system

Abstract

We provide the system. [Solution] Means for acquiring data from sensors to collect environmental data, A means of applying an analytical model to analyze the acquired data, A means for generating a schedule to optimize crop cultivation based on the results of the analysis, A means for analyzing the genetic characteristics of crops and developing varieties with specific traits, A system including means for automatically performing irrigation based on a generated schedule.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [[ID=X13]]

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Extreme weather conditions due to climate change are increasing, making it difficult to achieve stable crop production. In particular, for crops that require a large amount of water such as rice, drought and water shortages cause a decrease in production. In order to solve such problems and realize sustainable agriculture, technologies for efficient use of water resources and improvement of crop climate change tolerance are required.

Means for Solving the Problems

[0005] This invention provides a system that collects environmental data, analyzes that data using an analytical model, and proposes an optimal crop cultivation schedule. Furthermore, it supports the development of varieties with specific characteristics by analyzing the genetic characteristics of crops and has a function to automatically execute irrigation based on the generated schedule. In particular, by utilizing generational AI, it enables efficient data analysis and highly accurate predictions, providing users with appropriate information. This will enable effective agricultural management in response to climate change.

[0006] "Environmental data" refers to various types of information related to the agricultural environment, such as weather conditions, soil conditions, and water resource status.

[0007] A "sensor" is a device used to collect data from the physical environment and has the function of measuring things like temperature, humidity, and rainfall.

[0008] "Means of acquiring data" refers to the process or method of collecting data from sensors or other sources.

[0009] An "analytical model" refers to a mathematical or computational method designed to analyze collected data and derive patterns or predictions.

[0010] "Generative AI" is a type of artificial intelligence technology that refers to a system capable of analyzing data and generating new information or predictions.

[0011] A "schedule for optimizing crop growth" refers to a plan or timetable that indicates the optimal timing for management and tasks during the crop's growth process.

[0012] "Genetic characteristics" refer to the specific genetic makeup and features of a crop, which influence its growth and resilience.

[0013] "Means of automatic execution" refers to processes or functions in which specific tasks are performed autonomously without human intervention, based on pre-set controls or instructions.

[0014] "Communication methods" refer to the technologies and protocols used to send and receive information, which enable data exchange between different devices and systems. [Brief explanation of the drawing]

[0015] [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 the data processing device and 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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system for efficiently managing the cultivation of crops susceptible to the effects of climate change, particularly rice, which requires a large amount of water, and for realizing sustainable agriculture. This system supports crop growth by collecting and analyzing environmental data and generating an optimal growing schedule. Embodiments of the invention are shown below.

[0037] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from various sensors installed in farmland. This makes it possible to understand the current state of the agricultural environment. The collected data is stored in a database on the server and used as input data for analytical models.

[0038] The analysis model utilizes generative AI, and the server uses this model to perform detailed analysis based on the large amount of data collected. Specifically, it predicts future weather patterns based on past data and creates an optimal irrigation schedule to reduce soil moisture stress. Users receive the schedule generated by the server on their devices and further review and adjust it as needed.

[0039] This system features an automated irrigation function to improve the efficiency of irrigation water utilization. The user's terminal receives instructions from the server and activates irrigation equipment at the appropriate time. As a result, it can efficiently provide the water necessary for crops while reducing wasted water consumption.

[0040] Furthermore, the server analyzes information on the genetic characteristics of crops to support the development of new varieties resistant to drought and other environmental stresses. This functionality will facilitate collaboration with research institutions and breeding companies, accelerating the realization of sustainable agriculture.

[0041] By implementing this invention, farmers will be able to respond flexibly to fluctuations in climate conditions and water resources, thereby enhancing the sustainability of agriculture. This will lead to the stabilization of food production and the efficient use of water resources.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from installed sensors. This is done via the network, ensuring data integrity and up-to-dateness.

[0045] Step 2:

[0046] The server stores the acquired environmental data in a database. During this process, a timestamp and geographical location data are assigned to each data point, organizing the data for later analysis.

[0047] Step 3:

[0048] The server uses generative AI to analyze accumulated data and predict weather patterns and soil moisture changes. The AI ​​model compares past and current data to predict future environmental conditions with high accuracy.

[0049] Step 4:

[0050] Based on the analysis results, the server generates an optimal irrigation schedule. This schedule includes specific timings and water volumes to ensure efficient water management, taking into account predicted weather conditions and the current growth stage of the crops.

[0051] Step 5:

[0052] The user's terminal receives the irrigation schedule sent from the server and reviews its contents. The user can then review the schedule and make adjustments as needed to suit local conditions.

[0053] Step 6:

[0054] The terminal controls the irrigation system based on the finalized irrigation schedule. This automates the process of pumps operating at the appropriate times and efficiently supplying the required amount of water.

[0055] Step 7:

[0056] The server analyzes information on the genetic characteristics of crops and generates data on new varieties that are in need of development. This data is provided to research institutions and other organizations to help develop drought-resistant varieties.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern agriculture, sustainable crop production is becoming difficult due to the impacts of climate change and water resource instability. Water resource management, in particular, is a major challenge for environmentally friendly agriculture. Traditional methods often result in inefficient management of irrigation timing and water volume, negatively impacting crop growth and yield. Furthermore, rapid and accurate data analysis is crucial for analyzing the genetic characteristics of crops and developing new varieties.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring information from a device for collecting environmental information, means for applying an analysis program for analyzing the acquired information, and means for automatically executing irrigation based on the generated plan. This makes it possible to achieve sustainable agriculture while efficiently managing water resources. Furthermore, the analysis program using generated AI can analyze the genetic characteristics of crops in detail, accelerating the development of new, resistant varieties.

[0062] "Environmental information" refers to data that indicates environmental conditions in agricultural land, such as temperature, humidity, soil moisture content, and precipitation.

[0063] "Devices" refer to various sensors installed on farmland, which are equipment used to acquire environmental information in real time.

[0064] "Means of acquisition" refers to a process that has the function of collecting environmental information from a device to a server and storing it in a database.

[0065] An "analysis program" is software that analyzes data based on acquired environmental information to evaluate the optimal irrigation schedule and the genetic characteristics of crops.

[0066] The "generated plan" refers to the optimal irrigation schedule created by the analysis program, designed to promote efficient crop growth.

[0067] "Means of automatic execution" refers to a function that controls the irrigation system based on the generated irrigation schedule and automatically supplies water at the appropriate timing and volume.

[0068] "Generative AI" refers to artificial intelligence technology that analyzes large amounts of data, generates predictive models, and supports crop growth.

[0069] "Communication equipment" refers to facilities or technologies equipped with network functions to notify users' terminals of generated schedules and to update information in real time as needed.

[0070] This invention provides a system for realizing sustainable agriculture by utilizing environmental information. Specifically, a server, terminals, and users work together to perform efficient irrigation management and support crop growth.

[0071] The server acquires environmental information from various sensors installed in farmland, such as temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. This data is stored in a database on the server. Next, the server analyzes the acquired data using a generative AI model. This AI model can process large amounts of data and predict future weather conditions.

[0072] Specifically, the server generates an optimal irrigation schedule based on the analysis results. This schedule includes the specific time periods and water amounts for irrigation. The user's terminal receives the irrigation schedule sent from the server and can view it through the terminal's interface. The user can fine-tune the schedule as needed.

[0073] Furthermore, the terminal is connected to an automated irrigation system that automatically performs irrigation based on a user-approved schedule. This operation promotes the efficient use of water resources by ensuring the appropriate timing and amount of water. The server also analyzes the genetic characteristics of crops to support the development of new varieties. This information is useful for selecting and developing sustainable varieties.

[0074] As a concrete example, for a rice farmer in a certain region, the server analyzes past weather data and current environmental information to predict drought due to high temperatures next week. Based on this, the server generates an optimal irrigation schedule to maintain soil moisture and notifies the user's terminal. This allows the user to automatically irrigate according to the schedule, thereby improving the efficiency of water resources.

[0075] An example of a prompt message is: "Generate an optimal irrigation schedule for rice farmers in Tokyo. Based on the weekly forecast for July, and taking into account weather data from the past five years, propose a new irrigation pattern."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server acquires environmental information from sensors installed in farmland. Input data includes temperature, humidity, soil moisture, and precipitation sensors. This data is transmitted from the corresponding sensors and stored in the server's database. The stored data forms the basis for analysis, representing the first step towards efficient crop management.

[0079] Step 2:

[0080] The server inputs environmental information stored in the database into the analysis program. This input includes data such as temperature, humidity, soil moisture content, and precipitation. Using a generative AI model, the data is analyzed to predict future weather patterns. This analysis enables output such as irrigation schedules, achieving the goal of data analysis: improving agricultural efficiency.

[0081] Step 3:

[0082] The server generates an optimal irrigation schedule using the analysis results from a generated AI model. Input data includes future weather patterns and soil moisture levels. During this process, the server considers each piece of data to determine the required amount of water and the timing of irrigation. The output is a specific irrigation schedule, which can then be notified to the user.

