system

Abstract

The system according to this embodiment aims to optimize the cultivation process by appropriately managing environmental conditions, enabling early detection of diseases and quality control. [Solution] The system according to the embodiment comprises a monitoring unit, a control unit, a detection unit, an analysis unit, and a proposal unit. The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. The control unit automatically controls the environmental control device based on the data monitored by the monitoring unit. The detection unit performs quality control and disease detection based on the environment controlled by the control unit. The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process. The proposal unit proposes a cultivation model that takes advantage of the region's specific climate based on the data obtained by the analysis unit and supports branding.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to appropriately manage environmental conditions, detect diseases at an early stage, and perform quality control, and there is room for improvement in improving cultivation efficiency.

[0005] The system according to the embodiment aims to appropriately manage environmental conditions, detect diseases at an early stage and perform quality control, and optimize the cultivation process.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a control unit, a detection unit, an analysis unit, and a proposal unit. The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. The control unit automatically controls the environmental control device based on the data monitored by the monitoring unit. The detection unit performs quality control and disease detection based on the environment controlled by the control unit. The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process. The proposal unit proposes a cultivation model that takes advantage of the region's specific climate based on the data obtained by the analysis unit and supports branding. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately manage environmental conditions, enable early detection of diseases and quality control, and optimize the cultivation process. [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 applicable 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 mushroom cultivation system according to an embodiment of the present invention is a system that realizes environmental management, early detection of diseases, quality control, and improved cultivation efficiency in mushroom cultivation. This system consists of five components: growth monitoring and sensor linkage, environmental control and automatic adjustment, quality control and disease detection, data analysis and cultivation process optimization, and branding and utilization of local resources. For example, growth monitoring and sensor linkage allows for real-time monitoring of mycelial growth, temperature, humidity, and light intensity using cameras and sensors. This makes it possible to constantly understand the growth status of the mushrooms. For example, by measuring the temperature with a temperature sensor and photographing the mycelial growth with a camera, the progress of growth can be confirmed. Next, environmental control and automatic adjustment automatically control the environmental control device to provide the optimal environment for each variety. For example, the environmental control device can be adjusted to maintain the optimal temperature and humidity for a specific variety. This makes it possible to improve the quality of the mushrooms. Furthermore, quality control and disease detection detect diseases and quality abnormalities by color and shape, and determine the shipping standards and classify the mushrooms. For example, images captured by a camera can be analyzed to detect signs of disease. This enables early detection of diseases and allows for the shipment of high-quality mushrooms. Furthermore, data analysis and cultivation process optimization allow for the analysis of optimal cultivation methods from long-term data, leading to continuous improvement. For example, past cultivation data can be analyzed to identify optimal cultivation conditions, thereby improving cultivation efficiency. Finally, branding and utilization of local resources propose cultivation models that leverage the unique climate of the region, supporting branding efforts. For instance, selecting varieties suited to local climate conditions and branding them can enhance their value as a local specialty. This system enables improved mushroom cultivation quality, maximized profits, continuous improvement of cultivation efficiency and risk reduction, and the establishment of unique brands utilizing local resources. For example, consistently producing high-quality mushrooms maximizes profits. Early detection of diseases reduces risks. Furthermore, enhancing the brand value of local specialty products contributes to the revitalization of the local economy.This allows the mushroom cultivation system to improve the quality and maximize profits of mushroom cultivation, continuously improve cultivation efficiency and reduce risks, and establish a unique brand that utilizes local resources.

[0029] The mushroom cultivation system according to this embodiment comprises a monitoring unit, a control unit, a detection unit, an analysis unit, and a proposal unit. The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. The monitoring unit monitors mycelial growth using, for example, a camera and sensors. The monitoring unit can measure temperature using, for example, a temperature sensor. The monitoring unit can measure humidity using, for example, a humidity sensor. The monitoring unit can measure light intensity using, for example, a light sensor. The control unit automatically controls environmental control devices based on the data monitored by the monitoring unit. The control unit can automatically control airflow, for example. The control unit can automatically control a humidifier, for example. The control unit can automatically control a temperature control device, for example. The detection unit performs quality control and disease detection based on the environment controlled by the control unit. The detection unit can detect diseases by color or shape, for example. The detection unit can detect quality abnormalities, for example. The detection unit can determine shipping standards and classify the mushrooms, for example. The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process. For example, the analysis unit can analyze the optimal cultivation method from long-term data. For example, the analysis unit can analyze past cultivation data. For example, the analysis unit can optimize cultivation conditions. The proposal unit proposes a cultivation model that takes advantage of the region's unique climate based on the data obtained by the analysis unit and supports branding. For example, the proposal unit can select varieties suitable for the region's climate conditions. For example, the proposal unit can support branding to enhance the value of the product as a regional specialty. For example, the proposal unit can propose a cultivation model based on the region's climate conditions. As a result, the mushroom cultivation system according to the embodiment can monitor mycelial growth, temperature, humidity, and light intensity in real time, and support environmental control, quality control, disease detection, optimization of the cultivation process, and branding.

[0030] The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. For example, the unit uses cameras and sensors to monitor mycelial growth. Specifically, the cameras periodically capture high-resolution images, meticulously recording the mycelial growth state. This allows for accurate tracking of changes in mycelial growth rate and shape. Temperature sensors measure the temperature of the cultivation environment in real time and transmit the data to a central management system. Humidity sensors measure the humidity in the air, providing data to maintain optimal humidity for mycelial growth. Light sensors measure the light intensity of the cultivation environment, providing information to ensure the necessary light for mycelial growth. These sensors collect all data in real time and transmit it to a central database. This allows the monitoring unit to provide information to maintain the mycelial growth environment in optimal condition at all times. Furthermore, the monitoring unit also has a function to issue alerts when abnormal data is detected, enabling rapid response. For example, if the temperature or humidity exceeds the set range, the system immediately issues an alert and notifies the administrator. This allows the monitoring unit to provide information to maintain the mycelial growth environment in optimal condition at all times and to respond quickly when abnormalities occur.

[0031] The control unit automatically controls the environmental control devices based on data monitored by the monitoring unit. For example, the control unit can automatically control the airflow. Specifically, the airflow control device adjusts the airflow within the cultivation chamber to maintain an optimal air environment for mycelial growth. The humidifier operates automatically based on data from the humidity sensor to maintain the required humidity. The temperature control device operates automatically based on data from the temperature sensor to maintain the temperature within the cultivation chamber within an optimal range. This allows the control unit to automatically maintain an optimal environment for mycelial growth. Furthermore, the control unit can receive data from the monitoring unit in real time and quickly adjust the operation of the environmental control devices. For example, if the temperature changes rapidly, the control unit immediately activates the temperature control device to return the temperature to an appropriate range. Also, if the humidity drops, the control unit activates the humidifier to maintain the humidity within an appropriate range. This allows the control unit to constantly maintain an optimal environment for mycelial growth and to respond quickly if any abnormalities occur.

[0032] The detection unit performs quality control and disease detection based on an environment controlled by the control unit. For example, the detection unit can detect diseases by color or shape. Specifically, it uses cameras and image analysis technology to analyze the color and shape of mycelium and mushrooms in detail and detect signs of disease early. To detect quality abnormalities, the detection unit sets quality standards such as the size, shape, and color of the mushrooms and evaluates the quality based on these standards. To determine the shipping standards and classify the mushrooms, the detection unit collects quality data on the mushrooms and determines the grade based on the shipping standards. As a result, the detection unit can efficiently perform quality control and disease detection and provide high-quality mushrooms that meet the shipping standards. Furthermore, the detection unit can take prompt action by detecting diseases early. For example, if a disease is detected, the detection unit immediately issues an alert and notifies the manager. This allows the manager to take prompt action and prevent the spread of the disease.

