0042] The data collection unit can predict changes in temperature and humidity inside and outside the hive and optimize the timing of data collection based on these predictions. For example, the unit can collect data before temperatures change rapidly to minimize the impact of the change. It can also collect data before humidity rises to maintain the health of the bees. The unit can also collect data at seasonal transitions to respond to environmental changes. This allows for data collection at the optimal time by predicting changes in temperature and humidity. Specific methods and criteria for prediction include, but are not limited to, the algorithms used and the accuracy of the predictions.

[0043] The data collection unit analyzes camera images from inside and outside the hive and can increase the collection frequency if abnormal behavior is detected. For example, if the data collection unit detects abnormal behavior in honeybees, it increases the collection frequency to collect detailed data. If the data collection unit detects an abnormality inside the hive, it increases the collection frequency to respond quickly. The data collection unit can also increase the collection frequency to understand the situation if it detects an abnormality outside (for example, an attack by giant hornets). This allows for a quick response by increasing the collection frequency when abnormal behavior is detected. Specific definitions and criteria for abnormal behavior include, but are not limited to, patterns of honeybee movement and frequency of behavior.

[0044] The collection unit can collect ambient sounds around the beehive and detect anomalies by comparing them with the activity sounds of honeybees. For example, the collection unit can collect ambient sounds around the beehive and detect anomalies by comparing them with the activity sounds of honeybees. If the collection unit detects an abnormal ambient sound (for example, the buzzing of giant hornets), it can respond quickly. The collection unit can also monitor changes in ambient sounds and understand the health status of the honeybees. This allows for the rapid detection of anomalies by collecting ambient sounds and comparing them with the activity sounds of honeybees. Specific types of ambient sounds and collection methods include, but are not limited to, ambient noise and the buzzing of honeybees.

[0045] The collection unit can collect light intensity inside and outside the hive and analyze it in relation to the activity patterns of honeybees. For example, the collection unit can collect light intensity inside and outside the hive and analyze it in relation to the activity patterns of honeybees. The collection unit monitors changes in light intensity to understand the health status of the honeybees. The collection unit can also analyze the relationship between light intensity and honeybee activity patterns to maintain an optimal environment. This allows for the maintenance of an optimal environment by collecting light intensity and analyzing it in relation to the activity patterns of honeybees. Specific measurement methods and criteria for light intensity include, but are not limited to, the sensors used and the measurement range.

[0046] The analysis unit can analyze collected data over time to understand long-term trends. For example, the analysis unit analyzes collected data over time to understand long-term trends. The analysis unit analyzes changes in data over time to detect anomalies. Based on long-term data, the analysis unit can also predict the health status of honeybees. This allows for the understanding of long-term trends and early detection of anomalies by analyzing data over time. Specific methods and criteria for time-series analysis include, but are not limited to, the algorithms used and the scope of the analysis.

[0047] The analysis unit can cluster the collected data and identify anomalous patterns. For example, the analysis unit clusters the collected data and identifies anomalous patterns. Based on the clustering results, the analysis unit identifies the cause of the anomaly. The analysis unit can also analyze the anomalous patterns and respond quickly. This allows for the identification of anomalous patterns and rapid response through data clustering. Specific clustering methods and criteria include, but are not limited to, the algorithm used and the number of clusters.

[0048] The analysis unit can analyze collected data in relation to geographical information to understand the characteristics of each region. For example, the analysis unit analyzes collected data in relation to geographical information to understand the characteristics of each region. Based on the geographical information, the analysis unit analyzes the activity patterns of honeybees. The analysis unit can also understand the characteristics of each region and maintain an optimal environment. Thus, by analyzing data in relation to geographical information, it is possible to understand the characteristics of each region and maintain an optimal environment. Specific types of geographical information and methods of collection include, but are not limited to, GPS data and regional climate data.

[0049] The analysis unit can compare the collected data with data from other beekeepers and perform benchmarking. For example, the analysis unit can compare the collected data with data from other beekeepers and perform benchmarking. The analysis unit can use data from other beekeepers as a reference to maintain an optimal environment. Based on the benchmarking results, the analysis unit can also improve the efficiency of beekeeping. This improves the efficiency of beekeeping by comparing it with data from other beekeepers. Specific methods and criteria for benchmarking include, but are not limited to, the metrics to be compared and the scope of the data.

[0050] The control unit can issue instructions to automatically control the ventilation system inside the beehive based on the analysis results of the generating AI. For example, the control unit issues instructions to automatically control the ventilation system inside the beehive based on the analysis results of the generating AI. The control unit optimizes the temperature and humidity inside the beehive by controlling the ventilation system. The control unit can also maintain the health of the bees by controlling the ventilation system. This allows for the optimization of temperature and humidity inside the beehive through automatic control of the ventilation system. The specific configuration and control method of the ventilation system include, but are not limited to, the devices used and the scope of control.

[0051] The instruction unit can issue instructions to adjust the lighting inside the beehive based on the analysis results of the generating AI. For example, the instruction unit can issue instructions to adjust the lighting inside the beehive based on the analysis results of the generating AI. The instruction unit optimizes the bee activity patterns by adjusting the lighting. The instruction unit can also optimize the environment inside the beehive by adjusting the lighting. This allows for the optimization of bee activity patterns by adjusting the lighting. Specific types of lighting and adjustment methods include, but are not limited to, the types of lights used and the range of adjustment.

[0052] The control unit can issue instructions to notify not only the manager but also nearby beekeepers when an anomaly occurs. For example, the control unit can issue instructions to notify not only the manager but also nearby beekeepers when an anomaly occurs. The control unit will cooperate with nearby beekeepers to respond quickly to the anomaly. The control unit can also share information about the anomaly and improve the efficiency of beekeeping. This enables a quick response by notifying nearby beekeepers when an anomaly occurs. Specific definitions and criteria for an anomaly include, but are not limited to, examples such as temperature or humidity being outside the normal range or abnormal bee behavior.

[0053] The control unit can issue instructions to transmit camera footage from inside the hive to the administrator in real time when an anomaly occurs. For example, when an anomaly occurs, the control unit can issue instructions to transmit camera footage from inside the hive to the administrator in real time. Based on the camera footage, the control unit can identify the cause of the anomaly. The control unit can also respond quickly based on the real-time footage. This enables a rapid response by transmitting camera footage in real time when an anomaly occurs. Specific definitions and criteria for an anomaly include, but are not limited to, examples such as temperature or humidity being outside the normal range or abnormal bee behavior.