[0083] Step 4:

[0084] The user's terminal receives the irrigation schedule sent from the server. The input is the irrigation schedule information from the server. The user checks the schedule on the terminal screen and makes adjustments as needed. The output is the finalized adjusted irrigation schedule, and the system is ready for automatic irrigation.

[0085] Step 5:

[0086] The terminal controls the irrigation system based on the irrigation schedule confirmed and adjusted by the user. The input is the confirmed irrigation schedule. Specifically, it activates the irrigation system at the appropriate time and supplies the specified amount of water. The output is the efficient use of water resources, supporting optimal crop growth.

[0087] Step 6:

[0088] The server monitors the results of irrigation and reflects the results in a database. Inputs include changes in soil moisture content and environmental information after irrigation. Re-analyzing this data allows for further optimization of future irrigation schedules. The output provides highly accurate feedback, enabling continuous improvement of agricultural efficiency.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In agricultural work, it is necessary to effectively manage the growth of plants susceptible to the effects of climate change, minimize water waste, and achieve sustainable agriculture. Furthermore, developing new varieties based on collected environmental information and improving agricultural productivity are key challenges.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for acquiring information from a measuring device for collecting environmental information, means for applying a model for analyzing the acquired information, and means for generating a plan for optimizing plant growth based on the results of the analysis. This makes it possible to achieve efficient water supply and growth optimization, thereby promoting sustainable agriculture.

[0094] "Environmental information" refers to a variety of data used to indicate the conditions of agricultural land, such as temperature, humidity, and soil moisture content.

[0095] "Measuring devices" refer to sensors and devices installed on farmland to continuously collect environmental information.

[0096] "Means of acquisition" refers to the methods and technologies for receiving environmental information transmitted from a measuring device and importing it into a server.

[0097] A "model for analysis" is an algorithm or program that performs current situation analysis and future predictions based on collected environmental information.

[0098] "Generative means" refers to the process of creating work plans and schedules based on the results of the analysis model.

[0099] "Water supply" refers to the actions and systems for systematically providing the water necessary for plant growth.

[0100] "Means of operating the device" refers to a method of controlling automated equipment remotely or by program to perform a desired task.

[0101] "Work equipment" refers to hardware used to support agricultural work, such as irrigation systems and robots, that are placed on farmland.

[0102] The system for realizing this invention acquires environmental information from measuring devices installed in farmland and performs analysis on a server. The server uses hardware such as a Raspberry Pi to collect data such as temperature, humidity, and soil moisture content from sensors in real time. This information is analyzed using a generative AI model. The role of the analysis model is to create an optimal water supply plan, taking into account weather forecasts and soil moisture conditions. This plan is then transmitted to agricultural equipment that supports farm work. This equipment includes automatic irrigation systems and agricultural robots.

[0103] Users can receive information from the server via their terminals and review the generated water supply plan. Adjustments can be made to this plan as needed. For example, in agricultural areas, if there is no rain forecast for a week, the generating AI model adjusts the frequency and amount of water supply to achieve efficient irrigation. Such a system improves the efficiency and sustainability of agriculture.

[0104] As a concrete example, in farmland where rice is planted under arid climate conditions, a generative AI model analyzes weather data and creates an appropriate irrigation schedule. Based on this information, irrigation robots can automatically carry out the work, promoting crop growth while effectively utilizing water resources.

[0105] An example of a prompt message might be: "In a dry climate with no rain forecast for a week, what irrigation schedule should I set to ensure optimal water supply when growing rice?"

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server acquires environmental information (temperature, humidity, soil moisture content, etc.) from measuring devices installed in farmland. This input data is received from the sensors in real time and aggregated by the server as initial processing. Basic filtering is performed to check for any missed data or outliers. The output is a set of scrutinized environmental information.

[0109] Step 2:

[0110] The server inputs the acquired environmental information into a generating AI model for analysis. This model uses past data patterns and weather forecasting algorithms to predict future weather patterns and soil moisture conditions. Data processing includes shaping time-series data and correcting outliers. The output is future weather forecast information and soil moisture stress predictions.

[0111] Step 3:

[0112] The server generates an optimal water supply plan based on the analysis results. An algorithm is used to determine the timing and amount of irrigation, utilizing the output of the generated AI model. This is a calculation result based on prompt statements and is formalized as a water supply plan. The output is a water supply plan document.

[0113] Step 4:

[0114] The user receives the water supply plan from the server via a terminal and reviews its contents. The terminal allows the user to adjust the plan, modifying it as needed to create a customized plan. At this stage, the input is the initial water supply plan, and the output is the modified final plan.

[0115] Step 5:

[0116] The server transmits the finalized water supply plan to the farm's automated irrigation systems and agricultural robots. A communication protocol is used, and the equipment receives operational instructions based on the plan. The input is the user-adjusted water supply plan, and the output is the activation signal for the irrigation system at the appropriate time.

[0117] Step 6:

[0118] The irrigation system operates according to the server's instructions, supplying water as planned. Specific actions include starting pumps and opening / closing valves, thereby supplying the plants with the necessary water. The input is the activation signal, and the output is the actual irrigation activity.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention provides an advanced system to support the sustainable cultivation of crops. In addition to collecting and analyzing environmental data, generating irrigation schedules, and executing automated irrigation, this system incorporates an emotion engine that recognizes user emotions to improve efficiency.

[0121] The server acquires data from various sensors installed on the farm. This includes environmental data such as temperature, humidity, soil moisture, and precipitation, and this data is stored in a database for analysis. The collected data is input into an analysis model using generative AI to precisely predict future weather patterns and crop growth conditions. Based on this prediction, the server generates an irrigation schedule to optimize crop growth.

[0122] The user's device not only receives irrigation schedules from the server, but also uses an emotion engine to recognize the user's emotional state. This engine analyzes emotions through voice and facial expressions, and can then adjust the system interface and notification content accordingly. For example, if the system analyzes that the user is stressed, it can be configured to reduce the frequency of notifications and provide more concise information.

[0123] The emotion engine, receiving feedback from the device, records long-term emotional data of the user and optimizes the system to suit each individual user based on that data. This is expected to improve the user experience and make the system more user-friendly.

[0124] The terminal automatically controls the irrigation system based on an optimized irrigation schedule. This allows crops to efficiently receive the water they need, reducing the wasteful use of water resources. Specifically, in small-scale farm irrigation, optimizing irrigation based on daily weather conditions and emotions supports healthy crop growth.

[0125] This system, configured in this way, is an innovative technology that enables efficient use of water resources and improved productivity in agriculture, while reducing the burden on users.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server acquires environmental data from sensors placed on the farm. Specifically, it collects data such as temperature, humidity, soil moisture content, and precipitation at regular intervals and stores this data.

[0129] Step 2:

[0130] The server stores the collected data in a database and performs analysis using generating AI. This analysis compares past and present data to predict future weather conditions and soil changes.

[0131] Step 3:

[0132] The server generates an optimal irrigation schedule based on the analysis results. This schedule includes irrigation timing and water volume to mitigate predicted water stress.

[0133] Step 4:

[0134] The user's device receives the irrigation schedule sent from the server. The received schedule is displayed on the user interface, allowing the user to review its contents. The user can also adjust the schedule as needed.

[0135] Step 5:

[0136] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this, it adjusts the content and timing of notifications. For example, if it determines that the user is tired, the notification settings are changed to be more concise, conveying only the essential points.

[0137] Step 6:

[0138] The terminal automatically controls the irrigation system based on a confirmed irrigation schedule. Signals from the terminal activate the pumps at the appropriate times, supplying the specified amount of water.

[0139] Step 7:

[0140] The server receives and records long-term user sentiment data from the sentiment engine. This past sentiment data is then analyzed to optimize the interface so that the system better suits the user in the future.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] Modern agriculture demands flexible responses to environmental conditions and climate change. Furthermore, efficiently utilizing water resources while providing optimal growing conditions for individual crops is a challenge. Additionally, a lack of information provision and operational support that considers the emotions and stress levels of agricultural workers hinders improvements in work efficiency and user experience. Therefore, a system is needed to address these complex challenges.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an analysis algorithm for analyzing the acquired information, means for generating a plan for optimizing plant growth based on the analysis results, means for analyzing a person's emotional state from voice and facial expressions and adjusting the system's operation, and means for automatically supplying water based on the generated plan. This enables adaptation to environmental changes in agriculture, optimization of crop growth, and provision of information and operational support that takes into account the user's emotions.

[0146] "Environmental information" refers to data on external conditions in agriculture, such as temperature, humidity, soil moisture content, and precipitation.

[0147] "Sensing devices" refer to sensors and measuring instruments used to collect environmental information.

[0148] "Means of acquiring information" refers to methods and technologies for receiving data obtained from sensing devices and incorporating it into a system.