[0033] The analysis unit analyzes data detected by the detection unit to optimize the cultivation process. For example, the analysis unit can analyze the optimal cultivation method from long-term data. Specifically, it collects historical cultivation data and uses data analysis technology to identify optimal cultivation conditions. This improves the efficiency of the cultivation process and maximizes yields. By analyzing historical cultivation data, it can evaluate cultivation results under specific conditions and derive the optimal cultivation method. To optimize cultivation conditions, the analysis unit analyzes environmental conditions such as temperature, humidity, and light intensity in detail to identify the optimal conditions. This allows the analysis unit to efficiently optimize the cultivation process and stably produce high-quality mushrooms. Furthermore, the analysis unit can use data analysis technology to identify areas for improvement in the cultivation process and propose efficient cultivation methods. This allows the analysis unit to support the optimization of the cultivation process and achieve the production of high-quality mushrooms.

[0034] The Proposal Department, based on data obtained by the Analysis Department, proposes cultivation models that take advantage of the region's unique climate and supports branding. For example, the Proposal Department can select varieties suitable for the region's climate conditions. Specifically, it analyzes regional climate data to identify the most suitable mushroom varieties for that region. This allows for the proposal of cultivation models suited to the region's climate conditions and the realization of efficient cultivation. To support branding and enhance the value of the product as a regional specialty, the Proposal Department proposes brand strategies that leverage regional characteristics. For example, it proposes cultivation methods that utilize the region's climate conditions and soil characteristics to produce high-quality mushrooms unique to the region. This enhances the value of the product as a regional specialty and supports branding. To propose cultivation models based on regional climate conditions, the Proposal Department conducts a detailed analysis of regional climate data to identify the most suitable cultivation methods for that region. This allows the Proposal Department to propose efficient cultivation models that take advantage of the region's unique climate and support branding. Furthermore, the Proposal Department collaborates with local farmers and producers to support the implementation of the cultivation models. This allows the Proposal Department to support the development and branding of regional agriculture and realize the production of high-quality mushrooms.

[0035] The monitoring unit can monitor mycelial growth, temperature, humidity, and light intensity in real time using a camera and sensors. For example, the monitoring unit can capture images of mycelial growth using a camera and analyze the images to understand the growth status. For example, the monitoring unit can measure temperature using a temperature sensor and acquire temperature data in real time. For example, the monitoring unit can measure humidity using a humidity sensor and acquire humidity data in real time. For example, the monitoring unit can measure light intensity using a light sensor and acquire light intensity data in real time. In this way, mycelial growth, temperature, humidity, and light intensity can be monitored in real time using a camera and sensors. The specific time range and update frequency of "real time" can be set, for example, in seconds or minutes. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input image data captured by the camera into a generating AI, and the generating AI can analyze the image data to understand the growth status of the mycelium.

[0036] The control unit can automatically control the airflow and humidifier based on data monitored by the monitoring unit. For example, by automatically controlling the airflow, the control unit can maintain the optimal airflow for mushroom cultivation. For example, by automatically controlling the humidifier, the control unit can maintain the optimal humidity for mushroom cultivation. For example, the control unit can automatically control the temperature control device based on data from a temperature sensor. This allows for the provision of an optimal environment by automatically controlling the airflow and humidifier. The specific methods and criteria for automatic control can be set based on, for example, the control algorithm and the parameters of the controlled object. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input data acquired from the monitoring unit into a generating AI, which can then calculate the optimal control parameters and automatically control the airflow and humidifier.

[0037] The detection unit can detect diseases and quality abnormalities based on color and shape, determine shipping standards, and classify products. For example, the detection unit can analyze images captured by a camera to detect changes in color or abnormal shapes. For example, the detection unit can use image analysis technology to detect changes in color and shape in order to detect quality abnormalities. For example, the detection unit can make judgments based on quality standards in order to determine shipping standards and classify products. This enables quality control by detecting diseases and quality abnormalities based on color and shape, determining shipping standards, and classifying products. Specific criteria and detection methods for quality abnormalities can be set based on, for example, changes in color and shape, or abnormality thresholds. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data captured by a camera into a generating AI, which can analyze the image data to detect diseases and quality abnormalities.

[0038] The analysis unit can analyze optimal cultivation methods from long-term data and continuously improve them. For example, the analysis unit can analyze past cultivation data to find optimal cultivation conditions. For example, the analysis unit can optimize the cultivation process based on long-term data. For example, the analysis unit can use data analysis techniques to identify optimal parameters in order to optimize cultivation conditions. This allows for improved cultivation efficiency by analyzing optimal cultivation methods from long-term data and continuously improving them. Specific criteria and methods for optimal cultivation methods can be set based on optimization algorithms, for example, which parameters to optimize. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input past cultivation data into a generating AI, which can then analyze the data to identify optimal cultivation conditions.

[0039] The proposal department can propose cultivation models that take advantage of the region's unique climate and support branding. For example, the proposal department can select varieties suitable for the region's climate conditions and brand those varieties to enhance their value as local specialties. For example, the proposal department can propose cultivation models based on the region's climate conditions and support branding. For example, the proposal department can propose branding strategies to enhance the value of local specialties. In this way, by proposing cultivation models that take advantage of the region's unique climate and supporting branding, the value of local specialties can be enhanced. Specific methods and criteria for branding can be set based on, for example, marketing strategies and quality standards for branding. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input regional climate data into a generating AI, and the generating AI can propose an optimal cultivation model.

[0040] The monitoring unit can measure the growth rate of mycelium in real time and immediately detect abnormal growth patterns. For example, the monitoring unit can use a generating AI to analyze mycelial growth images captured by a camera and measure the growth rate in real time. For example, the monitoring unit can use a sensor to detect physical changes associated with mycelial growth and measure the growth rate in real time. For example, the monitoring unit can measure the growth rate of mycelium in real time in response to fluctuations in temperature and humidity and immediately detect abnormal growth patterns. This allows for early detection and resolution of problems by measuring the growth rate of mycelium in real time and immediately detecting abnormal growth patterns. The specific time range and update frequency for real time can be set, for example, in seconds or minutes. Some or all of the above-described processes in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input mycelial growth images captured by a camera into a generating AI, which can measure the growth rate and detect abnormal growth patterns.

[0041] The monitoring unit can record fluctuations in temperature, humidity, and light intensity in real time and immediately notify of abnormal fluctuations. For example, the monitoring unit can record temperature data measured by a temperature sensor in real time and immediately notify of abnormal fluctuations. For example, the monitoring unit can record humidity data measured by a humidity sensor in real time and immediately notify of abnormal fluctuations. For example, the monitoring unit can record light intensity data measured by a light sensor in real time and immediately notify of abnormal fluctuations. This allows for a rapid response by recording fluctuations in temperature, humidity, and light intensity in real time and immediately notifying of abnormal fluctuations. The specific time range and update frequency for real time can be set, for example, in seconds or minutes. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input temperature data measured by a temperature sensor into a generating AI, which can detect abnormal fluctuations and immediately notify of them.

[0042] The control unit can automatically adjust light intensity and nutrient supply in addition to controlling airflow and humidifiers. For example, in addition to controlling airflow, the control unit can automatically adjust light intensity based on light intensity measured by a light sensor. For example, in addition to controlling humidifiers, the control unit can automatically adjust nutrient supply based on nutrient status measured by a nutrient sensor. For example, the control unit can automatically adjust light intensity and nutrient supply along with airflow and humidifier control based on temperature measured by a temperature sensor. This allows for the provision of an optimal environment by automatically adjusting light intensity and nutrient supply in addition to controlling airflow and humidifiers. The specific methods and criteria for automatic adjustment can be set based on, for example, adjustment algorithms and parameters to be adjusted. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or without AI. For example, in addition to controlling airflow and humidifiers, the control unit can input data from light sensors and nutrient sensors into a generating AI, which can then calculate optimal adjustment parameters and automatically adjust light intensity and nutrient supply.

[0043] The control unit can record the operation history of the environmental control device and learn the optimal control pattern. For example, the control unit can record the operation history of the environmental control device, and the generating AI can learn the optimal control pattern. For example, the control unit can use the generating AI to propose the optimal environmental control pattern based on past operation history. For example, the control unit can analyze the operation history of the environmental control device, and the generating AI can automatically adjust the optimal control pattern. In this way, the accuracy of environmental control can be improved by recording the operation history of the environmental control device and learning the optimal control pattern. The specific criteria and methods for the optimal control pattern can be set based on, for example, which parameters to optimize and the optimization algorithm. Some or all of the above processing in the control unit may be performed using AI, or not using AI. For example, the control unit can input the operation history of the environmental control device to the generating AI, and the generating AI can learn the optimal control pattern and perform environmental control.