[0054] The measuring unit can compensate for the effects of temperature and humidity inside the beehive when measuring the amount of honey. For example, the measuring unit can compensate for the effect of temperature inside the beehive when measuring the amount of honey. The measuring unit can compensate for the effect of humidity inside the beehive when measuring the amount of honey. The measuring unit can also accurately measure the amount of honey by taking into account changes in temperature and humidity. In this way, accurate measurement of the amount of honey can be achieved by compensating for the effects of temperature and humidity. Specific methods and criteria for compensation include, but are not limited to, the algorithm used and the range of compensation.

[0055] The measuring unit can improve the accuracy of honey measurement by considering other factors within the hive (e.g., the number of bees) when measuring the amount of honey. The measuring unit can also accurately measure the amount of honey by considering the activity patterns of the bees. This improves measurement accuracy by considering other factors. Specific examples of other factors and methods of consideration include, but are not limited to, the number of bees and the condition of the hive.

[0056] The measurement unit can collect vibration data inside the beehive when measuring the amount of honey and detect abnormalities. For example, when measuring the amount of honey, the measurement unit collects vibration data inside the beehive and detects abnormalities. Based on the vibration data, the measurement unit identifies abnormalities inside the beehive. The measurement unit can also analyze the vibration data to understand the health status of the bees. This allows for rapid detection of abnormalities by collecting vibration data. The specific types of vibration data and collection methods include, for example, the sensors used and the collection range, but are not limited to these examples.

[0057] The measurement unit can collect acoustic data inside the beehive when measuring the amount of honey, and can understand the activity level of the bees. For example, when measuring the amount of honey, the measurement unit collects acoustic data inside the beehive and understands the activity level of the bees. Based on the acoustic data, the measurement unit can understand the health of the bees. The measurement unit can also analyze the acoustic data and identify the activity patterns of the bees. Thus, by collecting acoustic data, the activity level of the bees can be understood. Specific types of acoustic data and collection methods include, but are not limited to, the microphones used and the collection range.

[0058] The harvesting unit can optimize the temperature and humidity inside the hive when harvesting honey. For example, the harvesting unit optimizes the temperature inside the hive when harvesting honey. The harvesting unit can also maintain the health of the bees by optimizing temperature and humidity. Specific methods and criteria for optimization include, but are not limited to, the algorithms used and the scope of optimization.

[0059] The harvesting unit can employ methods to minimize bee activity when harvesting honey. For example, the harvesting unit can minimize vibrations during harvesting to suppress bee activity. The harvesting unit can also minimize noise during harvesting to suppress bee activity. This reduces the impact of harvesting by minimizing bee activity. Specific details and criteria for minimizing methods include, but are not limited to, the devices used and the scope of suppression.

[0060] The harvesting unit can transmit real-time camera footage from inside the beehive to the administrator when harvesting honey. For example, when harvesting honey, the harvesting unit transmits real-time camera footage from inside the beehive to the administrator. Based on the camera footage, the harvesting unit can monitor the progress of the harvest. The harvesting unit can also improve harvesting efficiency based on the real-time footage. This allows for monitoring the progress of the harvest by transmitting camera footage in real time. The specific time range and update frequency of "real-time" include, but are not limited to, seconds or minutes.

[0061] The harvesting unit can collect vibration data from inside the beehive when harvesting honey, thereby minimizing the impact of the harvest. For example, the harvesting unit can collect vibration data from inside the beehive when harvesting honey, minimizing the impact of the harvest. Based on this vibration data, the harvesting unit minimizes vibration during harvesting. The harvesting unit can also analyze the vibration data to suppress bee activity. Thus, by collecting vibration data, the impact of the harvest can be minimized. Specific types of vibration data and collection methods include, but are not limited to, the sensors used and the collection range.

[0062] The data collection unit can collect temperature and humidity inside and outside the hive in real time. For example, the data collection unit measures the temperature and humidity inside the hive with sensors and collects the data in real time. The data collection unit can similarly collect temperature and humidity outside the hive. This allows for the maintenance of an optimal environment by collecting temperature and humidity inside and outside the hive in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds or minutes.

[0063] The collection unit can collect camera images from both inside and outside the beehive. For example, the collection unit can collect camera images from inside the beehive to monitor the health and abnormalities of the bees. The collection unit can similarly collect camera images from outside the beehive. This allows for monitoring the health and abnormalities of the bees by collecting camera images from both inside and outside the beehive. The specific types and resolutions of the camera images include, but are not limited to, still images, videos, and a range of resolutions.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analysis unit can learn the behavioral patterns of honeybees based on the collected data and predict abnormal behavior. For example, the analysis unit analyzes the flight patterns and movements within the hive to detect signs of abnormal behavior. The analysis unit can also send notifications to administrators if abnormal behavior is predicted. This enables early detection of abnormal behavior and a rapid response. Furthermore, the analysis unit can identify the cause of the abnormal behavior and propose appropriate countermeasures.

[0066] The data collection unit can collect not only temperature and humidity inside and outside the beehive, but also meteorological data such as wind speed and wind direction. For example, the data collection unit can measure wind speed around the beehive to understand factors that affect honeybee flight. The data collection unit can also measure wind direction to predict honeybee flight routes. By collecting meteorological data, it becomes possible to understand honeybee behavior more accurately and provide an optimal environment.

[0067] The analysis unit can not only predict the health of honeybees based on the collected data, but also assess the risk of pathogen outbreaks within the hive. For example, the analysis unit analyzes temperature and humidity data to identify environments where pathogens are likely to thrive. If the risk of pathogen outbreaks is high, the analysis unit can also send a notification to the manager. This helps prevent pathogen outbreaks and maintain the health of the honeybees.

[0068] The data collection unit can collect not only temperature and humidity inside and outside the hive, but also environmental data such as light intensity and UV levels. For example, the data collection unit can measure the light intensity around the hive to understand factors that affect bee activity. The data collection unit can also measure UV levels to assess the health of the bees. By collecting environmental data in this way, it becomes possible to more accurately understand bee behavior and provide an optimal environment.