[0149] An "analysis algorithm" refers to the computational procedures and models used to derive important insights and predictions based on collected data.

[0150] "Means of generating plans" refers to methods for designing schedules and actions to optimize crop cultivation and resource utilization based on the results of analysis.

[0151] "Means for analyzing emotional states" refers to methods and technologies for evaluating a user's emotions through voice and facial expressions, and for understanding that state within a system.

[0152] "Means of automatically supplying water" refers to the automatic control of equipment and systems that supply the required amount of water to crops based on a planned schedule.

[0153] This invention relates to an agricultural support system based on data collection, analysis, optimization, and emotion recognition. The system is configured as follows:

[0154] The server collects environmental information using various sensors. These sensors include temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. These sensors are installed on the farm and transmit data to the server in real time. This data is stored in a database and processed by the server's analysis algorithms.

[0155] The server analyzes the collected data through a generative AI model. Based on the predictive information obtained from this analysis, it generates a plan to optimize plant growth and formulates an irrigation schedule. This model utilizes machine learning technology, enabling future predictions based on historical data.

[0156] The user's device uses an emotion engine to analyze voice and facial expressions and evaluate the user's emotional state. Based on this evaluation, the device can provide information and operate the system according to the user's state. Furthermore, the device automatically controls the irrigation equipment based on the irrigation schedule sent from the server.

[0157] For example, if the server predicts high temperatures the following day based on data from sensors on a given day, irrigation will be carried out with an increased water volume. On the other hand, if the system determines that the user is experiencing stress, the frequency of notifications will be reduced.

[0158] An example of a prompt message might be: "Analyze the farm's environmental data to generate an optimal irrigation schedule and adjust notifications based on the user's stress level."

[0159] This system will improve the efficiency of agricultural work, the efficient use of water resources, and provide users with a comfortable operating experience.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server acquires environmental information from various sensing devices placed on the farm. This information includes temperature, humidity, soil moisture content, and precipitation. It receives data transmitted in real time from the sensing devices and stores it in a database. In this process, sensor data is the input, and a structured environmental information database is the output.

[0163] Step 2:

[0164] The server inputs environmental information stored in the database into a generating AI model. This model uses machine learning algorithms to predict future weather patterns and crop growth conditions based on past data. In this analysis process, environmental information serves as input data, and future weather and crop growth predictions are output.

[0165] Step 3:

[0166] The server develops an irrigation plan to optimize plant growth based on the output from the generating AI model. In generating the plan, it analyzes predictive data to determine the required water volume and irrigation timing. The input is the prediction result from the generating AI, and the output is a detailed irrigation schedule.

[0167] Step 4:

[0168] The user's device receives the irrigation schedule sent from the server and uses an emotion engine to analyze the user's voice and facial expressions. This analysis evaluates the user's emotional state. The input to the analysis process is voice and facial expression data obtained from the user, and the output is information about the user's emotional state.

[0169] Step 5:

[0170] The device generates information and notifications tailored to the user's state based on the results of emotion analysis. It adjusts the frequency and content of notifications according to the user's stress level, providing a more user-friendly interface. The input is emotional state information, and the output is the adjusted notifications and interface.

[0171] Step 6:

[0172] The terminal automatically controls the irrigation system based on the irrigation schedule from the server. Specifically, it operates valves and pumps at the appropriate times to supply the crops with the necessary water. The irrigation schedule functions as the input, and the actual water supply is the output.

[0173] In this way, servers and terminals work together to achieve automation and efficiency in agriculture.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] In modern agriculture and factory automation, there is a need for efficient process management while flexibly responding to environmental changes. Furthermore, reducing user stress and building interactive systems are also crucial challenges. Existing systems lack adaptive adjustment functions that respond to user needs, making it difficult to improve the user experience.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an evaluation method for analyzing the acquired information, and means for using an emotion engine for analyzing the user's emotional state. This enables flexible and efficient environmental control while providing notifications adjusted according to the user's emotional state.

[0179] "Environmental information" refers to data about the conditions of the target environment, such as ambient temperature, humidity, and noise level.

[0180] A "sensing device" is a device that detects and acquires environmental information, and incorporates sensors.

[0181] "Means of acquiring information" refers to the functions and methods for collecting environmental information obtained from sensing devices.

[0182] An "evaluation method" is a process or algorithm used to analyze acquired environmental information and formulate optimal control and planning strategies based on the results.

[0183] "User's emotional state" refers to the user's feelings and psychological state, which are factors that trigger specific behaviors and responses.

[0184] An "emotion engine" is a system that analyzes the user's emotional state and dynamically adjusts the system's response and interface based on the results.

[0185] "Means of providing notifications" refers to devices or functions used to convey information or alerts to users, such as messages or alert sounds.

[0186] The system in this invention is composed of several hardware and software components in order to improve the efficiency of environmental control and process management in factories and agricultural sites.

[0187] The server acquires environmental information from multiple sensing devices and stores it in a database. These sensing devices include various sensors that detect temperature, humidity, and noise levels. This data is stored on a cloud-based platform, such as AWS®. The acquired data undergoes preprocessing using Python, after which an evaluation method is applied. A generative AI model is used to analyze the data for evaluation purposes, and based on the obtained information, optimal processes and control plans are generated.

[0188] The device notifies the user of necessary information based on the results of the evaluation method. This includes the end user's smartphone or PC. Notifications are provided flexibly after analyzing the user's emotional state using an emotion engine, thus improving the user experience. This emotion engine performs speech recognition and facial expression analysis, and adjusts the system's feedback function.

[0189] As a concrete example, consider a manager leading a parts assembly process in a small factory. If the emotion engine detects that the manager is under high stress, the notifications the user receives will be limited to important progress information, reducing the user's burden and providing a more efficient work environment.

[0190] As an example of a prompt message, it can be set in text format as, "Generate the optimal process control schedule based on today's factory temperature and humidity data." This enables efficient control, resulting in improved efficiency and productivity.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server acquires environmental information from various sensing devices. This environmental information includes temperature, humidity, and noise levels. The acquired data is sent to a cloud-based platform, where the environmental information is stored in a database.

[0194] Step 2:

[0195] The server retrieves environmental information stored in the database and preprocesses the data using Python. This preprocessing includes correcting missing values ​​and removing noise. The preprocessed data is then input into an evaluation method and analyzed using a generative AI model. This analysis outputs an optimal process control schedule based on the environmental information.

[0196] Step 3:

[0197] The terminal receives an optimal process control schedule sent from the server. The received schedule is then notified to the user. At this time, the terminal analyzes the user's emotional state using an emotion engine. The emotion engine performs speech recognition and facial expression analysis, and adjusts the notification content based on the results. Specifically, if the user is feeling stressed, the notification content is narrowed down to the essentials and adjusted to prevent information overload.

[0198] Step 4:

[0199] The user controls the process based on notifications from their device. Based on the information received on the device, the user makes decisions and provides feedback to the environment. This feedback information is sent to the server and incorporated into the next cycle of the system. This allows the entire system to dynamically adapt to the user's situation.

[0200] By following these steps, efficient environmental control and process management that takes into account the user's emotional state can be achieved.

[0201] 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.

[0202] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0208] 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.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0210] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] 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.

[0212] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The 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.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention provides a system for efficiently managing the cultivation of crops susceptible to the effects of climate change, particularly rice, which requires a large amount of water, and for realizing sustainable agriculture. This system supports crop growth by collecting and analyzing environmental data and generating an optimal growing schedule. Embodiments of the invention are shown below.

[0218] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from various sensors installed in farmland. This makes it possible to understand the current state of the agricultural environment. The collected data is stored in a database on the server and used as input data for analytical models.

[0219] The analysis model utilizes generative AI, and the server uses this model to perform detailed analysis based on the large amount of data collected. Specifically, it predicts future weather patterns based on past data and creates an optimal irrigation schedule to reduce soil moisture stress. Users receive the schedule generated by the server on their devices and further review and adjust it as needed.

[0220] This system features an automated irrigation function to improve the efficiency of irrigation water utilization. The user's terminal receives instructions from the server and activates irrigation equipment at the appropriate time. As a result, it can efficiently provide the water necessary for crops while reducing wasted water consumption.

[0221] Furthermore, the server analyzes information on the genetic characteristics of crops to support the development of new varieties resistant to drought and other environmental stresses. This functionality will facilitate collaboration with research institutions and breeding companies, accelerating the realization of sustainable agriculture.

[0222] By implementing this invention, farmers will be able to respond flexibly to fluctuations in climate conditions and water resources, thereby enhancing the sustainability of agriculture. This will lead to the stabilization of food production and the efficient use of water resources.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from installed sensors. This is done via the network, ensuring data integrity and up-to-dateness.

[0226] Step 2:

[0227] The server stores the acquired environmental data in a database. During this process, a timestamp and geographical location data are assigned to each data point, organizing the data for later analysis.