[0044] The detection unit can detect changes in mycelial growth rate and texture, in addition to changes in color and shape. For example, the detection unit can use a generating AI to analyze changes in the color and shape of mycelial growth captured by a camera to detect diseases. For example, the detection unit can use a sensor to measure the growth rate of mycelial growth and detect abnormal growth patterns. For example, the detection unit can use a texture sensor to detect changes in the texture of mycelial growth and identify signs of disease. This allows for more accurate disease detection by detecting changes in mycelial growth rate and texture, in addition to changes in color and shape. Specific detection methods and criteria for texture changes can be set based on, for example, sensors and algorithms for detecting texture changes. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input changes in the color and shape of mycelial growth captured by a camera into a generating AI, which can then detect diseases.

[0045] The detection unit can immediately propose countermeasures when it detects signs of disease. For example, when the detection unit detects signs of disease, the generating AI can propose the optimal countermeasures. For example, when the detection unit detects signs of disease, the generating AI can propose countermeasures based on past data. For example, when the detection unit detects signs of disease, the generating AI can propose countermeasures in real time, enabling a rapid response. This allows for a rapid response by immediately proposing countermeasures when signs of disease are detected. The specific time range for "immediately" can be set, for example, in seconds or minutes. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on detected signs of disease into the generating AI, and the generating AI can propose the optimal countermeasures.

[0046] The analysis unit can analyze data by combining long-term data with real-time data. For example, the analysis unit can combine long-term data and real-time data so that the generating AI can analyze the optimal cultivation method. For example, the analysis unit can combine historical data and current data so that the generating AI can optimize the cultivation process. For example, the analysis unit can analyze long-term data and real-time data so that the generating AI can adjust the cultivation conditions. This allows for a more accurate analysis of cultivation methods by combining long-term data with real-time data. The specific time range and update frequency of real-time data can be set, for example, in seconds or minutes. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input long-term data and real-time data into the generating AI, which can analyze the data to identify the optimal cultivation method.

[0047] The analysis unit can compare past and current cultivation data to identify optimal cultivation conditions. For example, the analysis unit can compare past and current cultivation data, and the generating AI can identify optimal cultivation conditions. For example, the analysis unit can adjust current cultivation conditions based on past data, and the generating AI can propose the optimal cultivation method. For example, the analysis unit can analyze past and current cultivation data, and the generating AI can optimize the cultivation process. This allows for the identification of optimal cultivation conditions by comparing past and current cultivation data. Specific criteria and methods for optimal cultivation conditions can be set based on, for example, which parameters to optimize, and optimization algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input past and current cultivation data into the generating AI, and the generating AI can identify optimal cultivation conditions.

[0048] The analysis unit can analyze long-term data in combination with data from other growers. For example, the analysis unit can combine long-term data with data from other growers, allowing the generating AI to analyze the optimal cultivation method. For example, the analysis unit can use data from other growers as a reference, allowing the generating AI to optimize the cultivation process. For example, the analysis unit can analyze long-term data and data from other growers, allowing the generating AI to adjust cultivation conditions. This enables a more accurate analysis of cultivation methods by combining long-term data with data from other growers. The specific types and methods of acquiring data from other growers can be set based on, for example, a shared database or data type. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input long-term data and data from other growers into the generating AI, which can analyze the data to identify the optimal cultivation method.

[0049] The analysis unit can identify optimal cultivation conditions by combining and analyzing past cultivation data and market data. For example, the analysis unit can combine past cultivation data and market data, and the generating AI can identify optimal cultivation conditions. For example, the analysis unit can adjust past cultivation data based on market data, and the generating AI can propose the optimal cultivation method. For example, the analysis unit can analyze past cultivation data and market data, and the generating AI can optimize the cultivation process. In this way, by combining and analyzing past cultivation data and market data, optimal cultivation conditions can be identified. The specific criteria and methods for optimal cultivation conditions can be set based on, for example, which parameters to optimize, and optimization algorithms. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input past cultivation data and market data into the generating AI, and the generating AI can identify optimal cultivation conditions.

[0050] The proposal unit can propose cultivation models that take into account not only the climate specific to the region but also the characteristics of the soil and water quality. For example, the proposal unit can propose cultivation models that take into account the characteristics of the soil in addition to the regional climate data. For example, the proposal unit can propose cultivation models that combine water quality data with the climate specific to the region. For example, the proposal unit can analyze the characteristics of the soil and water quality and propose cultivation models that are suitable for the regional climate. In this way, by proposing cultivation models that take into account the characteristics of the soil and water quality in addition to the climate specific to the region, it is possible to provide more appropriate cultivation models. The specific types and measurement methods of soil and water quality characteristics can be set based on, for example, pH value, nutrient content, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input regional climate data and soil and water quality characteristic data into a generating AI, and the generating AI can propose an optimal cultivation model.

[0051] The proposal unit can propose an optimal branding strategy by combining past cultivation data and market data. For example, the proposal unit can combine past cultivation data and market data, and a generative AI can propose an optimal branding strategy. For example, the proposal unit can adjust past cultivation data based on market data, and a generative AI can propose a branding strategy. For example, the proposal unit can analyze past cultivation data and market data, and a generative AI can optimize the branding strategy. In this way, by combining and analyzing past cultivation data and market data, an optimal branding strategy can be proposed. The specific criteria and methods for the optimal branding strategy can be set based on, for example, marketing strategies, quality standards, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input past cultivation data and market data into a generative AI, and the generative AI can propose an optimal branding strategy.

[0052] The proposal unit can propose cultivation models that take into account not only the region's unique climate but also its culture and traditions. For example, the proposal unit can propose cultivation models that take into account the region's culture in addition to the region's climate data. For example, the proposal unit can propose cultivation models that combine the region's unique climate with the region's traditions. For example, the proposal unit can analyze the region's culture and traditions and propose cultivation models that are suitable for the region's climate. This allows for the provision of more appropriate cultivation models by proposing cultivation models that take into account the region's culture and traditions in addition to the region's unique climate. The specific types and methods of consideration of the region's culture and traditions can be set based on, for example, local festivals, traditional cultivation methods, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input region climate data and region culture and tradition data into a generating AI, and the generating AI can propose the optimal cultivation model.

[0053] The proposal unit can propose an optimal branding strategy by combining past cultivation data and consumer preference data. For example, the proposal unit can combine past cultivation data and consumer preference data, and the generating AI can propose an optimal branding strategy. For example, the proposal unit can adjust past cultivation data based on consumer preference data, and the generating AI can propose a branding strategy. For example, the proposal unit can analyze past cultivation data and consumer preference data, and the generating AI can optimize the branding strategy. In this way, by combining and analyzing past cultivation data and consumer preference data, an optimal branding strategy can be proposed. The specific types and acquisition methods of consumer preference data can be set based on, for example, survey results, purchase history, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input past cultivation data and consumer preference data into the generating AI, and the generating AI can propose an optimal branding strategy.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The monitoring unit can predict the harvest time of mushrooms in addition to the growth of mycelium. For example, the monitoring unit can predict the harvest time by analyzing images captured by a camera and understanding the growth stage of the mushrooms. For example, the monitoring unit can predict the harvest time by calculating the growth rate of mushrooms based on temperature and humidity data. For example, the monitoring unit can predict the harvest time by comparing the current growth status with past growth data. This allows for accurate prediction of the mushroom harvest time, enabling the determination of the optimal harvest timing. The methods and criteria for predicting the harvest time can be set based on, for example, image analysis of growth stages, calculation of growth rate, and comparison of past data. Some or all of the above-mentioned processes in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input image data captured by a camera into a generating AI, which can then predict the harvest time.

[0056] In addition to operating the environmental control device, the control unit can automatically correct abnormalities in the cultivation environment. For example, if the control unit detects abnormalities in temperature or humidity, it can immediately perform corrective actions. For example, if the control unit detects abnormalities in light intensity, it can automatically adjust the light intensity. For example, if the control unit detects abnormalities in nutrient supply, it can automatically adjust the nutrient supply. This allows for the maintenance of an optimal cultivation environment by immediately correcting abnormalities in the cultivation environment. Specific methods for detecting and correcting abnormalities can be set based on, for example, abnormality detection by sensors and correction algorithms. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input abnormality data detected by sensors into a generating AI, which can then perform corrective actions.