[0069] The analysis unit can not only predict the health of the bees based on the collected data, but also evaluate the nutritional status within the hive. For example, the analysis unit analyzes bee activity data to identify signs of nutritional deficiency. If the nutritional status deteriorates, the analysis unit can also send a notification to the manager. This allows for proper management of nutritional status and maintenance of the bees' health.

[0070] The data collection unit can collect data not only on temperature and humidity inside and outside the beehive, but also on soil pH and nutrients. For example, the unit can measure the pH of the soil around the beehive to understand factors that affect bee activity. The unit can also measure soil nutrients to assess the health of the bees. By collecting soil data, it becomes possible to more accurately understand bee behavior and provide an optimal environment.

[0071] The analysis unit can not only predict the health of the bees based on the collected data, but also assess the stress level within the hive. For example, the analysis unit analyzes the bees' activity data and identifies signs of stress. If the stress level is high, the analysis unit can also send a notification to the manager. This allows for proper management of stress levels and maintenance of the bees' health.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit uses sensors and cameras to collect data from inside and outside the hive. The collection unit can collect temperature and humidity inside and outside the hive in real time, and also collect camera images from inside and outside the hive. This allows for monitoring of the bees' health and any abnormalities. Step 2: The analysis unit uses a generating AI to analyze the data collected by the collection unit. Based on the collected data, the generating AI analyzes the health of the bees and the environment inside the hive, and issues instructions to maintain the optimal environment. Step 3: The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the analysis unit. The instruction unit can issue instructions to adjust the temperature and humidity inside the hive and control the ventilation system, humidifier, etc. Step 4: The measurement unit measures the amount of honey using a weight sensor. The measurement unit can also measure the amount of honey stored in the tank inside the beehive in real time and correct for the effects of temperature and humidity. Step 5: The harvesting unit automatically harvests honey based on the amount of honey measured by the measuring unit. The harvesting unit automatically harvests honey when the measured amount of honey exceeds a certain standard, employing a method that minimizes the activity of bees during harvesting.

[0074] (Example of form 2) The automated beekeeping system according to an embodiment of the present invention is a system that achieves the automation of beekeeping by utilizing an AI agent. This system uses sensors and cameras to monitor the inside and outside of the beehive in real time and automatically manages the health of the bees, the temperature and humidity inside the beehive, and the timing of honey harvesting. The collected data is analyzed by a generating AI and instructions are given to maintain the optimal environment. Furthermore, it is equipped with a mechanism that measures the amount of honey using a weight sensor and automatically harvests the honey. This enables efficient and sustainable beekeeping and significantly reduces the workload of beekeepers. For example, the entire beehive for beekeeping is designed. Since honeybees have the characteristic of grasping the honeycomb structure (hexagonal grid) that forms the basis of their comb, the base of the beehive is made of honeycomb structured resin. This honeycomb structure is sloped inside, and a tank is installed along the slope so that honey can be stored. A weight sensor is installed at the bottom of this tank to monitor the amount stored. In addition, temperature and humidity sensors and cameras are installed inside and outside the structure for continuous monitoring. This information is analyzed by feeding structured data in JSON format and unstructured data separately to a generating AI. In the event of an anomaly that could potentially destroy a beehive (for example, a wax moth outbreak or a giant hornet attack), the system immediately notifies the administrator to prompt action. This system aims to lower the barriers to entry into beekeeping and increase the number of beekeepers, not only those who keep bees as a business but also those who keep them as a hobby. Increasing the beekeeping population will also indirectly benefit the pollination of natural plants, thus leading to more direct and effective nature conservation activities compared to educational campaigns. As a result, this automated beekeeping system will enable efficient and sustainable beekeeping, significantly reducing the workload of beekeepers.

[0075] The automated beekeeping system according to this embodiment comprises a collection unit, an analysis unit, an instruction unit, a measurement unit, and a harvesting unit. The collection unit collects data inside and outside the beehive using sensors and cameras. The collection unit can, for example, collect temperature and humidity inside and outside the beehive in real time. The collection unit can also collect camera images inside and outside the beehive. For example, the collection unit measures the temperature and humidity inside the beehive with sensors and collects the data in real time. The collection unit can similarly collect temperature and humidity outside the beehive. Furthermore, the collection unit can collect camera images inside and outside the beehive to monitor the health status and any abnormalities of the bees. The analysis unit uses a generating AI to analyze the data collected by the collection unit. For example, the analysis unit provides the collected data to the generating AI in JSON format, and the generating AI analyzes it. Based on the collected data, the generating AI analyzes the health status of the bees and the environment inside the beehive. For example, the generating AI analyzes the temperature and humidity data inside the beehive and issues instructions to maintain an optimal environment. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the analysis unit. For example, the instruction unit issues instructions to adjust the temperature and humidity inside the beehive based on the analysis results of the generating AI. The instruction unit can control ventilation systems, humidifiers, etc., to maintain the temperature and humidity inside the beehive within the optimal range. The measurement unit measures the amount of honey using a weight sensor. For example, the measurement unit can measure the amount of honey stored in a tank inside the beehive in real time. The measurement unit can also compensate for the effects of temperature and humidity in order to accurately measure the amount of honey. The harvesting unit automatically harvests honey based on the amount of honey measured by the measurement unit. For example, the harvesting unit can automatically harvest honey when the measured amount of honey exceeds a certain standard. The harvesting unit can also employ methods to minimize the activity of bees during harvesting. As a result, the automated beekeeping system according to this embodiment can achieve automation and efficiency in beekeeping and significantly reduce the workload of beekeepers.