[0228] Step 3:

[0229] The server uses generative AI to analyze accumulated data and predict weather patterns and soil moisture changes. The AI ​​model compares past and current data to predict future environmental conditions with high accuracy.

[0230] Step 4:

[0231] Based on the analysis results, the server generates an optimal irrigation schedule. This schedule includes specific timings and water volumes to ensure efficient water management, taking into account predicted weather conditions and the current growth stage of the crops.

[0232] Step 5:

[0233] The user's terminal receives the irrigation schedule sent from the server and reviews its contents. The user can then review the schedule and make adjustments as needed to suit local conditions.

[0234] Step 6:

[0235] The terminal controls the irrigation system based on the finalized irrigation schedule. This automates the process of pumps operating at the appropriate times and efficiently supplying the required amount of water.

[0236] Step 7:

[0237] The server analyzes information on the genetic characteristics of crops and generates data on new varieties that are in need of development. This data is provided to research institutions and other organizations to help develop drought-resistant varieties.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] In modern agriculture, sustainable crop production is becoming difficult due to the impacts of climate change and water resource instability. Water resource management, in particular, is a major challenge for environmentally friendly agriculture. Traditional methods often result in inefficient management of irrigation timing and water volume, negatively impacting crop growth and yield. Furthermore, rapid and accurate data analysis is crucial for analyzing the genetic characteristics of crops and developing new varieties.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for acquiring information from a device for collecting environmental information, means for applying an analysis program for analyzing the acquired information, and means for automatically executing irrigation based on the generated plan. This makes it possible to achieve sustainable agriculture while efficiently managing water resources. Furthermore, the analysis program using generated AI can analyze the genetic characteristics of crops in detail, accelerating the development of new, resistant varieties.

[0243] "Environmental information" refers to data that indicates environmental conditions in agricultural land, such as temperature, humidity, soil moisture content, and precipitation.

[0244] "Devices" refer to various sensors installed on farmland, which are equipment used to acquire environmental information in real time.

[0245] "Means of acquisition" refers to a process that has the function of collecting environmental information from a device to a server and storing it in a database.

[0246] An "analysis program" is software that analyzes data based on acquired environmental information to evaluate the optimal irrigation schedule and the genetic characteristics of crops.

[0247] The "generated plan" refers to the optimal irrigation schedule created by the analysis program, designed to promote efficient crop growth.

[0248] "Means of automatic execution" refers to a function that controls the irrigation system based on the generated irrigation schedule and automatically supplies water at the appropriate timing and volume.

[0249] "Generative AI" refers to artificial intelligence technology that analyzes large amounts of data, generates predictive models, and supports crop growth.

[0250] "Communication equipment" refers to facilities or technologies equipped with network functions to notify users' terminals of generated schedules and to update information in real time as needed.

[0251] This invention provides a system for realizing sustainable agriculture by utilizing environmental information. Specifically, a server, terminals, and users work together to perform efficient irrigation management and support crop growth.

[0252] The server acquires environmental information from various sensors installed in farmland, such as temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. This data is stored in a database on the server. Next, the server analyzes the acquired data using a generative AI model. This AI model can process large amounts of data and predict future weather conditions.

[0253] Specifically, the server generates an optimal irrigation schedule based on the analysis results. This schedule includes the specific time periods and water amounts for irrigation. The user's terminal receives the irrigation schedule sent from the server and can view it through the terminal's interface. The user can fine-tune the schedule as needed.

[0254] Furthermore, the terminal is connected to an automated irrigation system that automatically performs irrigation based on a user-approved schedule. This operation promotes the efficient use of water resources by ensuring the appropriate timing and amount of water. The server also analyzes the genetic characteristics of crops to support the development of new varieties. This information is useful for selecting and developing sustainable varieties.

[0255] As a concrete example, for a rice farmer in a certain region, the server analyzes past weather data and current environmental information to predict drought due to high temperatures next week. Based on this, the server generates an optimal irrigation schedule to maintain soil moisture and notifies the user's terminal. This allows the user to automatically irrigate according to the schedule, thereby improving the efficiency of water resources.

[0256] An example of a prompt message is: "Generate an optimal irrigation schedule for rice farmers in Tokyo. Based on the weekly forecast for July, and taking into account weather data from the past five years, propose a new irrigation pattern."

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server acquires environmental information from sensors installed in farmland. Input data includes temperature, humidity, soil moisture, and precipitation sensors. This data is transmitted from the corresponding sensors and stored in the server's database. The stored data forms the basis for analysis, representing the first step towards efficient crop management.

[0260] Step 2:

[0261] The server inputs environmental information stored in the database into the analysis program. This input includes data such as temperature, humidity, soil moisture content, and precipitation. Using a generative AI model, the data is analyzed to predict future weather patterns. This analysis enables output such as irrigation schedules, achieving the goal of data analysis: improving agricultural efficiency.

[0262] Step 3:

[0263] The server generates an optimal irrigation schedule using the analysis results from a generated AI model. Input data includes future weather patterns and soil moisture levels. During this process, the server considers each piece of data to determine the required amount of water and the timing of irrigation. The output is a specific irrigation schedule, which can then be notified to the user.

[0264] Step 4:

[0265] The user's terminal receives the irrigation schedule sent from the server. The input is the irrigation schedule information from the server. The user checks the schedule on the terminal screen and makes adjustments as needed. The output is the finalized adjusted irrigation schedule, and the system is ready for automatic irrigation.

[0266] Step 5:

[0267] The terminal controls the irrigation system based on the irrigation schedule confirmed and adjusted by the user. The input is the confirmed irrigation schedule. Specifically, it activates the irrigation system at the appropriate time and supplies the specified amount of water. The output is the efficient use of water resources, supporting optimal crop growth.

[0268] Step 6:

[0269] The server monitors the results of irrigation and reflects the results in a database. Inputs include changes in soil moisture content and environmental information after irrigation. Re-analyzing this data allows for further optimization of future irrigation schedules. The output provides highly accurate feedback, enabling continuous improvement of agricultural efficiency.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In agricultural work, it is necessary to effectively manage the growth of plants susceptible to the effects of climate change, minimize water waste, and achieve sustainable agriculture. Furthermore, developing new varieties based on collected environmental information and improving agricultural productivity are key challenges.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0274] In this invention, the server includes means for acquiring information from a measuring device for collecting environmental information, means for applying a model for analyzing the acquired information, and means for generating a plan for optimizing plant growth based on the results of the analysis. This makes it possible to achieve efficient water supply and growth optimization, thereby promoting sustainable agriculture.

[0275] "Environmental information" refers to a variety of data used to indicate the conditions of agricultural land, such as temperature, humidity, and soil moisture content.

[0276] "Measuring devices" refer to sensors and devices installed on farmland to continuously collect environmental information.

[0277] "Means of acquisition" refers to the methods and technologies for receiving environmental information transmitted from a measuring device and importing it into a server.

[0278] A "model for analysis" is an algorithm or program that performs current situation analysis and future predictions based on collected environmental information.

[0279] "Generative means" refers to the process of creating work plans and schedules based on the results of the analysis model.

[0280] "Water supply" refers to the actions and systems for systematically providing the water necessary for plant growth.

[0281] "Means of operating the device" refers to a method of controlling automated equipment remotely or by program to perform a desired task.

[0282] "Work equipment" refers to hardware used to support agricultural work, such as irrigation systems and robots, that are placed on farmland.

[0283] The system for realizing this invention acquires environmental information from measuring devices installed in farmland and performs analysis on a server. The server uses hardware such as a Raspberry Pi to collect data such as temperature, humidity, and soil moisture content from sensors in real time. This information is analyzed using a generative AI model. The role of the analysis model is to create an optimal water supply plan, taking into account weather forecasts and soil moisture conditions. This plan is then transmitted to agricultural equipment that supports farm work. This equipment includes automatic irrigation systems and agricultural robots.

[0284] The user can receive information from the server through the terminal and view the generated water supply plan. If necessary, adjustments can be made to this plan. In places where agricultural work is carried out, for example, when there is no rain forecast for a week, the generation AI model adjusts the frequency and amount of water supply to achieve efficient irrigation. By operating such a system, the efficiency and sustainability of agriculture are improved.

[0285] As a specific example, in farmland where paddy rice is planted under a dry climate, the generation AI model analyzes meteorological data and creates an appropriate irrigation schedule. Based on this information, the irrigation robot can automatically perform the irrigation, promoting the growth of crops while effectively utilizing water resources.

[0286] Examples of prompt texts include "Please tell me what irrigation schedule should be set to provide optimal water supply when growing paddy rice in a dry climate where no rain is forecast for a week."

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server acquires environmental information (such as temperature, humidity, soil moisture content, etc.) from the measurement devices installed on the farmland. This input data is received from the sensors in real time and aggregated by the server as initial processing. Basic filtering processing is performed to check for any oversight or outliers in the data. The output is a set of scrutinized environmental information.