[0057] The detection unit can detect the nutritional value of mushrooms in addition to signs of disease. For example, the detection unit can analyze images captured by a camera and estimate the nutritional value from the color and shape of the mushrooms. For example, the detection unit can measure the components of mushrooms with a sensor and detect the nutritional value. For example, the detection unit can compare the current nutritional value of mushrooms with past data and detect the nutritional value. This allows for enhanced quality control by accurately detecting the nutritional value of mushrooms. Specific methods and criteria for detecting nutritional value can be set based on, for example, component measurement sensors, image analysis, and comparison of past data. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data captured by a camera into a generating AI, which can then detect the nutritional value.

[0058] The analysis department can analyze consumer feedback in addition to cultivation data. For example, the analysis department can identify areas for improvement in cultivation methods based on consumer survey results. For example, the analysis department can identify popular varieties and cultivation methods based on consumer purchase history. For example, the analysis department can identify areas for improvement in cultivation methods based on consumer reviews. In this way, by analyzing consumer feedback, areas for improvement in cultivation methods can be identified, leading to quality improvement. Specific methods and criteria for analyzing feedback can be set based on, for example, analysis of survey results, analysis of purchase history, and analysis of reviews. Some or all of the above-mentioned processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input consumer feedback data into a generating AI, which can then identify areas for improvement.

[0059] The proposal department can propose marketing strategies to maximize profits, in addition to suggesting cultivation models. For example, the proposal department can suggest the optimal sales timing based on past sales data. For example, the proposal department can identify target markets and propose marketing strategies based on consumer purchase history. For example, the proposal department can suggest optimal sales channels based on local market data. This improves sales efficiency by proposing marketing strategies to maximize profits. The specific methods and criteria for proposing marketing strategies can be set based on, for example, the analysis of sales data, the analysis of purchase history, and the analysis of market data. Some or all of the above-mentioned processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input sales data and market data into a generating AI, and the generating AI can propose a marketing strategy.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. For example, the monitoring unit uses a camera and sensors to monitor mycelial growth, a temperature sensor to measure temperature, a humidity sensor to measure humidity, and a light sensor to measure light intensity. Step 2: The control unit automatically controls the environmental control device based on the data monitored by the monitoring unit. The control unit can, for example, automatically control the airflow, humidifier, and temperature control device. Step 3: The detection unit performs quality control and disease detection based on the environment controlled by the control unit. For example, the detection unit can detect diseases by color or shape, detect quality abnormalities, determine shipping standards, and classify products into grades. Step 4: The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process. For example, the analysis unit can analyze the optimal cultivation method from long-term data, analyze past cultivation data, and optimize cultivation conditions. Step 5: Based on the data obtained by the analysis department, the proposal department will propose a cultivation model that takes advantage of the region's unique climate and support branding. For example, the proposal department can select varieties suitable for the region's climate conditions, support branding to enhance the value of the product as a local specialty, and propose a cultivation model based on the region's climate conditions.

[0062] (Example of form 2) The mushroom cultivation system according to an embodiment of the present invention is a system that realizes environmental management, early detection of diseases, quality control, and improved cultivation efficiency in mushroom cultivation. This system consists of five components: growth monitoring and sensor linkage, environmental control and automatic adjustment, quality control and disease detection, data analysis and cultivation process optimization, and branding and utilization of local resources. For example, growth monitoring and sensor linkage allows for real-time monitoring of mycelial growth, temperature, humidity, and light intensity using cameras and sensors. This makes it possible to constantly understand the growth status of the mushrooms. For example, by measuring the temperature with a temperature sensor and photographing the mycelial growth with a camera, the progress of growth can be confirmed. Next, environmental control and automatic adjustment automatically control the environmental control device to provide the optimal environment for each variety. For example, the environmental control device can be adjusted to maintain the optimal temperature and humidity for a specific variety. This makes it possible to improve the quality of the mushrooms. Furthermore, quality control and disease detection detect diseases and quality abnormalities by color and shape, and determine the shipping standards and classify the mushrooms. For example, images captured by a camera can be analyzed to detect signs of disease. This enables early detection of diseases and allows for the shipment of high-quality mushrooms. Furthermore, data analysis and cultivation process optimization allow for the analysis of optimal cultivation methods from long-term data, leading to continuous improvement. For example, past cultivation data can be analyzed to identify optimal cultivation conditions, thereby improving cultivation efficiency. Finally, branding and utilization of local resources propose cultivation models that leverage the unique climate of the region, supporting branding efforts. For instance, selecting varieties suited to local climate conditions and branding them can enhance their value as a local specialty. This system enables improved mushroom cultivation quality, maximized profits, continuous improvement of cultivation efficiency and risk reduction, and the establishment of unique brands utilizing local resources. For example, consistently producing high-quality mushrooms maximizes profits. Early detection of diseases reduces risks. Furthermore, enhancing the brand value of local specialty products contributes to the revitalization of the local economy.This allows the mushroom cultivation system to improve the quality and maximize profits of mushroom cultivation, continuously improve cultivation efficiency and reduce risks, and establish a unique brand that utilizes local resources.

[0063] The mushroom cultivation system according to this embodiment comprises a monitoring unit, a control unit, a detection unit, an analysis unit, and a proposal unit. The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. The monitoring unit monitors mycelial growth using, for example, a camera and sensors. The monitoring unit can measure temperature using, for example, a temperature sensor. The monitoring unit can measure humidity using, for example, a humidity sensor. The monitoring unit can measure light intensity using, for example, a light sensor. The control unit automatically controls environmental control devices based on the data monitored by the monitoring unit. The control unit can automatically control airflow, for example. The control unit can automatically control a humidifier, for example. The control unit can automatically control a temperature control device, for example. The detection unit performs quality control and disease detection based on the environment controlled by the control unit. The detection unit can detect diseases by color or shape, for example. The detection unit can detect quality abnormalities, for example. The detection unit can determine shipping standards and classify the mushrooms, for example. The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process. For example, the analysis unit can analyze the optimal cultivation method from long-term data. For example, the analysis unit can analyze past cultivation data. For example, the analysis unit can optimize cultivation conditions. The proposal unit proposes a cultivation model that takes advantage of the region's unique climate based on the data obtained by the analysis unit and supports branding. For example, the proposal unit can select varieties suitable for the region's climate conditions. For example, the proposal unit can support branding to enhance the value of the product as a regional specialty. For example, the proposal unit can propose a cultivation model based on the region's climate conditions. As a result, the mushroom cultivation system according to the embodiment can monitor mycelial growth, temperature, humidity, and light intensity in real time, and support environmental control, quality control, disease detection, optimization of the cultivation process, and branding.

[0064] The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. For example, the unit uses cameras and sensors to monitor mycelial growth. Specifically, the cameras periodically capture high-resolution images, meticulously recording the mycelial growth state. This allows for accurate tracking of changes in mycelial growth rate and shape. Temperature sensors measure the temperature of the cultivation environment in real time and transmit the data to a central management system. Humidity sensors measure the humidity in the air, providing data to maintain optimal humidity for mycelial growth. Light sensors measure the light intensity of the cultivation environment, providing information to ensure the necessary light for mycelial growth. These sensors collect all data in real time and transmit it to a central database. This allows the monitoring unit to provide information to maintain the mycelial growth environment in optimal condition at all times. Furthermore, the monitoring unit also has a function to issue alerts when abnormal data is detected, enabling rapid response. For example, if the temperature or humidity exceeds the set range, the system immediately issues an alert and notifies the administrator. This allows the monitoring unit to provide information to maintain the mycelial growth environment in optimal condition at all times and to respond quickly when abnormalities occur.