[0076] The data collection unit uses sensors and cameras to collect data from inside and outside the beehive. Specifically, it can collect temperature and humidity inside and outside the beehive in real time. Since temperature and humidity inside the beehive greatly affect the health and activity of honeybees, it is important to collect this data accurately. The data collection unit uses temperature and humidity sensors installed inside the beehive to acquire environmental data inside the beehive in real time. This allows for constant monitoring of temperature and humidity fluctuations inside the beehive and immediate response if an abnormality occurs. The data collection unit can also collect temperature and humidity outside the beehive. Environmental data outside the beehive is important as reference information when adjusting the environment inside the beehive. For example, if the outside temperature is high, measures to lower the temperature inside the beehive will be necessary. Furthermore, the data collection unit can collect camera images inside and outside the beehive to monitor the health and abnormalities of the honeybees. The cameras capture the movements and behavior of honeybees inside the beehive and collect them as image data. This allows for early detection of abnormal behavior and signs of disease in honeybees. The collected data is transmitted to a central database and used for analysis by the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and instruction units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The analysis unit uses a generating AI to analyze data collected by the collection unit. Specifically, the collected data is provided to the generating AI in JSON format, and the generating AI analyzes it. Based on the collected data, the generating AI analyzes the health of the bees and the environment inside the hive. For example, the generating AI analyzes data on temperature and humidity inside the hive and issues instructions to maintain an optimal environment. Based on past data and statistical information, the generating AI can predict the behavior patterns of bees and environmental fluctuations. This allows the analysis unit to quickly and accurately analyze the collected data and provide information to optimize the environment inside the hive. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if the temperature inside the hive rises sharply or the activity of bees decreases abnormally, the generating AI can detect the anomaly and issue instructions to take appropriate measures. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0078] The instruction unit issues instructions to maintain an optimal environment based on the analysis results obtained by the analysis unit. Specifically, it issues instructions to adjust the temperature and humidity inside the hive based on the analysis results of the generating AI. The instruction unit can control ventilation systems, humidifiers, etc., to maintain the temperature and humidity inside the hive within an optimal range. For example, if the temperature inside the hive is too high, it will issue instructions to activate the ventilation system to lower the temperature. Also, if the humidity inside the hive is too low, it will issue instructions to activate the humidifier to raise the humidity. In this way, the instruction unit can always maintain the environment inside the hive in an optimal state. Furthermore, the instruction unit can also issue instructions to maintain the health of the bees. For example, if the activity of the bees is decreasing, it can issue instructions to improve the environment inside the hive. In this way, the instruction unit can provide information to maintain the health of the bees and optimize the environment inside the hive.

[0079] The measurement unit measures the amount of honey using a weight sensor. Specifically, it can measure the amount of honey stored in the tank inside the beehive in real time. The measurement unit can also correct for the effects of temperature and humidity in order to accurately measure the amount of honey. For example, if the temperature inside the beehive is high, the viscosity of the honey may decrease and the weight may fluctuate, so temperature correction ensures accurate measurement. In addition, the measurement unit can instruct the harvesting unit to automatically start harvesting when the amount of honey exceeds a certain standard. This allows the measurement unit to accurately measure the amount of honey and optimize the timing of harvesting. Furthermore, the measurement unit can provide the collected data to the analysis unit, which can then provide information to understand the honey production status. In this way, the measurement unit can play an important role in improving the efficiency of honey production.

[0080] The harvesting unit automatically harvests honey based on the amount measured by the measuring unit. Specifically, it can automatically harvest honey when the measured amount exceeds a certain standard. The harvesting unit can also employ methods to minimize the activity of bees during harvesting. For example, the harvesting unit can minimize the impact on bees by harvesting during times when bees are not inside the hive. In addition, the harvesting unit can employ a quiet design and vibration-reducing mechanism to avoid disturbing bees during harvesting. This reduces stress on bees and allows for efficient honey harvesting. Furthermore, the harvesting unit can automatically transfer the harvested honey to a tank for storage. This automates the entire process from honey harvesting to storage, significantly reducing the workload of beekeepers. The harvesting unit can also store the harvested honey at appropriate temperatures and humidity to maintain its quality. This allows the harvesting unit to efficiently harvest while preserving honey quality.

[0081] The data collection unit can collect temperature and humidity inside and outside the hive in real time. For example, the data collection unit measures the temperature and humidity inside the hive with sensors and collects the data in real time. The data collection unit can similarly collect temperature and humidity outside the hive. This allows for the maintenance of an optimal environment by collecting temperature and humidity inside and outside the hive in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds or minutes.

[0082] The collection unit can collect camera images from both inside and outside the beehive. For example, the collection unit can collect camera images from inside the beehive to monitor the health and abnormalities of the bees. The collection unit can similarly collect camera images from outside the beehive. This allows for monitoring the health and abnormalities of the bees by collecting camera images from both inside and outside the beehive. The specific types and resolutions of the camera images include, but are not limited to, still images, videos, and a range of resolutions.

[0083] The analysis unit provides the collected data to the generating AI in JSON format, which then analyzes it. For example, the analysis unit provides the collected data to the generating AI in JSON format, which then analyzes it. Based on the collected data, the generating AI analyzes the health of the bees and the environment inside the hive. This allows for efficient data analysis by providing the collected data to the generating AI in JSON format. The specific structure and content of the JSON format include, for example, key-value pairs and a hierarchical data structure, but are not limited to these examples.

[0084] The control unit can issue instructions to adjust the temperature and humidity inside the beehive based on the analysis results of the generating AI. For example, the control unit can control ventilation systems, humidifiers, etc., to maintain the temperature and humidity inside the beehive within an optimal range. This allows for the maintenance of an optimal environment by adjusting the temperature and humidity based on the analysis results of the generating AI. Specific methods and criteria for adjusting the temperature and humidity include, but are not limited to, the devices used and the range of adjustment.

[0085] The measurement unit can measure the amount of honey in real time. For example, it can measure the amount of honey stored in a tank inside a beehive in real time. The measurement unit can also compensate for the effects of temperature and humidity in order to accurately measure the amount of honey. This allows for the optimization of harvest timing by measuring the amount of honey in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds or minutes.

[0086] The harvesting unit can automatically harvest honey based on the measured amount of honey. For example, the harvesting unit automatically harvests honey when the measured amount of honey exceeds a certain standard. The harvesting unit can also employ methods to minimize the activity of bees during harvesting. This allows for efficient harvesting by automatically harvesting honey based on the measured amount. Specific methods and standards for automatic harvesting include, but are not limited to, the timing of harvesting and the equipment used.