[0290] Step 2:

[0291] The server inputs the acquired environmental information into a generating AI model for analysis. This model uses past data patterns and weather forecasting algorithms to predict future weather patterns and soil moisture conditions. Data processing includes shaping time-series data and correcting outliers. The output is future weather forecast information and soil moisture stress predictions.

[0292] Step 3:

[0293] The server generates an optimal water supply plan based on the analysis results. An algorithm is used to determine the timing and amount of irrigation, utilizing the output of the generated AI model. This is a calculation result based on prompt statements and is formalized as a water supply plan. The output is a water supply plan document.

[0294] Step 4:

[0295] The user receives the water supply plan from the server via a terminal and reviews its contents. The terminal allows the user to adjust the plan, modifying it as needed to create a customized plan. At this stage, the input is the initial water supply plan, and the output is the modified final plan.

[0296] Step 5:

[0297] The server transmits the finalized water supply plan to the farm's automated irrigation systems and agricultural robots. A communication protocol is used, and the equipment receives operational instructions based on the plan. The input is the user-adjusted water supply plan, and the output is the activation signal for the irrigation system at the appropriate time.

[0298] Step 6:

[0299] The irrigation system operates according to the server's instructions, supplying water as planned. Specific actions include starting pumps and opening / closing valves, thereby supplying the plants with the necessary water. The input is the activation signal, and the output is the actual irrigation activity.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention provides an advanced system to support the sustainable cultivation of crops. In addition to collecting and analyzing environmental data, generating irrigation schedules, and executing automated irrigation, this system incorporates an emotion engine that recognizes user emotions to improve efficiency.

[0302] The server acquires data from various sensors installed on the farm. This includes environmental data such as temperature, humidity, soil moisture, and precipitation, and this data is stored in a database for analysis. The collected data is input into an analysis model using generative AI to precisely predict future weather patterns and crop growth conditions. Based on this prediction, the server generates an irrigation schedule to optimize crop growth.

[0303] The user's device not only receives irrigation schedules from the server, but also uses an emotion engine to recognize the user's emotional state. This engine analyzes emotions through voice and facial expressions, and can then adjust the system interface and notification content accordingly. For example, if the system analyzes that the user is stressed, it can be configured to reduce the frequency of notifications and provide more concise information.

[0304] The emotion engine, receiving feedback from the device, records long-term emotional data of the user and optimizes the system to suit each individual user based on that data. This is expected to improve the user experience and make the system more user-friendly.

[0305] The terminal automatically controls the irrigation device based on the optimized irrigation schedule. As a result, the crops can efficiently obtain the required moisture, and the use of wasted water resources can be suppressed. As a specific example, in the irrigation of small-scale farms, the optimization of irrigation based on daily weather conditions and feelings supports the healthy growth of crops.

[0306] The system configured in this way is an innovative technology that realizes the efficient use of water resources and the improvement of productivity in agriculture, and reduces the burden on users.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] The server acquires environmental data from sensors arranged on the farm. Specifically, data such as temperature, humidity, soil moisture content, and precipitation are collected at regular intervals, and these data are saved.

[0310] Step 2:

[0311] The server accumulates the collected data in the database and performs analysis using generative AI. In this analysis, past data and current data are compared to predict future weather conditions and soil changes.

[0312] Step 3:

[0313] The server generates an optimal irrigation schedule based on the analysis results. This schedule includes the irrigation timing and amount of water to relieve the predicted water stress.

[0314] Step 4:

[0315] The user's terminal receives the irrigation schedule sent from the server. The received schedule is displayed on the user interface, and the content can be confirmed. Also, the user can adjust the schedule as needed.

[0316] Step 5:

[0317] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this, it adjusts the content and timing of notifications. For example, if it determines that the user is tired, the notification settings are changed to be more concise, conveying only the essential points.

[0318] Step 6:

[0319] The terminal automatically controls the irrigation system based on a confirmed irrigation schedule. Signals from the terminal activate the pumps at the appropriate times, supplying the specified amount of water.

[0320] Step 7:

[0321] The server receives and records long-term user sentiment data from the sentiment engine. This past sentiment data is then analyzed to optimize the interface so that the system better suits the user in the future.

[0322] (Example 2)

[0323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0324] Modern agriculture demands flexible responses to environmental conditions and climate change. Furthermore, efficiently utilizing water resources while providing optimal growing conditions for individual crops is a challenge. Additionally, a lack of information provision and operational support that considers the emotions and stress levels of agricultural workers hinders improvements in work efficiency and user experience. Therefore, a system is needed to address these complex challenges.

[0325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0326] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an analysis algorithm for analyzing the acquired information, means for generating a plan for optimizing plant growth based on the analysis results, means for analyzing a person's emotional state from voice and facial expressions and adjusting the system's operation, and means for automatically supplying water based on the generated plan. This enables adaptation to environmental changes in agriculture, optimization of crop growth, and provision of information and operational support that takes into account the user's emotions.

[0327] "Environmental information" refers to data on external conditions in agriculture, such as temperature, humidity, soil moisture content, and precipitation.

[0328] "Sensing devices" refer to sensors and measuring instruments used to collect environmental information.

[0329] "Means of acquiring information" refers to methods and technologies for receiving data obtained from sensing devices and incorporating it into a system.

[0330] An "analysis algorithm" refers to the computational procedures and models used to derive important insights and predictions based on collected data.

[0331] "Means of generating plans" refers to methods for designing schedules and actions to optimize crop cultivation and resource utilization based on the results of analysis.

[0332] "Means for analyzing emotional states" refers to methods and technologies for evaluating a user's emotions through voice and facial expressions, and for understanding that state within a system.

[0333] "Means of automatically supplying water" refers to the automatic control of equipment and systems that supply the required amount of water to crops based on a planned schedule.

[0334] This invention relates to an agricultural support system based on data collection, analysis, optimization, and emotion recognition. The system is configured as follows:

[0335] The server collects environmental information using various sensors. These sensors include temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. These sensors are installed on the farm and transmit data to the server in real time. This data is stored in a database and processed by the server's analysis algorithms.

[0336] The server analyzes the collected data through a generative AI model. Based on the predictive information obtained from this analysis, it generates a plan to optimize plant growth and formulates an irrigation schedule. This model utilizes machine learning technology, enabling future predictions based on historical data.

[0337] The user's device uses an emotion engine to analyze voice and facial expressions and evaluate the user's emotional state. Based on this evaluation, the device can provide information and operate the system according to the user's state. Furthermore, the device automatically controls the irrigation equipment based on the irrigation schedule sent from the server.

[0338] For example, if the server predicts high temperatures the following day based on data from sensors on a given day, irrigation will be carried out with an increased water volume. On the other hand, if the system determines that the user is experiencing stress, the frequency of notifications will be reduced.

[0339] An example of a prompt message might be: "Analyze the farm's environmental data to generate an optimal irrigation schedule and adjust notifications based on the user's stress level."

[0340] This system will improve the efficiency of agricultural work, the efficient use of water resources, and provide users with a comfortable operating experience.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server acquires environmental information from various sensing devices placed on the farm. This information includes temperature, humidity, soil moisture content, and precipitation. It receives data transmitted in real time from the sensing devices and stores it in a database. In this process, sensor data is the input, and a structured environmental information database is the output.

[0344] Step 2:

[0345] The server inputs environmental information stored in the database into a generating AI model. This model uses machine learning algorithms to predict future weather patterns and crop growth conditions based on past data. In this analysis process, environmental information serves as input data, and future weather and crop growth predictions are output.

[0346] Step 3:

[0347] The server develops an irrigation plan to optimize plant growth based on the output from the generating AI model. In generating the plan, it analyzes predictive data to determine the required water volume and irrigation timing. The input is the prediction result from the generating AI, and the output is a detailed irrigation schedule.

[0348] Step 4:

[0349] The user's device receives the irrigation schedule sent from the server and uses an emotion engine to analyze the user's voice and facial expressions. This analysis evaluates the user's emotional state. The input to the analysis process is voice and facial expression data obtained from the user, and the output is information about the user's emotional state.

[0350] Step 5:

[0351] The device generates information and notifications tailored to the user's state based on the results of emotion analysis. It adjusts the frequency and content of notifications according to the user's stress level, providing a more user-friendly interface. The input is emotional state information, and the output is the adjusted notifications and interface.

[0352] Step 6:

[0353] The terminal automatically controls the irrigation system based on the irrigation schedule from the server. Specifically, it operates valves and pumps at the appropriate times to supply the crops with the necessary water. The irrigation schedule functions as the input, and the actual water supply is the output.

[0354] In this way, servers and terminals work together to achieve automation and efficiency in agriculture.

[0355] (Application Example 2)

[0356] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0357] In modern agriculture and factory automation, there is a need for efficient process management while flexibly responding to environmental changes. Furthermore, reducing user stress and building interactive systems are also crucial challenges. Existing systems lack adaptive adjustment functions that respond to user needs, making it difficult to improve the user experience.