[0065] The control unit automatically controls the environmental control devices based on data monitored by the monitoring unit. For example, the control unit can automatically control the airflow. Specifically, the airflow control device adjusts the airflow within the cultivation chamber to maintain an optimal air environment for mycelial growth. The humidifier operates automatically based on data from the humidity sensor to maintain the required humidity. The temperature control device operates automatically based on data from the temperature sensor to maintain the temperature within the cultivation chamber within an optimal range. This allows the control unit to automatically maintain an optimal environment for mycelial growth. Furthermore, the control unit can receive data from the monitoring unit in real time and quickly adjust the operation of the environmental control devices. For example, if the temperature changes rapidly, the control unit immediately activates the temperature control device to return the temperature to an appropriate range. Also, if the humidity drops, the control unit activates the humidifier to maintain the humidity within an appropriate range. This allows the control unit to constantly maintain an optimal environment for mycelial growth and to respond quickly if any abnormalities occur.

[0066] The detection unit performs quality control and disease detection based on an environment controlled by the control unit. For example, the detection unit can detect diseases by color or shape. Specifically, it uses cameras and image analysis technology to analyze the color and shape of mycelium and mushrooms in detail and detect signs of disease early. To detect quality abnormalities, the detection unit sets quality standards such as the size, shape, and color of the mushrooms and evaluates the quality based on these standards. To determine the shipping standards and classify the mushrooms, the detection unit collects quality data on the mushrooms and determines the grade based on the shipping standards. As a result, the detection unit can efficiently perform quality control and disease detection and provide high-quality mushrooms that meet the shipping standards. Furthermore, the detection unit can take prompt action by detecting diseases early. For example, if a disease is detected, the detection unit immediately issues an alert and notifies the manager. This allows the manager to take prompt action and prevent the spread of the disease.

[0067] The analysis unit analyzes data detected by the detection unit to optimize the cultivation process. For example, the analysis unit can analyze the optimal cultivation method from long-term data. Specifically, it collects historical cultivation data and uses data analysis technology to identify optimal cultivation conditions. This improves the efficiency of the cultivation process and maximizes yields. By analyzing historical cultivation data, it can evaluate cultivation results under specific conditions and derive the optimal cultivation method. To optimize cultivation conditions, the analysis unit analyzes environmental conditions such as temperature, humidity, and light intensity in detail to identify the optimal conditions. This allows the analysis unit to efficiently optimize the cultivation process and stably produce high-quality mushrooms. Furthermore, the analysis unit can use data analysis technology to identify areas for improvement in the cultivation process and propose efficient cultivation methods. This allows the analysis unit to support the optimization of the cultivation process and achieve the production of high-quality mushrooms.

[0068] The Proposal Department, based on data obtained by the Analysis Department, proposes cultivation models that take advantage of the region's unique climate and supports branding. For example, the Proposal Department can select varieties suitable for the region's climate conditions. Specifically, it analyzes regional climate data to identify the most suitable mushroom varieties for that region. This allows for the proposal of cultivation models suited to the region's climate conditions and the realization of efficient cultivation. To support branding and enhance the value of the product as a regional specialty, the Proposal Department proposes brand strategies that leverage regional characteristics. For example, it proposes cultivation methods that utilize the region's climate conditions and soil characteristics to produce high-quality mushrooms unique to the region. This enhances the value of the product as a regional specialty and supports branding. To propose cultivation models based on regional climate conditions, the Proposal Department conducts a detailed analysis of regional climate data to identify the most suitable cultivation methods for that region. This allows the Proposal Department to propose efficient cultivation models that take advantage of the region's unique climate and support branding. Furthermore, the Proposal Department collaborates with local farmers and producers to support the implementation of the cultivation models. This allows the Proposal Department to support the development and branding of regional agriculture and realize the production of high-quality mushrooms.

[0069] The monitoring unit can monitor mycelial growth, temperature, humidity, and light intensity in real time using a camera and sensors. For example, the monitoring unit can capture images of mycelial growth using a camera and analyze the images to understand the growth status. For example, the monitoring unit can measure temperature using a temperature sensor and acquire temperature data in real time. For example, the monitoring unit can measure humidity using a humidity sensor and acquire humidity data in real time. For example, the monitoring unit can measure light intensity using a light sensor and acquire light intensity data in real time. In this way, mycelial growth, temperature, humidity, and light intensity can be monitored in real time using a camera and sensors. The specific time range and update frequency of "real time" can be set, for example, in seconds or minutes. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input image data captured by the camera into a generating AI, and the generating AI can analyze the image data to understand the growth status of the mycelium.

[0070] The control unit can automatically control the airflow and humidifier based on data monitored by the monitoring unit. For example, by automatically controlling the airflow, the control unit can maintain the optimal airflow for mushroom cultivation. For example, by automatically controlling the humidifier, the control unit can maintain the optimal humidity for mushroom cultivation. For example, the control unit can automatically control the temperature control device based on data from a temperature sensor. This allows for the provision of an optimal environment by automatically controlling the airflow and humidifier. The specific methods and criteria for automatic control can be set based on, for example, the control algorithm and the parameters of the controlled object. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input data acquired from the monitoring unit into a generating AI, which can then calculate the optimal control parameters and automatically control the airflow and humidifier.

[0071] The detection unit can detect diseases and quality abnormalities based on color and shape, determine shipping standards, and classify products. For example, the detection unit can analyze images captured by a camera to detect changes in color or abnormal shapes. For example, the detection unit can use image analysis technology to detect changes in color and shape in order to detect quality abnormalities. For example, the detection unit can make judgments based on quality standards in order to determine shipping standards and classify products. This enables quality control by detecting diseases and quality abnormalities based on color and shape, determining shipping standards, and classifying products. Specific criteria and detection methods for quality abnormalities can be set based on, for example, changes in color and shape, or abnormality thresholds. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data captured by a camera into a generating AI, which can analyze the image data to detect diseases and quality abnormalities.

[0072] The analysis unit can analyze optimal cultivation methods from long-term data and continuously improve them. For example, the analysis unit can analyze past cultivation data to find optimal cultivation conditions. For example, the analysis unit can optimize the cultivation process based on long-term data. For example, the analysis unit can use data analysis techniques to identify optimal parameters in order to optimize cultivation conditions. This allows for improved cultivation efficiency by analyzing optimal cultivation methods from long-term data and continuously improving them. Specific criteria and methods for optimal cultivation methods can be set based on optimization algorithms, for example, which parameters to optimize. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input past cultivation data into a generating AI, which can then analyze the data to identify optimal cultivation conditions.

[0073] The proposal department can propose cultivation models that take advantage of the region's unique climate and support branding. For example, the proposal department can select varieties suitable for the region's climate conditions and brand those varieties to enhance their value as local specialties. For example, the proposal department can propose cultivation models based on the region's climate conditions and support branding. For example, the proposal department can propose branding strategies to enhance the value of local specialties. In this way, by proposing cultivation models that take advantage of the region's unique climate and supporting branding, the value of local specialties can be enhanced. Specific methods and criteria for branding can be set based on, for example, marketing strategies and quality standards for branding. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input regional climate data into a generating AI, and the generating AI can propose an optimal cultivation model.

[0074] The monitoring unit can estimate the user's emotions and adjust the frequency of data acquisition based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the frequency of data acquisition and provide more detailed information. For example, if the user is relaxed, the monitoring unit can decrease the frequency of data acquisition and provide only the minimum necessary information. For example, if the user is in a hurry, the monitoring unit can prioritize the acquisition of only important data and provide it quickly. This allows for the provision of information tailored to the user's needs by adjusting the frequency of data acquisition according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the frequency of acquiring monitoring data.

[0075] The monitoring unit can measure the growth rate of mycelium in real time and immediately detect abnormal growth patterns. For example, the monitoring unit can use a generating AI to analyze mycelial growth images captured by a camera and measure the growth rate in real time. For example, the monitoring unit can use a sensor to detect physical changes associated with mycelial growth and measure the growth rate in real time. For example, the monitoring unit can measure the growth rate of mycelium in real time in response to fluctuations in temperature and humidity and immediately detect abnormal growth patterns. This allows for early detection and resolution of problems by measuring the growth rate of mycelium in real time and immediately detecting abnormal growth patterns. The specific time range and update frequency for real time can be set, for example, in seconds or minutes. Some or all of the above-described processes in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input mycelial growth images captured by a camera into a generating AI, which can measure the growth rate and detect abnormal growth patterns.