[0087] The control unit can issue instructions to send a notification to the administrator when an anomaly occurs. For example, the control unit can issue instructions to send a notification to the administrator when an anomaly occurs. The control unit can describe the details of the anomaly to encourage a prompt response. This enables a quick response by notifying the administrator when an anomaly occurs. Specific definitions and criteria for an anomaly include, but are not limited to, examples such as temperature or humidity being outside the normal range or abnormal bee behavior.

[0088] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will reduce the frequency of data collection to lessen the system load. If the user is relaxed, the data collection unit will increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can also prioritize the collection of only the most important data. This reduces the system load by adjusting the frequency of data collection according to the user's emotions. Specific methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis.

[0089] The data collection unit can predict changes in temperature and humidity inside and outside the hive and optimize the timing of data collection based on these predictions. For example, the unit can collect data before temperatures change rapidly to minimize the impact of the change. It can also collect data before humidity rises to maintain the health of the bees. The unit can also collect data at seasonal transitions to respond to environmental changes. This allows for data collection at the optimal time by predicting changes in temperature and humidity. Specific methods and criteria for prediction include, but are not limited to, the algorithms used and the accuracy of the predictions.

[0090] The data collection unit analyzes camera images from inside and outside the hive and can increase the collection frequency if abnormal behavior is detected. For example, if the data collection unit detects abnormal behavior in honeybees, it increases the collection frequency to collect detailed data. If the data collection unit detects an abnormality inside the hive, it increases the collection frequency to respond quickly. The data collection unit can also increase the collection frequency to understand the situation if it detects an abnormality outside (for example, an attack by giant hornets). This allows for a quick response by increasing the collection frequency when abnormal behavior is detected. Specific definitions and criteria for abnormal behavior include, but are not limited to, patterns of honeybee movement and frequency of behavior.

[0091] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit will prioritize collecting only the most important data. If the user is relaxed, the unit will prioritize collecting detailed data. If the user is in a hurry, the unit can also prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by prioritizing the data to be collected according to the user's emotions. Specific methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis.

[0092] The collection unit can collect ambient sounds around the beehive and detect anomalies by comparing them with the activity sounds of honeybees. For example, the collection unit can collect ambient sounds around the beehive and detect anomalies by comparing them with the activity sounds of honeybees. If the collection unit detects an abnormal ambient sound (for example, the buzzing of giant hornets), it can respond quickly. The collection unit can also monitor changes in ambient sounds and understand the health status of the honeybees. This allows for the rapid detection of anomalies by collecting ambient sounds and comparing them with the activity sounds of honeybees. Specific types of ambient sounds and collection methods include, but are not limited to, ambient noise and the buzzing of honeybees.

[0093] The collection unit can collect light intensity inside and outside the hive and analyze it in relation to the activity patterns of honeybees. For example, the collection unit can collect light intensity inside and outside the hive and analyze it in relation to the activity patterns of honeybees. The collection unit monitors changes in light intensity to understand the health status of the honeybees. The collection unit can also analyze the relationship between light intensity and honeybee activity patterns to maintain an optimal environment. This allows for the maintenance of an optimal environment by collecting light intensity and analyzing it in relation to the activity patterns of honeybees. Specific measurement methods and criteria for light intensity include, but are not limited to, the sensors used and the measurement range.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple display method. If the user is relaxed, the analysis unit provides a detailed display method. If the user is in a hurry, the analysis unit can also provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0095] The analysis unit can analyze collected data over time to understand long-term trends. For example, the analysis unit analyzes collected data over time to understand long-term trends. The analysis unit analyzes changes in data over time to detect anomalies. Based on long-term data, the analysis unit can also predict the health status of honeybees. This allows for the understanding of long-term trends and early detection of anomalies by analyzing data over time. Specific methods and criteria for time-series analysis include, but are not limited to, the algorithms used and the scope of the analysis.

[0096] The analysis unit can cluster the collected data and identify anomalous patterns. For example, the analysis unit clusters the collected data and identifies anomalous patterns. Based on the clustering results, the analysis unit identifies the cause of the anomaly. The analysis unit can also analyze the anomalous patterns and respond quickly. This allows for the identification of anomalous patterns and rapid response through data clustering. Specific clustering methods and criteria include, but are not limited to, the algorithm used and the number of clusters.

[0097] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides concise results. If the user is relaxed, the analysis unit provides detailed results. If the user is in a hurry, the analysis unit can also provide concise results. In this way, by adjusting the level of detail in the analysis results according to the user's emotions, the system can provide the user with appropriate information. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0098] The analysis unit can analyze collected data in relation to geographical information to understand the characteristics of each region. For example, the analysis unit analyzes collected data in relation to geographical information to understand the characteristics of each region. Based on the geographical information, the analysis unit analyzes the activity patterns of honeybees. The analysis unit can also understand the characteristics of each region and maintain an optimal environment. Thus, by analyzing data in relation to geographical information, it is possible to understand the characteristics of each region and maintain an optimal environment. Specific types of geographical information and methods of collection include, but are not limited to, GPS data and regional climate data.

[0099] The analysis unit can compare the collected data with data from other beekeepers and perform benchmarking. For example, the analysis unit can compare the collected data with data from other beekeepers and perform benchmarking. The analysis unit can use data from other beekeepers as a reference to maintain an optimal environment. Based on the benchmarking results, the analysis unit can also improve the efficiency of beekeeping. This improves the efficiency of beekeeping by comparing it with data from other beekeepers. Specific methods and criteria for benchmarking include, but are not limited to, the metrics to be compared and the scope of the data.

[0100] The instruction unit can estimate the user's emotions and adjust the urgency of instructions based on those emotions. For example, if the user is stressed, the instruction unit will prioritize less urgent instructions. If the user is relaxed, the instruction unit will prioritize more urgent instructions. If the user is in a hurry, the instruction unit can also issue instructions that require a quick response. This reduces the user's burden by adjusting the urgency of instructions according to their emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0101] The control unit can issue instructions to automatically control the ventilation system inside the beehive based on the analysis results of the generating AI. For example, the control unit issues instructions to automatically control the ventilation system inside the beehive based on the analysis results of the generating AI. The control unit optimizes the temperature and humidity inside the beehive by controlling the ventilation system. The control unit can also maintain the health of the bees by controlling the ventilation system. This allows for the optimization of temperature and humidity inside the beehive through automatic control of the ventilation system. The specific configuration and control method of the ventilation system include, but are not limited to, the devices used and the scope of control.