[0358] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0359] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an evaluation method for analyzing the acquired information, and means for using an emotion engine for analyzing the user's emotional state. This enables flexible and efficient environmental control while providing notifications adjusted according to the user's emotional state.

[0360] "Environmental information" refers to data about the conditions of the target environment, such as ambient temperature, humidity, and noise level.

[0361] A "sensing device" is a device that detects and acquires environmental information, and incorporates sensors.

[0362] "Means of acquiring information" refers to the functions and methods for collecting environmental information obtained from sensing devices.

[0363] An "evaluation method" is a process or algorithm used to analyze acquired environmental information and formulate optimal control and planning strategies based on the results.

[0364] "User's emotional state" refers to the user's feelings and psychological state, which are factors that trigger specific behaviors and responses.

[0365] An "emotion engine" is a system that analyzes the user's emotional state and dynamically adjusts the system's response and interface based on the results.

[0366] "Means of providing notifications" refers to devices or functions used to convey information or alerts to users, such as messages or alert sounds.

[0367] The system in this invention is composed of several hardware and software components in order to improve the efficiency of environmental control and process management in factories and agricultural sites.

[0368] The server acquires environmental information from multiple sensing devices and stores it in a database. These sensing devices include various sensors that detect temperature, humidity, and noise levels. This data is stored on a cloud-based platform, such as AWS. The acquired data undergoes preprocessing using Python, after which an evaluation method is applied. A generative AI model is used to analyze the data for evaluation purposes, and based on the obtained information, optimal processes and control plans are generated.

[0369] The device notifies the user of necessary information based on the results of the evaluation method. This includes the end user's smartphone or PC. Notifications are provided flexibly after analyzing the user's emotional state using an emotion engine, thus improving the user experience. This emotion engine performs speech recognition and facial expression analysis, and adjusts the system's feedback function.

[0370] As a concrete example, consider a manager leading a parts assembly process in a small factory. If the emotion engine detects that the manager is under high stress, the notifications the user receives will be limited to important progress information, reducing the user's burden and providing a more efficient work environment.

[0371] As an example of a prompt message, it can be set in text format as, "Generate the optimal process control schedule based on today's factory temperature and humidity data." This enables efficient control, resulting in improved efficiency and productivity.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The server acquires environmental information from various sensing devices. This environmental information includes temperature, humidity, and noise levels. The acquired data is sent to a cloud-based platform, where the environmental information is stored in a database.

[0375] Step 2:

[0376] The server retrieves environmental information stored in the database and preprocesses the data using Python. This preprocessing includes correcting missing values ​​and removing noise. The preprocessed data is then input into an evaluation method and analyzed using a generative AI model. This analysis outputs an optimal process control schedule based on the environmental information.

[0377] Step 3:

[0378] The terminal receives an optimal process control schedule sent from the server. The received schedule is then notified to the user. At this time, the terminal analyzes the user's emotional state using an emotion engine. The emotion engine performs speech recognition and facial expression analysis, and adjusts the notification content based on the results. Specifically, if the user is feeling stressed, the notification content is narrowed down to the essentials and adjusted to prevent information overload.

[0379] Step 4:

[0380] The user controls the process based on notifications from their device. Based on the information received on the device, the user makes decisions and provides feedback to the environment. This feedback information is sent to the server and incorporated into the next cycle of the system. This allows the entire system to dynamically adapt to the user's situation.

[0381] By following these steps, efficient environmental control and process management that takes into account the user's emotional state can be achieved.

[0382] 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.

[0383] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0389] 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.

[0390] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0391] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0392] 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.

[0393] 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.

[0394] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0395] The 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.

[0396] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0397] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0398] This invention provides a system for efficiently managing the cultivation of crops susceptible to the effects of climate change, particularly rice, which requires a large amount of water, and for realizing sustainable agriculture. This system supports crop growth by collecting and analyzing environmental data and generating an optimal growing schedule. Embodiments of the invention are shown below.

[0399] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from various sensors installed in farmland. This makes it possible to understand the current state of the agricultural environment. The collected data is stored in a database on the server and used as input data for analytical models.

[0400] The analysis model utilizes generative AI, and the server uses this model to perform detailed analysis based on the large amount of data collected. Specifically, it predicts future weather patterns based on past data and creates an optimal irrigation schedule to reduce soil moisture stress. Users receive the schedule generated by the server on their devices and further review and adjust it as needed.

[0401] This system features an automated irrigation function to improve the efficiency of irrigation water utilization. The user's terminal receives instructions from the server and activates irrigation equipment at the appropriate time. As a result, it can efficiently provide the water necessary for crops while reducing wasted water consumption.

[0402] Furthermore, the server analyzes information on the genetic characteristics of crops to support the development of new varieties resistant to drought and other environmental stresses. This functionality will facilitate collaboration with research institutions and breeding companies, accelerating the realization of sustainable agriculture.

[0403] By implementing this invention, farmers will be able to respond flexibly to fluctuations in climate conditions and water resources, thereby enhancing the sustainability of agriculture. This will lead to the stabilization of food production and the efficient use of water resources.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from installed sensors. This is done via the network, ensuring data integrity and up-to-dateness.

[0407] Step 2:

[0408] The server stores the acquired environmental data in a database. During this process, a timestamp and geographical location data are assigned to each data point, organizing the data for later analysis.

[0409] Step 3:

[0410] The server uses generative AI to analyze accumulated data and predict weather patterns and soil moisture changes. The AI ​​model compares past and current data to predict future environmental conditions with high accuracy.

[0411] Step 4:

[0412] Based on the analysis results, the server generates an optimal irrigation schedule. This schedule includes specific timings and water volumes to ensure efficient water management, taking into account predicted weather conditions and the current growth stage of the crops.

[0413] Step 5:

[0414] The user's terminal receives the irrigation schedule sent from the server and reviews its contents. The user can then review the schedule and make adjustments as needed to suit local conditions.

[0415] Step 6:

[0416] The terminal controls the irrigation system based on the finalized irrigation schedule. This automates the process of pumps operating at the appropriate times and efficiently supplying the required amount of water.

[0417] Step 7:

[0418] The server analyzes information on the genetic characteristics of crops and generates data on new varieties that are in need of development. This data is provided to research institutions and other organizations to help develop drought-resistant varieties.

[0419] (Example 1)

[0420] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0421] In modern agriculture, sustainable crop production is becoming difficult due to the impacts of climate change and water resource instability. Water resource management, in particular, is a major challenge for environmentally friendly agriculture. Traditional methods often result in inefficient management of irrigation timing and water volume, negatively impacting crop growth and yield. Furthermore, rapid and accurate data analysis is crucial for analyzing the genetic characteristics of crops and developing new varieties.

[0422] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0423] In this invention, the server includes means for acquiring information from a device for collecting environmental information, means for applying an analysis program for analyzing the acquired information, and means for automatically executing irrigation based on the generated plan. This makes it possible to achieve sustainable agriculture while efficiently managing water resources. Furthermore, the analysis program using generated AI can analyze the genetic characteristics of crops in detail, accelerating the development of new, resistant varieties.

[0424] "Environmental information" refers to data that indicates environmental conditions in agricultural land, such as temperature, humidity, soil moisture content, and precipitation.

[0425] "Devices" refer to various sensors installed on farmland, which are equipment used to acquire environmental information in real time.

[0426] "Means of acquisition" refers to a process that has the function of collecting environmental information from a device to a server and storing it in a database.

[0427] An "analysis program" is software that analyzes data based on acquired environmental information to evaluate the optimal irrigation schedule and the genetic characteristics of crops.

[0428] The "generated plan" refers to the optimal irrigation schedule created by the analysis program, designed to promote efficient crop growth.

[0429] "Means of automatic execution" refers to a function that controls the irrigation system based on the generated irrigation schedule and automatically supplies water at the appropriate timing and volume.

[0430] "Generative AI" refers to artificial intelligence technology that analyzes large amounts of data, generates predictive models, and supports crop growth.

[0431] "Communication equipment" refers to facilities or technologies equipped with network functions to notify users' terminals of generated schedules and to update information in real time as needed.

[0432] This invention provides a system for realizing sustainable agriculture by utilizing environmental information. Specifically, a server, terminals, and users work together to perform efficient irrigation management and support crop growth.

[0433] The server acquires environmental information from various sensors installed in farmland, such as temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. This data is stored in a database on the server. Next, the server analyzes the acquired data using a generative AI model. This AI model can process large amounts of data and predict future weather conditions.

[0434] Specifically, the server generates an optimal irrigation schedule based on the analysis results. This schedule includes the specific time periods and water amounts for irrigation. The user's terminal receives the irrigation schedule sent from the server and can view it through the terminal's interface. The user can fine-tune the schedule as needed.