[0076] The monitoring unit can record fluctuations in temperature, humidity, and light intensity in real time and immediately notify of abnormal fluctuations. For example, the monitoring unit can record temperature data measured by a temperature sensor in real time and immediately notify of abnormal fluctuations. For example, the monitoring unit can record humidity data measured by a humidity sensor in real time and immediately notify of abnormal fluctuations. For example, the monitoring unit can record light intensity data measured by a light sensor in real time and immediately notify of abnormal fluctuations. This allows for a rapid response by recording fluctuations in temperature, humidity, and light intensity in real time and immediately notifying of abnormal fluctuations. The specific time range and update frequency for real time can be set, for example, in seconds or minutes. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input temperature data measured by a temperature sensor into a generating AI, which can detect abnormal fluctuations and immediately notify of them.

[0077] The control unit can estimate the user's emotions and adjust the priority of environmental control based on the estimated emotions. For example, if the user is stressed, the control unit can increase the priority of environmental control and respond quickly. For example, if the user is relaxed, the control unit can lower the priority of environmental control and make only the necessary adjustments. For example, if the user is in a hurry, the control unit can prioritize only important environmental control and respond quickly. This allows for environmental control that meets the user's needs by adjusting the priority of environmental control according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the priority of environmental control.

[0078] The control unit can automatically adjust light intensity and nutrient supply in addition to controlling airflow and humidifiers. For example, in addition to controlling airflow, the control unit can automatically adjust light intensity based on light intensity measured by a light sensor. For example, in addition to controlling humidifiers, the control unit can automatically adjust nutrient supply based on nutrient status measured by a nutrient sensor. For example, the control unit can automatically adjust light intensity and nutrient supply along with airflow and humidifier control based on temperature measured by a temperature sensor. This allows for the provision of an optimal environment by automatically adjusting light intensity and nutrient supply in addition to controlling airflow and humidifiers. The specific methods and criteria for automatic adjustment can be set based on, for example, adjustment algorithms and parameters to be adjusted. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or without AI. For example, in addition to controlling airflow and humidifiers, the control unit can input data from light sensors and nutrient sensors into a generating AI, which can then calculate optimal adjustment parameters and automatically adjust light intensity and nutrient supply.

[0079] The control unit can record the operation history of the environmental control device and learn the optimal control pattern. For example, the control unit can record the operation history of the environmental control device, and the generating AI can learn the optimal control pattern. For example, the control unit can use the generating AI to propose the optimal environmental control pattern based on past operation history. For example, the control unit can analyze the operation history of the environmental control device, and the generating AI can automatically adjust the optimal control pattern. In this way, the accuracy of environmental control can be improved by recording the operation history of the environmental control device and learning the optimal control pattern. The specific criteria and methods for the optimal control pattern can be set based on, for example, which parameters to optimize and the optimization algorithm. Some or all of the above processing in the control unit may be performed using AI, or not using AI. For example, the control unit can input the operation history of the environmental control device to the generating AI, and the generating AI can learn the optimal control pattern and perform environmental control.

[0080] The detection unit can estimate the user's emotions and adjust the priority of disease detection based on the estimated user emotions. For example, if the user is stressed, the detection unit can increase the priority of disease detection and respond quickly. For example, if the user is relaxed, the detection unit can lower the priority of disease detection and perform only the minimum necessary detections. For example, if the user is in a hurry, the detection unit can prioritize the detection of only important diseases and respond quickly. This allows for disease detection that meets the user's needs by adjusting the priority of disease detection according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the priority for disease detection.

[0081] The detection unit can detect changes in mycelial growth rate and texture, in addition to changes in color and shape. For example, the detection unit can use a generating AI to analyze changes in the color and shape of mycelial growth captured by a camera to detect diseases. For example, the detection unit can use a sensor to measure the growth rate of mycelial growth and detect abnormal growth patterns. For example, the detection unit can use a texture sensor to detect changes in the texture of mycelial growth and identify signs of disease. This allows for more accurate disease detection by detecting changes in mycelial growth rate and texture, in addition to changes in color and shape. Specific detection methods and criteria for texture changes can be set based on, for example, sensors and algorithms for detecting texture changes. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input changes in the color and shape of mycelial growth captured by a camera into a generating AI, which can then detect diseases.

[0082] The detection unit can immediately propose countermeasures when it detects signs of disease. For example, when the detection unit detects signs of disease, the generating AI can propose the optimal countermeasures. For example, when the detection unit detects signs of disease, the generating AI can propose countermeasures based on past data. For example, when the detection unit detects signs of disease, the generating AI can propose countermeasures in real time, enabling a rapid response. This allows for a rapid response by immediately proposing countermeasures when signs of disease are detected. The specific time range for "immediately" can be set, for example, in seconds or minutes. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on detected signs of disease into the generating AI, and the generating AI can propose the optimal countermeasures.

[0083] The analysis unit can estimate the user's emotions and adjust the priority of data analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the priority of data analysis and respond quickly. For example, if the user is relaxed, the analysis unit can lower the priority of data analysis and perform only the minimum necessary analysis. For example, if the user is in a hurry, the analysis unit can prioritize the analysis of only important data and respond quickly. This allows for data analysis tailored to the user's needs by adjusting the priority of data analysis according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, emotion recognition algorithms and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the priority of data analysis.

[0084] The analysis unit can analyze data by combining long-term data with real-time data. For example, the analysis unit can combine long-term data and real-time data so that the generating AI can analyze the optimal cultivation method. For example, the analysis unit can combine historical data and current data so that the generating AI can optimize the cultivation process. For example, the analysis unit can analyze long-term data and real-time data so that the generating AI can adjust the cultivation conditions. This allows for a more accurate analysis of cultivation methods by combining long-term data with real-time data. The specific time range and update frequency of real-time data can be set, for example, in seconds or minutes. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input long-term data and real-time data into the generating AI, which can analyze the data to identify the optimal cultivation method.

[0085] The analysis unit can compare past and current cultivation data to identify optimal cultivation conditions. For example, the analysis unit can compare past and current cultivation data, and the generating AI can identify optimal cultivation conditions. For example, the analysis unit can adjust current cultivation conditions based on past data, and the generating AI can propose the optimal cultivation method. For example, the analysis unit can analyze past and current cultivation data, and the generating AI can optimize the cultivation process. This allows for the identification of optimal cultivation conditions by comparing past and current cultivation data. Specific criteria and methods for optimal cultivation conditions can be set based on, for example, which parameters to optimize, and optimization algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input past and current cultivation data into the generating AI, and the generating AI can identify optimal cultivation conditions.

[0086] The analysis unit can estimate the user's emotions and adjust the frequency of data analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the frequency of data analysis and respond quickly. For example, if the user is relaxed, the analysis unit can decrease the frequency of data analysis and perform only the minimum necessary analysis. For example, if the user is in a hurry, the analysis unit can prioritize the analysis of only important data and respond quickly. This allows for data analysis tailored to the user's needs by adjusting the frequency of data analysis according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the frequency of data analysis.

[0087] The analysis unit can analyze long-term data in combination with data from other growers. For example, the analysis unit can combine long-term data with data from other growers, allowing the generating AI to analyze the optimal cultivation method. For example, the analysis unit can use data from other growers as a reference, allowing the generating AI to optimize the cultivation process. For example, the analysis unit can analyze long-term data and data from other growers, allowing the generating AI to adjust cultivation conditions. This enables a more accurate analysis of cultivation methods by combining long-term data with data from other growers. The specific types and methods of acquiring data from other growers can be set based on, for example, a shared database or data type. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input long-term data and data from other growers into the generating AI, which can analyze the data to identify the optimal cultivation method.

[0088] The analysis unit can identify optimal cultivation conditions by combining and analyzing past cultivation data and market data. For example, the analysis unit can combine past cultivation data and market data, and the generating AI can identify optimal cultivation conditions. For example, the analysis unit can adjust past cultivation data based on market data, and the generating AI can propose the optimal cultivation method. For example, the analysis unit can analyze past cultivation data and market data, and the generating AI can optimize the cultivation process. In this way, by combining and analyzing past cultivation data and market data, optimal cultivation conditions can be identified. The specific criteria and methods for optimal cultivation conditions can be set based on, for example, which parameters to optimize, and optimization algorithms. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input past cultivation data and market data into the generating AI, and the generating AI can identify optimal cultivation conditions.