[0102] The instruction unit can issue instructions to adjust the lighting inside the beehive based on the analysis results of the generating AI. For example, the instruction unit can issue instructions to adjust the lighting inside the beehive based on the analysis results of the generating AI. The instruction unit optimizes the bee activity patterns by adjusting the lighting. The instruction unit can also optimize the environment inside the beehive by adjusting the lighting. This allows for the optimization of bee activity patterns by adjusting the lighting. Specific types of lighting and adjustment methods include, but are not limited to, the types of lights used and the range of adjustment.

[0103] The instruction unit can estimate the user's emotions and customize the instructions based on those emotions. For example, if the user is stressed, the instruction unit will provide concise instructions. If the user is relaxed, the instruction unit will provide detailed instructions. If the user is in a hurry, the instruction unit can also provide instructions that allow for quick responses. In this way, by customizing the instructions according to the user's emotions, appropriate instructions can be provided to the user. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0104] The control unit can issue instructions to notify not only the manager but also nearby beekeepers when an anomaly occurs. For example, the control unit can issue instructions to notify not only the manager but also nearby beekeepers when an anomaly occurs. The control unit will cooperate with nearby beekeepers to respond quickly to the anomaly. The control unit can also share information about the anomaly and improve the efficiency of beekeeping. This enables a quick response by notifying nearby beekeepers when an anomaly occurs. Specific definitions and criteria for an anomaly include, but are not limited to, examples such as temperature or humidity being outside the normal range or abnormal bee behavior.

[0105] The control unit can issue instructions to transmit camera footage from inside the hive to the administrator in real time when an anomaly occurs. For example, when an anomaly occurs, the control unit can issue instructions to transmit camera footage from inside the hive to the administrator in real time. Based on the camera footage, the control unit can identify the cause of the anomaly. The control unit can also respond quickly based on the real-time footage. This enables a rapid response by transmitting camera footage in real time when an anomaly occurs. Specific definitions and criteria for an anomaly include, but are not limited to, examples such as temperature or humidity being outside the normal range or abnormal bee behavior.

[0106] The measurement unit can estimate the user's emotions and adjust the display method of the measurement data based on the estimated emotions. For example, if the user is stressed, the measurement unit provides a simple display method. If the user is relaxed, the measurement unit provides a detailed display method. If the user is in a hurry, the measurement unit can also provide a concise display method. By adjusting the display method of the measurement data according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0107] The measuring unit can compensate for the effects of temperature and humidity inside the beehive when measuring the amount of honey. For example, the measuring unit can compensate for the effect of temperature inside the beehive when measuring the amount of honey. The measuring unit can compensate for the effect of humidity inside the beehive when measuring the amount of honey. The measuring unit can also accurately measure the amount of honey by taking into account changes in temperature and humidity. In this way, accurate measurement of the amount of honey can be achieved by compensating for the effects of temperature and humidity. Specific methods and criteria for compensation include, but are not limited to, the algorithm used and the range of compensation.

[0108] The measuring unit can improve the accuracy of honey measurement by considering other factors within the hive (e.g., the number of bees) when measuring the amount of honey. The measuring unit can also accurately measure the amount of honey by considering the activity patterns of the bees. This improves measurement accuracy by considering other factors. Specific examples of other factors and methods of consideration include, but are not limited to, the number of bees and the condition of the hive.

[0109] The measurement unit can estimate the user's emotions and prioritize measurement data based on the estimated emotions. For example, if the user is stressed, the measurement unit will prioritize displaying only important measurement data. If the user is relaxed, the measurement unit will prioritize displaying detailed measurement data. If the user is in a hurry, the measurement unit can also prioritize displaying measurement data that can be displayed quickly. In this way, important data can be prioritized by prioritizing measurement data according to the user's emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0110] The measurement unit can collect vibration data inside the beehive when measuring the amount of honey and detect abnormalities. For example, when measuring the amount of honey, the measurement unit collects vibration data inside the beehive and detects abnormalities. Based on the vibration data, the measurement unit identifies abnormalities inside the beehive. The measurement unit can also analyze the vibration data to understand the health status of the bees. This allows for rapid detection of abnormalities by collecting vibration data. The specific types of vibration data and collection methods include, for example, the sensors used and the collection range, but are not limited to these examples.

[0111] The measurement unit can collect acoustic data inside the beehive when measuring the amount of honey, and can understand the activity level of the bees. For example, when measuring the amount of honey, the measurement unit collects acoustic data inside the beehive and understands the activity level of the bees. Based on the acoustic data, the measurement unit can understand the health of the bees. The measurement unit can also analyze the acoustic data and identify the activity patterns of the bees. Thus, by collecting acoustic data, the activity level of the bees can be understood. Specific types of acoustic data and collection methods include, but are not limited to, the microphones used and the collection range.

[0112] The harvesting unit can estimate the user's emotions and adjust the harvesting timing based on those emotions. For example, if the user is stressed, the harvesting unit will delay the harvesting time. If the user is relaxed, the harvesting unit will speed up the harvesting time. If the user is in a hurry, the harvesting unit can also select a time for quick harvesting. By adjusting the harvesting timing according to the user's emotions, the burden on the user can be reduced. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0113] The harvesting unit can optimize the temperature and humidity inside the hive when harvesting honey. For example, the harvesting unit optimizes the temperature inside the hive when harvesting honey. The harvesting unit can also maintain the health of the bees by optimizing temperature and humidity. Specific methods and criteria for optimization include, but are not limited to, the algorithms used and the scope of optimization.

[0114] The harvesting unit can employ methods to minimize bee activity when harvesting honey. For example, the harvesting unit can minimize vibrations during harvesting to suppress bee activity. The harvesting unit can also minimize noise during harvesting to suppress bee activity. This reduces the impact of harvesting by minimizing bee activity. Specific details and criteria for minimizing methods include, but are not limited to, the devices used and the scope of suppression.