[0435] Furthermore, the terminal is connected to an automated irrigation system that automatically performs irrigation based on a user-approved schedule. This operation promotes the efficient use of water resources by ensuring the appropriate timing and amount of water. The server also analyzes the genetic characteristics of crops to support the development of new varieties. This information is useful for selecting and developing sustainable varieties.

[0436] As a concrete example, for a rice farmer in a certain region, the server analyzes past weather data and current environmental information to predict drought due to high temperatures next week. Based on this, the server generates an optimal irrigation schedule to maintain soil moisture and notifies the user's terminal. This allows the user to automatically irrigate according to the schedule, thereby improving the efficiency of water resources.

[0437] An example of a prompt message is: "Generate an optimal irrigation schedule for rice farmers in Tokyo. Based on the weekly forecast for July, and taking into account weather data from the past five years, propose a new irrigation pattern."

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The server acquires environmental information from sensors installed in farmland. Input data includes temperature, humidity, soil moisture, and precipitation sensors. This data is transmitted from the corresponding sensors and stored in the server's database. The stored data forms the basis for analysis, representing the first step towards efficient crop management.

[0441] Step 2:

[0442] The server inputs environmental information stored in the database into the analysis program. This input includes data such as temperature, humidity, soil moisture content, and precipitation. Using a generative AI model, the data is analyzed to predict future weather patterns. This analysis enables output such as irrigation schedules, achieving the goal of data analysis: improving agricultural efficiency.

[0443] Step 3:

[0444] The server generates an optimal irrigation schedule using the analysis results from a generated AI model. Input data includes future weather patterns and soil moisture levels. During this process, the server considers each piece of data to determine the required amount of water and the timing of irrigation. The output is a specific irrigation schedule, which can then be notified to the user.

[0445] Step 4:

[0446] The user's terminal receives the irrigation schedule sent from the server. The input is the irrigation schedule information from the server. The user checks the schedule on the terminal screen and makes adjustments as needed. The output is the finalized adjusted irrigation schedule, and the system is ready for automatic irrigation.

[0447] Step 5:

[0448] The terminal controls the irrigation system based on the irrigation schedule confirmed and adjusted by the user. The input is the confirmed irrigation schedule. Specifically, it activates the irrigation system at the appropriate time and supplies the specified amount of water. The output is the efficient use of water resources, supporting optimal crop growth.

[0449] Step 6:

[0450] The server monitors the results of irrigation and reflects the results in a database. Inputs include changes in soil moisture content and environmental information after irrigation. Re-analyzing this data allows for further optimization of future irrigation schedules. The output provides highly accurate feedback, enabling continuous improvement of agricultural efficiency.

[0451] (Application Example 1)

[0452] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] In agricultural work, it is necessary to effectively manage the growth of plants susceptible to the effects of climate change, minimize water waste, and achieve sustainable agriculture. Furthermore, developing new varieties based on collected environmental information and improving agricultural productivity are key challenges.

[0454] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0455] In this invention, the server includes means for acquiring information from a measuring device for collecting environmental information, means for applying a model for analyzing the acquired information, and means for generating a plan for optimizing plant growth based on the results of the analysis. This makes it possible to achieve efficient water supply and growth optimization, thereby promoting sustainable agriculture.

[0456] "Environmental information" refers to a variety of data used to indicate the conditions of agricultural land, such as temperature, humidity, and soil moisture content.

[0457] "Measuring devices" refer to sensors and devices installed on farmland to continuously collect environmental information.

[0458] "Means of acquisition" refers to the methods and technologies for receiving environmental information transmitted from a measuring device and importing it into a server.

[0459] A "model for analysis" is an algorithm or program that performs current situation analysis and future predictions based on collected environmental information.

[0460] "Generative means" refers to the process of creating work plans and schedules based on the results of the analysis model.

[0461] "Water supply" refers to the actions and systems for systematically providing the water necessary for plant growth.

[0462] "Means of operating the device" refers to a method of controlling automated equipment remotely or by program to perform a desired task.

[0463] "Work equipment" refers to hardware used to support agricultural work, such as irrigation systems and robots, that are placed on farmland.

[0464] The system for realizing this invention acquires environmental information from measuring devices installed in farmland and performs analysis on a server. The server uses hardware such as a Raspberry Pi to collect data such as temperature, humidity, and soil moisture content from sensors in real time. This information is analyzed using a generative AI model. The role of the analysis model is to create an optimal water supply plan, taking into account weather forecasts and soil moisture conditions. This plan is then transmitted to agricultural equipment that supports farm work. This equipment includes automatic irrigation systems and agricultural robots.

[0465] Users can receive information from the server via their terminals and review the generated water supply plan. Adjustments can be made to this plan as needed. For example, in agricultural areas, if there is no rain forecast for a week, the generating AI model adjusts the frequency and amount of water supply to achieve efficient irrigation. Such a system improves the efficiency and sustainability of agriculture.

[0466] As a concrete example, in farmland where rice is planted under arid climate conditions, a generative AI model analyzes weather data and creates an appropriate irrigation schedule. Based on this information, irrigation robots can automatically carry out the work, promoting crop growth while effectively utilizing water resources.

[0467] An example of a prompt message might be: "In a dry climate with no rain forecast for a week, what irrigation schedule should I set to ensure optimal water supply when growing rice?"

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server acquires environmental information (temperature, humidity, soil moisture content, etc.) from measuring devices installed in farmland. This input data is received from the sensors in real time and aggregated by the server as initial processing. Basic filtering is performed to check for any missed data or outliers. The output is a set of scrutinized environmental information.

[0471] Step 2:

[0472] The server inputs the acquired environmental information into a generating AI model for analysis. This model uses past data patterns and weather forecasting algorithms to predict future weather patterns and soil moisture conditions. Data processing includes shaping time-series data and correcting outliers. The output is future weather forecast information and soil moisture stress predictions.

[0473] Step 3:

[0474] The server generates an optimal water supply plan based on the analysis results. An algorithm is used to determine the timing and amount of irrigation, utilizing the output of the generated AI model. This is a calculation result based on prompt statements and is formalized as a water supply plan. The output is a water supply plan document.

[0475] Step 4:

[0476] The user receives the water supply plan from the server via a terminal and reviews its contents. The terminal allows the user to adjust the plan, modifying it as needed to create a customized plan. At this stage, the input is the initial water supply plan, and the output is the modified final plan.

[0477] Step 5:

[0478] The server transmits the finalized water supply plan to the farm's automated irrigation systems and agricultural robots. A communication protocol is used, and the equipment receives operational instructions based on the plan. The input is the user-adjusted water supply plan, and the output is the activation signal for the irrigation system at the appropriate time.

[0479] Step 6:

[0480] The irrigation system operates according to the server's instructions, supplying water as planned. Specific actions include starting pumps and opening / closing valves, thereby supplying the plants with the necessary water. The input is the activation signal, and the output is the actual irrigation activity.

[0481] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0482] This invention provides an advanced system to support the sustainable cultivation of crops. In addition to collecting and analyzing environmental data, generating irrigation schedules, and executing automated irrigation, this system incorporates an emotion engine that recognizes user emotions to improve efficiency.

[0483] The server acquires data from various sensors installed on the farm. This includes environmental data such as temperature, humidity, soil moisture, and precipitation, and this data is stored in a database for analysis. The collected data is input into an analysis model using generative AI to precisely predict future weather patterns and crop growth conditions. Based on this prediction, the server generates an irrigation schedule to optimize crop growth.

[0484] The user's device not only receives irrigation schedules from the server, but also uses an emotion engine to recognize the user's emotional state. This engine analyzes emotions through voice and facial expressions, and can then adjust the system interface and notification content accordingly. For example, if the system analyzes that the user is stressed, it can be configured to reduce the frequency of notifications and provide more concise information.

[0485] The emotion engine, receiving feedback from the device, records long-term emotional data of the user and optimizes the system to suit each individual user based on that data. This is expected to improve the user experience and make the system more user-friendly.

[0486] The terminal automatically controls the irrigation system based on an optimized irrigation schedule. This allows crops to efficiently receive the water they need, reducing the wasteful use of water resources. Specifically, in small-scale farm irrigation, optimizing irrigation based on daily weather conditions and emotions supports healthy crop growth.

[0487] This system, configured in this way, is an innovative technology that enables efficient use of water resources and improved productivity in agriculture, while reducing the burden on users.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The server acquires environmental data from sensors placed on the farm. Specifically, it collects data such as temperature, humidity, soil moisture content, and precipitation at regular intervals and stores this data.

[0491] Step 2:

[0492] The server stores the collected data in a database and performs analysis using generating AI. This analysis compares past and present data to predict future weather conditions and soil changes.

[0493] Step 3:

[0494] The server generates an optimal irrigation schedule based on the analysis results. This schedule includes irrigation timing and water volume to mitigate predicted water stress.