[0089] The suggestion unit can estimate the user's emotions and adjust the cultivation model suggestion method based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy-to-understand cultivation model. For example, if the user is relaxed, the suggestion unit can suggest a cultivation model that includes detailed information. For example, if the user is in a hurry, the suggestion unit can suggest a cultivation model that gets straight to the point. In this way, by adjusting the cultivation model suggestion method according to the user's emotions, it becomes possible to make suggestions that meet the user's needs. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm, the data used, etc. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into a generative AI, and the generative AI can estimate the emotions and adjust the cultivation model suggestion method.

[0090] The proposal unit can propose cultivation models that take into account not only the climate specific to the region but also the characteristics of the soil and water quality. For example, the proposal unit can propose cultivation models that take into account the characteristics of the soil in addition to the regional climate data. For example, the proposal unit can propose cultivation models that combine water quality data with the climate specific to the region. For example, the proposal unit can analyze the characteristics of the soil and water quality and propose cultivation models that are suitable for the regional climate. In this way, by proposing cultivation models that take into account the characteristics of the soil and water quality in addition to the climate specific to the region, it is possible to provide more appropriate cultivation models. The specific types and measurement methods of soil and water quality characteristics can be set based on, for example, pH value, nutrient content, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input regional climate data and soil and water quality characteristic data into a generating AI, and the generating AI can propose an optimal cultivation model.

[0091] The proposal unit can propose an optimal branding strategy by combining past cultivation data and market data. For example, the proposal unit can combine past cultivation data and market data, and a generative AI can propose an optimal branding strategy. For example, the proposal unit can adjust past cultivation data based on market data, and a generative AI can propose a branding strategy. For example, the proposal unit can analyze past cultivation data and market data, and a generative AI can optimize the branding strategy. In this way, by combining and analyzing past cultivation data and market data, an optimal branding strategy can be proposed. The specific criteria and methods for the optimal branding strategy can be set based on, for example, marketing strategies, quality standards, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input past cultivation data and market data into a generative AI, and the generative AI can propose an optimal branding strategy.

[0092] The suggestion unit can estimate the user's emotions and adjust the frequency of suggested cultivation models based on the estimated emotions. For example, if the user is stressed, the suggestion unit can increase the frequency of suggested cultivation models to respond quickly. For example, if the user is relaxed, the suggestion unit can decrease the frequency of suggested cultivation models and provide only the minimum necessary suggestions. For example, if the user is in a hurry, the suggestion unit can prioritize suggesting only important cultivation models to respond quickly. In this way, by adjusting the frequency of suggested cultivation models according to the user's emotions, it becomes possible to provide suggestions that meet the user's needs. The specific method and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm, the data used, etc. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the frequency of suggestions for the cultivation model.

[0093] The proposal unit can propose cultivation models that take into account not only the region's unique climate but also its culture and traditions. For example, the proposal unit can propose cultivation models that take into account the region's culture in addition to the region's climate data. For example, the proposal unit can propose cultivation models that combine the region's unique climate with the region's traditions. For example, the proposal unit can analyze the region's culture and traditions and propose cultivation models that are suitable for the region's climate. This allows for the provision of more appropriate cultivation models by proposing cultivation models that take into account the region's culture and traditions in addition to the region's unique climate. The specific types and methods of consideration of the region's culture and traditions can be set based on, for example, local festivals, traditional cultivation methods, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input region climate data and region culture and tradition data into a generating AI, and the generating AI can propose the optimal cultivation model.

[0094] The proposal unit can propose an optimal branding strategy by combining past cultivation data and consumer preference data. For example, the proposal unit can combine past cultivation data and consumer preference data, and the generating AI can propose an optimal branding strategy. For example, the proposal unit can adjust past cultivation data based on consumer preference data, and the generating AI can propose a branding strategy. For example, the proposal unit can analyze past cultivation data and consumer preference data, and the generating AI can optimize the branding strategy. In this way, by combining and analyzing past cultivation data and consumer preference data, an optimal branding strategy can be proposed. The specific types and acquisition methods of consumer preference data can be set based on, for example, survey results, purchase history, etc. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input past cultivation data and consumer preference data into the generating AI, and the generating AI can propose an optimal branding strategy.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The monitoring unit can predict the harvest time of mushrooms in addition to the growth of mycelium. For example, the monitoring unit can predict the harvest time by analyzing images captured by a camera and understanding the growth stage of the mushrooms. For example, the monitoring unit can predict the harvest time by calculating the growth rate of mushrooms based on temperature and humidity data. For example, the monitoring unit can predict the harvest time by comparing the current growth status with past growth data. This allows for accurate prediction of the mushroom harvest time, enabling the determination of the optimal harvest timing. The methods and criteria for predicting the harvest time can be set based on, for example, image analysis of growth stages, calculation of growth rate, and comparison of past data. Some or all of the above-mentioned processes in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input image data captured by a camera into a generating AI, which can then predict the harvest time.

[0097] In addition to operating the environmental control device, the control unit can automatically correct abnormalities in the cultivation environment. For example, if the control unit detects abnormalities in temperature or humidity, it can immediately perform corrective actions. For example, if the control unit detects abnormalities in light intensity, it can automatically adjust the light intensity. For example, if the control unit detects abnormalities in nutrient supply, it can automatically adjust the nutrient supply. This allows for the maintenance of an optimal cultivation environment by immediately correcting abnormalities in the cultivation environment. Specific methods for detecting and correcting abnormalities can be set based on, for example, abnormality detection by sensors and correction algorithms. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input abnormality data detected by sensors into a generating AI, which can then perform corrective actions.

[0098] The detection unit can detect the nutritional value of mushrooms in addition to signs of disease. For example, the detection unit can analyze images captured by a camera and estimate the nutritional value from the color and shape of the mushrooms. For example, the detection unit can measure the components of mushrooms with a sensor and detect the nutritional value. For example, the detection unit can compare the current nutritional value of mushrooms with past data and detect the nutritional value. This allows for enhanced quality control by accurately detecting the nutritional value of mushrooms. Specific methods and criteria for detecting nutritional value can be set based on, for example, component measurement sensors, image analysis, and comparison of past data. Some or all of the above-described processes in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input image data captured by a camera into a generating AI, which can then detect the nutritional value.

[0099] The analysis department can analyze consumer feedback in addition to cultivation data. For example, the analysis department can identify areas for improvement in cultivation methods based on consumer survey results. For example, the analysis department can identify popular varieties and cultivation methods based on consumer purchase history. For example, the analysis department can identify areas for improvement in cultivation methods based on consumer reviews. In this way, by analyzing consumer feedback, areas for improvement in cultivation methods can be identified, leading to quality improvement. Specific methods and criteria for analyzing feedback can be set based on, for example, analysis of survey results, analysis of purchase history, and analysis of reviews. Some or all of the above-mentioned processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input consumer feedback data into a generating AI, which can then identify areas for improvement.

[0100] The proposal department can propose marketing strategies to maximize profits, in addition to suggesting cultivation models. For example, the proposal department can suggest the optimal sales timing based on past sales data. For example, the proposal department can identify target markets and propose marketing strategies based on consumer purchase history. For example, the proposal department can suggest optimal sales channels based on local market data. This improves sales efficiency by proposing marketing strategies to maximize profits. The specific methods and criteria for proposing marketing strategies can be set based on, for example, the analysis of sales data, the analysis of purchase history, and the analysis of market data. Some or all of the above-mentioned processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input sales data and market data into a generating AI, and the generating AI can propose a marketing strategy.

[0101] The monitoring unit can estimate the user's emotions and adjust the display method of monitoring data based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple and easy-to-understand display method. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit can prioritize displaying only important data. This makes it possible to provide information that meets the user's needs by adjusting the display method of monitoring data according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display method of monitoring data.