[0115] The harvesting unit can estimate the user's emotions and customize the harvesting method based on those emotions. For example, if the user is stressed, the harvesting unit provides a concise harvesting method. If the user is relaxed, the harvesting unit provides a detailed harvesting method. If the user is in a hurry, the harvesting unit can also provide a method for quick harvesting. By customizing the harvesting method according to the user's emotions, the system can provide the user with an appropriate harvesting method. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0116] The harvesting unit can transmit real-time camera footage from inside the beehive to the administrator when harvesting honey. For example, when harvesting honey, the harvesting unit transmits real-time camera footage from inside the beehive to the administrator. Based on the camera footage, the harvesting unit can monitor the progress of the harvest. The harvesting unit can also improve harvesting efficiency based on the real-time footage. This allows for monitoring the progress of the harvest by transmitting camera footage in real time. The specific time range and update frequency of "real-time" include, but are not limited to, seconds or minutes.

[0117] The harvesting unit can collect vibration data from inside the beehive when harvesting honey, thereby minimizing the impact of the harvest. For example, the harvesting unit can collect vibration data from inside the beehive when harvesting honey, minimizing the impact of the harvest. Based on this vibration data, the harvesting unit minimizes vibration during harvesting. The harvesting unit can also analyze the vibration data to suppress bee activity. Thus, by collecting vibration data, the impact of the harvest can be minimized. Specific types of vibration data and collection methods include, but are not limited to, the sensors used and the collection range.

[0118] The data collection unit can collect temperature and humidity inside and outside the hive in real time. For example, the data collection unit measures the temperature and humidity inside the hive with sensors and collects the data in real time. The data collection unit can similarly collect temperature and humidity outside the hive. This allows for the maintenance of an optimal environment by collecting temperature and humidity inside and outside the hive in real time. The specific time range and update frequency of "real time" include, but are not limited to, seconds or minutes.

[0119] The collection unit can collect camera images from both inside and outside the beehive. For example, the collection unit can collect camera images from inside the beehive to monitor the health and abnormalities of the bees. The collection unit can similarly collect camera images from outside the beehive. This allows for monitoring the health and abnormalities of the bees by collecting camera images from both inside and outside the beehive. The specific types and resolutions of the camera images include, but are not limited to, still images, videos, and a range of resolutions.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The analysis unit can learn the behavioral patterns of honeybees based on the collected data and predict abnormal behavior. For example, the analysis unit analyzes the flight patterns and movements within the hive to detect signs of abnormal behavior. The analysis unit can also send notifications to administrators if abnormal behavior is predicted. This enables early detection of abnormal behavior and a rapid response. Furthermore, the analysis unit can identify the cause of the abnormal behavior and propose appropriate countermeasures.

[0122] The control unit can not only adjust the temperature and humidity inside the hive, but also issue instructions to reduce the stress levels of the bees. For example, it can adjust the lighting inside the hive to provide a relaxing environment for the bees. It can also optimize the acoustic environment inside the hive to reduce stress on the bees. This helps maintain the health of the bees and enables efficient beekeeping.

[0123] The data collection unit can collect not only temperature and humidity inside and outside the beehive, but also meteorological data such as wind speed and wind direction. For example, the data collection unit can measure wind speed around the beehive to understand factors that affect honeybee flight. The data collection unit can also measure wind direction to predict honeybee flight routes. By collecting meteorological data, it becomes possible to understand honeybee behavior more accurately and provide an optimal environment.

[0124] The analysis unit can not only predict the health of honeybees based on the collected data, but also assess the risk of pathogen outbreaks within the hive. For example, the analysis unit analyzes temperature and humidity data to identify environments where pathogens are likely to thrive. If the risk of pathogen outbreaks is high, the analysis unit can also send a notification to the manager. This helps prevent pathogen outbreaks and maintain the health of the honeybees.

[0125] The control unit can not only adjust the temperature and humidity inside the hive, but also issue instructions to optimize the bees' activity. For example, it can adjust the lighting inside the hive to optimize the bees' activity time. It can also adjust the acoustic environment inside the hive to promote communication among the bees. This optimizes the bees' activity and enables efficient beekeeping.

[0126] The data collection unit can collect not only temperature and humidity inside and outside the hive, but also environmental data such as light intensity and UV levels. For example, the data collection unit can measure the light intensity around the hive to understand factors that affect bee activity. The data collection unit can also measure UV levels to assess the health of the bees. By collecting environmental data in this way, it becomes possible to more accurately understand bee behavior and provide an optimal environment.

[0127] The analysis unit can not only predict the health of the bees based on the collected data, but also evaluate the nutritional status within the hive. For example, the analysis unit analyzes bee activity data to identify signs of nutritional deficiency. If the nutritional status deteriorates, the analysis unit can also send a notification to the manager. This allows for proper management of nutritional status and maintenance of the bees' health.

[0128] The control unit can not only adjust the temperature and humidity inside the hive, but also issue instructions to promote bee reproduction. For example, the control unit can adjust the lighting inside the hive to promote bee reproduction. It can also adjust the acoustic environment inside the hive to promote bee reproduction. This optimizes bee reproduction and enables efficient beekeeping.

[0129] The data collection unit can collect data not only on temperature and humidity inside and outside the beehive, but also on soil pH and nutrients. For example, the unit can measure the pH of the soil around the beehive to understand factors that affect bee activity. The unit can also measure soil nutrients to assess the health of the bees. By collecting soil data, it becomes possible to more accurately understand bee behavior and provide an optimal environment.

[0130] The analysis unit can not only predict the health of the bees based on the collected data, but also assess the stress level within the hive. For example, the analysis unit analyzes the bees' activity data and identifies signs of stress. If the stress level is high, the analysis unit can also send a notification to the manager. This allows for proper management of stress levels and maintenance of the bees' health.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit uses sensors and cameras to collect data from inside and outside the hive. The collection unit can collect temperature and humidity inside and outside the hive in real time, and also collect camera images from inside and outside the hive. This allows for monitoring of the bees' health and any abnormalities. Step 2: The analysis unit uses a generating AI to analyze the data collected by the collection unit. Based on the collected data, the generating AI analyzes the health of the bees and the environment inside the hive, and issues instructions to maintain the optimal environment. Step 3: The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the analysis unit. The instruction unit can issue instructions to adjust the temperature and humidity inside the hive and control the ventilation system, humidifier, etc. Step 4: The measurement unit measures the amount of honey using a weight sensor. The measurement unit can also measure the amount of honey stored in the tank inside the beehive in real time and correct for the effects of temperature and humidity. Step 5: The harvesting unit automatically harvests honey based on the amount of honey measured by the measuring unit. The harvesting unit automatically harvests honey when the measured amount of honey exceeds a certain standard, employing a method that minimizes the activity of bees during harvesting.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, instruction unit, measurement unit, and harvesting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data inside and outside the beehive using the sensors and camera 42 of the smart device 14. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the collected data using a generating AI. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The measurement unit measures the amount of honey using the weight sensor of the smart device 14. The harvesting unit automatically harvests honey based on the amount of honey measured by the measurement unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, instruction unit, measurement unit, and harvesting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data inside and outside the beehive using the sensors and camera 42 of the smart glasses 214. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the collected data using a generating AI. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The measurement unit measures the amount of honey using the weight sensor of the smart glasses 214. The harvesting unit automatically harvests honey based on the amount of honey measured by the measurement unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, instruction unit, measurement unit, and harvesting unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit collects data from inside and outside the beehive using the sensors and camera 42 of the headset terminal 314. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the collected data using a generating AI. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The measurement unit measures the amount of honey using the weight sensor of the headset terminal 314. The harvesting unit automatically harvests honey based on the amount of honey measured by the measurement unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, instruction unit, measurement unit, and harvesting unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data inside and outside the beehive using the sensors and camera 42 of the robot 414. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the collected data using a generating AI. The instruction unit issues instructions to maintain the optimal environment based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The measurement unit measures the amount of honey using the weight sensor of the robot 414. The harvesting unit automatically harvests the honey based on the amount of honey measured by the measurement unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A data collection unit that uses sensors and cameras to collect data from inside and outside the nest box, The data collected by the aforementioned collection unit is analyzed by an analysis unit, and the generation AI analyzes the data. An instruction unit that issues instructions for maintaining the optimal environment based on the analysis results obtained by the analysis unit, A measuring unit that measures the amount of honey using a weight sensor, The system includes a harvesting unit that automatically harvests honey based on the amount of honey measured by the aforementioned measuring unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects temperature and humidity data inside and outside the beehive in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect camera images from inside and outside the nest box. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is fed to the generating AI in JSON format, and the generating AI analyzes it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The indicator unit is, Based on the analysis results of the generated AI, it issues instructions to adjust the temperature and humidity inside the nest box. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned measuring unit is Measure the amount of honey in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned harvesting section is The system automatically harvests honey based on the measured amount. The system described in Appendix 1, characterized by the features described herein. (Note 8) The indicator unit is, Issue instructions to contact the administrator when an anomaly occurs. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system predicts changes in temperature and humidity inside and outside the hive and optimizes collection timing based on these predictions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system analyzes camera images from inside and outside the nest box and increases the data collection frequency if abnormal behavior is detected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system collects ambient sounds around the beehive and compares them to the sounds of honeybee activity to detect abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is We collect light intensity data from inside and outside the beehive and analyze it in relation to the activity patterns of honeybees. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The collected data is analyzed over time to understand long-term trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The collected data is clustered to identify anomalous patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The collected data is analyzed in conjunction with geographical information to understand the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The collected data will be compared with data from other beekeepers to perform benchmarking. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, It estimates the user's emotions and adjusts the urgency of instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, Based on the analysis results of the generated AI, it issues instructions to automatically control the ventilation system inside the beehive. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, Based on the analysis results of the generated AI, it issues instructions to adjust the lighting inside the nest box. The system described in Appendix 1, characterized by the features described herein. (Note 24) The indicator unit is, It estimates the user's emotions and customizes the instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The indicator unit is, When an anomaly occurs, instructions will be given to contact not only the administrator but also nearby beekeepers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The indicator unit is, When an anomaly occurs, the system will issue an instruction to send the camera footage from inside the nest box to the administrator in real time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement data is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned measuring unit is When measuring the amount of honey, the temperature and humidity inside the beehive are taken into account. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned measuring unit is When measuring the amount of honey, we take other factors within the beehive into consideration to improve measurement accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned measuring unit is It estimates the user's emotions and prioritizes measurement data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned measuring unit is When measuring the amount of honey, vibration data inside the beehive is collected to detect abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned measuring unit is When measuring the amount of honey, acoustic data is collected inside the beehive to understand the activity level of the bees. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned harvesting section is It estimates the user's emotions and adjusts the harvest timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned harvesting section is When harvesting honey, optimize the temperature and humidity inside the beehive. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned harvesting section is When harvesting honey, employ methods that minimize the activity of honeybees. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned harvesting section is It estimates the user's emotions and customizes the harvesting method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned harvesting section is When harvesting honey, the camera footage from inside the beehive is transmitted to the administrator in real time. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned harvesting section is When harvesting honey, vibration data from inside the beehive is collected to minimize the impact of the harvest. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned collection unit is The system collects temperature and humidity data inside and outside the beehive in real time. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned collection unit is Collect camera images from inside and outside the nest box. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that uses sensors and cameras to collect data from inside and outside the nest box, The data collected by the aforementioned collection unit is analyzed by an analysis unit using a generating AI. An instruction unit that issues instructions for maintaining the optimal environment based on the analysis results obtained by the analysis unit, A measuring unit that measures the amount of honey using a weight sensor, The system includes a harvesting unit that automatically harvests honey based on the amount of honey measured by the aforementioned measuring unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects temperature and humidity data inside and outside the beehive in real time. The system according to feature 1.

3. The aforementioned collection unit is Collect camera images from inside and outside the nest box. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is fed to the generating AI in JSON format, and the generating AI analyzes it. The system according to feature 1.

5. The indicator unit is, Based on the analysis results of the generated AI, it issues instructions to adjust the temperature and humidity inside the nest box. The system according to feature 1.

6. The aforementioned measuring unit is Measure the amount of honey in real time. The system according to feature 1.

7. The aforementioned harvesting section is The system automatically harvests honey based on the measured amount. The system according to feature 1.

8. The indicator unit is, Issue instructions to contact the administrator when an anomaly occurs. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is The system predicts changes in temperature and humidity inside and outside the hive and optimizes the timing of collection based on these predictions. The system according to feature 1.

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