[0102] The control unit can estimate the user's emotions and adjust the environmental control feedback based on the estimated emotions. For example, if the user is stressed, the control unit can provide rapid environmental control feedback. For example, if the user is relaxed, the control unit can provide slow environmental control feedback. For example, if the user is in a hurry, the control unit can prioritize providing only important feedback. This allows for environmental control tailored to the user's needs by adjusting the environmental control feedback according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the environmental control feedback.

[0103] The detection unit can estimate the user's emotions and adjust the disease detection notification method based on the estimated user emotions. For example, if the user is stressed, the detection unit can quickly notify the user of disease detection. For example, if the user is relaxed, the detection unit can slowly notify the user of disease detection. For example, if the user is in a hurry, the detection unit can prioritize notifying only of important diseases. By adjusting the disease detection notification method according to the user's emotions, disease detection that meets the user's needs becomes possible. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the notification method for disease detection.

[0104] The analysis unit can estimate the user's emotions and adjust the display method of the data analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand display method of the results. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can prioritize displaying only the most important data. This allows for the provision of information tailored to the user's needs by adjusting the display method of the data analysis results according to the user's emotions. Specific methods and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the display method of the data analysis results.

[0105] The suggestion unit can estimate the user's emotions and adjust the suggested cultivation model based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy-to-implement cultivation model. For example, if the user is relaxed, the suggestion unit can suggest a cultivation model that includes detailed information. For example, if the user is in a hurry, the suggestion unit can suggest a cultivation model that focuses only on the important points. By adjusting the suggested cultivation model according to the user's emotions, it becomes possible to make suggestions that meet the user's needs. The specific method and criteria for estimating emotions can be set based on, for example, an emotion recognition algorithm and the data used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the suggested cultivation model.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time. For example, the monitoring unit uses a camera and sensors to monitor mycelial growth, a temperature sensor to measure temperature, a humidity sensor to measure humidity, and a light sensor to measure light intensity. Step 2: The control unit automatically controls the environmental control device based on the data monitored by the monitoring unit. The control unit can, for example, automatically control the airflow, humidifier, and temperature control device. Step 3: The detection unit performs quality control and disease detection based on the environment controlled by the control unit. For example, the detection unit can detect diseases by color or shape, detect quality abnormalities, determine shipping standards, and classify products into grades. Step 4: The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process. For example, the analysis unit can analyze the optimal cultivation method from long-term data, analyze past cultivation data, and optimize cultivation conditions. Step 5: Based on the data obtained by the analysis department, the proposal department will propose a cultivation model that takes advantage of the region's unique climate and support branding. For example, the proposal department can select varieties suitable for the region's climate conditions, support branding to enhance the value of the product as a local specialty, and propose a cultivation model based on the region's climate conditions.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the monitoring unit, control unit, detection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time using the camera 42, temperature sensor, humidity sensor, and light sensor of the smart device 14. The control unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically controls the environmental control device based on the data monitored by the monitoring unit. The detection unit detects diseases and quality abnormalities by color and shape using the camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data detected by the detection unit to optimize the cultivation process. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a cultivation model that takes advantage of the region's specific climate based on the data obtained by the analysis unit, supporting branding. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the monitoring unit, control unit, detection unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time using the camera 42, temperature sensor, humidity sensor, and light sensor of the smart glasses 214. The control unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically controls the environmental control device based on the data monitored by the monitoring unit. The detection unit detects diseases and quality abnormalities by color and shape using, for example, the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the data detected by the detection unit to optimize the cultivation process. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes a cultivation model that takes advantage of the region's specific climate based on the data obtained by the analysis unit, and supports branding. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the monitoring unit, control unit, detection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time using the camera 42, temperature sensor, humidity sensor, and light sensor of the headset terminal 314. The control unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically controls the environmental control device based on the data monitored by the monitoring unit. The detection unit detects diseases and quality abnormalities by color and shape using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data detected by the detection unit to optimize the cultivation process. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a cultivation model that takes advantage of the region's specific climate based on the data obtained by the analysis unit, supporting branding. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the monitoring unit, control unit, detection unit, analysis unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the monitoring unit monitors mycelial growth, temperature, humidity, and light intensity in real time using the robot 414's camera 42, temperature sensor, humidity sensor, and light sensor. The control unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically controls the environmental control device based on the data monitored by the monitoring unit. The detection unit detects diseases and quality abnormalities by color and shape using the robot 414's camera 42. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the data detected by the detection unit to optimize the cultivation process. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes a cultivation model that takes advantage of the region's specific climate based on the data obtained by the analysis unit, and supports branding. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A monitoring unit that monitors mycelial growth, temperature, humidity, and light intensity in real time, A control unit that automatically controls the environmental control device based on the data monitored by the aforementioned monitoring unit, A detection unit that performs quality control and disease detection based on the environment controlled by the control unit, The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process, The system includes a proposal unit that proposes cultivation models that utilize the region's unique climate based on data obtained by the aforementioned analysis unit, and supports branding. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Cameras and sensors are used to monitor mycelial growth, temperature, humidity, and light levels in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The control unit, The system automatically controls the airflow and humidifier based on the data monitored by the aforementioned monitoring unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is Diseases and quality abnormalities are detected by color and shape, and the products are graded to determine the shipping standards. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze the optimal cultivation methods from long-term data and continuously improve them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose cultivation models that take advantage of the region's unique climate and support branding. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the frequency of monitoring data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, It measures the growth rate of mycelium in real time and immediately detects abnormal growth patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It records temperature, humidity, and light intensity fluctuations in real time and immediately notifies of abnormal fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The control unit, It estimates the user's emotions and adjusts the priority of environmental controls based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The control unit, In addition to controlling airflow and humidifiers, it also automatically adjusts light intensity and nutrient supply. The system described in Appendix 1, characterized by the features described herein. (Note 12) The control unit, The system records the operation history of the environmental control device and learns the optimal control pattern. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is It estimates the user's emotions and adjusts the priority of disease detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is In addition to changes in color and shape, it also detects changes in the growth rate and texture of mycelium. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is If signs of disease are detected, we will immediately propose countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates user sentiment and adjusts the priority of data analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is The analysis combines long-term data with real-time data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is By comparing past and current cultivation data, we can identify the optimal cultivation conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates user sentiment and adjusts the frequency of data analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is In addition to long-term data, we combine and analyze data from other growers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is By combining and analyzing past cultivation data and market data, we identify the optimal cultivation conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, We estimate the user's emotions and adjust the cultivation model proposal method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, In addition to the region's unique climate, we propose cultivation models that take into account the characteristics of the soil and water quality. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We combine past cultivation data and market data to propose the optimal branding strategy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the frequency of suggested cultivation models based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, In addition to the region's unique climate, we propose cultivation models that take into account local culture and traditions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, We combine past cultivation data with consumer preference data to propose the optimal branding strategy. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 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 monitoring unit that monitors mycelial growth, temperature, humidity, and light intensity in real time, A control unit that automatically controls the environmental control device based on the data monitored by the aforementioned monitoring unit, A detection unit that performs quality control and disease detection based on the environment controlled by the control unit, The analysis unit analyzes the data detected by the detection unit and optimizes the cultivation process, The system includes a proposal unit that proposes cultivation models that utilize the region's unique climate based on data obtained by the aforementioned analysis unit, and supports branding. A system characterized by the following features.

2. The aforementioned monitoring unit, Cameras and sensors are used to monitor mycelial growth, temperature, humidity, and light levels in real time. The system according to feature 1.

3. The control unit, The system automatically controls the airflow and humidifier based on the data monitored by the aforementioned monitoring unit. The system according to feature 1.

4. The detection unit is Diseases and quality abnormalities are detected by color and shape, and the products are graded to determine the shipping standards. The system according to feature 1.

5. The aforementioned analysis unit is Analyze the optimal cultivation methods from long-term data and continuously improve them. The system according to feature 1.

6. The aforementioned proposal section is, We propose cultivation models that take advantage of the region's unique climate and support branding. The system according to feature 1.

7. The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the frequency of monitoring data acquisition based on the estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, It measures the growth rate of mycelium in real time and immediately detects abnormal growth patterns. The system according to feature 1.

Images