[0495] Step 4:

[0496] The user's device receives the irrigation schedule sent from the server. The received schedule is displayed on the user interface, allowing the user to review its contents. The user can also adjust the schedule as needed.

[0497] Step 5:

[0498] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this, it adjusts the content and timing of notifications. For example, if it determines that the user is tired, the notification settings are changed to be more concise, conveying only the essential points.

[0499] Step 6:

[0500] The terminal automatically controls the irrigation system based on a confirmed irrigation schedule. Signals from the terminal activate the pumps at the appropriate times, supplying the specified amount of water.

[0501] Step 7:

[0502] The server receives and records long-term user sentiment data from the sentiment engine. This past sentiment data is then analyzed to optimize the interface so that the system better suits the user in the future.

[0503] (Example 2)

[0504] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0505] Modern agriculture demands flexible responses to environmental conditions and climate change. Furthermore, efficiently utilizing water resources while providing optimal growing conditions for individual crops is a challenge. Additionally, a lack of information provision and operational support that considers the emotions and stress levels of agricultural workers hinders improvements in work efficiency and user experience. Therefore, a system is needed to address these complex challenges.

[0506] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0507] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an analysis algorithm for analyzing the acquired information, means for generating a plan for optimizing plant growth based on the analysis results, means for analyzing a person's emotional state from voice and facial expressions and adjusting the system's operation, and means for automatically supplying water based on the generated plan. This enables adaptation to environmental changes in agriculture, optimization of crop growth, and provision of information and operational support that takes into account the user's emotions.

[0508] "Environmental information" refers to data on external conditions in agriculture, such as temperature, humidity, soil moisture content, and precipitation.

[0509] "Sensing devices" refer to sensors and measuring instruments used to collect environmental information.

[0510] "Means of acquiring information" refers to methods and technologies for receiving data obtained from sensing devices and incorporating it into a system.

[0511] An "analysis algorithm" refers to the computational procedures and models used to derive important insights and predictions based on collected data.

[0512] "Means of generating plans" refers to methods for designing schedules and actions to optimize crop cultivation and resource utilization based on the results of analysis.

[0513] "Means for analyzing emotional states" refers to methods and technologies for evaluating a user's emotions through voice and facial expressions, and for understanding that state within a system.

[0514] "Means of automatically supplying water" refers to the automatic control of equipment and systems that supply the required amount of water to crops based on a planned schedule.

[0515] This invention relates to an agricultural support system based on data collection, analysis, optimization, and emotion recognition. The system is configured as follows:

[0516] The server collects environmental information using various sensors. These sensors include temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. These sensors are installed on the farm and transmit data to the server in real time. This data is stored in a database and processed by the server's analysis algorithms.

[0517] The server analyzes the collected data through a generative AI model. Based on the predictive information obtained from this analysis, it generates a plan to optimize plant growth and formulates an irrigation schedule. This model utilizes machine learning technology, enabling future predictions based on historical data.

[0518] The user's device uses an emotion engine to analyze voice and facial expressions and evaluate the user's emotional state. Based on this evaluation, the device can provide information and operate the system according to the user's state. Furthermore, the device automatically controls the irrigation equipment based on the irrigation schedule sent from the server.

[0519] For example, if the server predicts high temperatures the following day based on data from sensors on a given day, irrigation will be carried out with an increased water volume. On the other hand, if the system determines that the user is experiencing stress, the frequency of notifications will be reduced.

[0520] An example of a prompt message might be: "Analyze the farm's environmental data to generate an optimal irrigation schedule and adjust notifications based on the user's stress level."

[0521] This system will improve the efficiency of agricultural work, the efficient use of water resources, and provide users with a comfortable operating experience.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] The server acquires environmental information from various sensing devices placed on the farm. This information includes temperature, humidity, soil moisture content, and precipitation. It receives data transmitted in real time from the sensing devices and stores it in a database. In this process, sensor data is the input, and a structured environmental information database is the output.

[0525] Step 2:

[0526] The server inputs environmental information stored in the database into a generating AI model. This model uses machine learning algorithms to predict future weather patterns and crop growth conditions based on past data. In this analysis process, environmental information serves as input data, and future weather and crop growth predictions are output.

[0527] Step 3:

[0528] The server develops an irrigation plan to optimize plant growth based on the output from the generating AI model. In generating the plan, it analyzes predictive data to determine the required water volume and irrigation timing. The input is the prediction result from the generating AI, and the output is a detailed irrigation schedule.

[0529] Step 4:

[0530] The user's device receives the irrigation schedule sent from the server and uses an emotion engine to analyze the user's voice and facial expressions. This analysis evaluates the user's emotional state. The input to the analysis process is voice and facial expression data obtained from the user, and the output is information about the user's emotional state.

[0531] Step 5:

[0532] The device generates information and notifications tailored to the user's state based on the results of emotion analysis. It adjusts the frequency and content of notifications according to the user's stress level, providing a more user-friendly interface. The input is emotional state information, and the output is the adjusted notifications and interface.

[0533] Step 6:

[0534] The terminal automatically controls the irrigation system based on the irrigation schedule from the server. Specifically, it operates valves and pumps at the appropriate times to supply the crops with the necessary water. The irrigation schedule functions as the input, and the actual water supply is the output.

[0535] In this way, servers and terminals work together to achieve automation and efficiency in agriculture.

[0536] (Application Example 2)

[0537] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0538] In modern agriculture and factory automation, there is a need for efficient process management while flexibly responding to environmental changes. Furthermore, reducing user stress and building interactive systems are also crucial challenges. Existing systems lack adaptive adjustment functions that respond to user needs, making it difficult to improve the user experience.

[0539] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0540] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an evaluation method for analyzing the acquired information, and means for using an emotion engine for analyzing the user's emotional state. This enables flexible and efficient environmental control while providing notifications adjusted according to the user's emotional state.

[0541] "Environmental information" refers to data about the conditions of the target environment, such as ambient temperature, humidity, and noise level.

[0542] A "sensing device" is a device that detects and acquires environmental information, and incorporates sensors.

[0543] "Means of acquiring information" refers to the functions and methods for collecting environmental information obtained from sensing devices.

[0544] An "evaluation method" is a process or algorithm used to analyze acquired environmental information and formulate optimal control and planning strategies based on the results.

[0545] "User's emotional state" refers to the user's feelings and psychological state, which are factors that trigger specific behaviors and responses.

[0546] An "emotion engine" is a system that analyzes the user's emotional state and dynamically adjusts the system's response and interface based on the results.

[0547] "Means of providing notifications" refers to devices or functions used to convey information or alerts to users, such as messages or alert sounds.

[0548] The system in this invention is composed of several hardware and software components in order to improve the efficiency of environmental control and process management in factories and agricultural sites.

[0549] The server acquires environmental information from multiple sensing devices and stores it in a database. These sensing devices include various sensors that detect temperature, humidity, and noise levels. This data is stored on a cloud-based platform, such as AWS. The acquired data undergoes preprocessing using Python, after which an evaluation method is applied. A generative AI model is used to analyze the data for evaluation purposes, and based on the obtained information, optimal processes and control plans are generated.

[0550] The device notifies the user of necessary information based on the results of the evaluation method. This includes the end user's smartphone or PC. Notifications are provided flexibly after analyzing the user's emotional state using an emotion engine, thus improving the user experience. This emotion engine performs speech recognition and facial expression analysis, and adjusts the system's feedback function.

[0551] As a concrete example, consider a manager leading a parts assembly process in a small factory. If the emotion engine detects that the manager is under high stress, the notifications the user receives will be limited to important progress information, reducing the user's burden and providing a more efficient work environment.

[0552] As an example of a prompt message, it can be set in text format as, "Generate the optimal process control schedule based on today's factory temperature and humidity data." This enables efficient control, resulting in improved efficiency and productivity.

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The server acquires environmental information from various sensing devices. This environmental information includes temperature, humidity, and noise levels. The acquired data is sent to a cloud-based platform, where the environmental information is stored in a database.

[0556] Step 2:

[0557] The server retrieves environmental information stored in the database and preprocesses the data using Python. This preprocessing includes correcting missing values ​​and removing noise. The preprocessed data is then input into an evaluation method and analyzed using a generative AI model. This analysis outputs an optimal process control schedule based on the environmental information.

[0558] Step 3:

[0559] The terminal receives an optimal process control schedule sent from the server. The received schedule is then notified to the user. At this time, the terminal analyzes the user's emotional state using an emotion engine. The emotion engine performs speech recognition and facial expression analysis, and adjusts the notification content based on the results. Specifically, if the user is feeling stressed, the notification content is narrowed down to the essentials and adjusted to prevent information overload.

[0560] Step 4:

[0561] The user controls the process based on notifications from their device. Based on the information received on the device, the user makes decisions and provides feedback to the environment. This feedback information is sent to the server and incorporated into the next cycle of the system. This allows the entire system to dynamically adapt to the user's situation.

[0562] By following these steps, efficient environmental control and process management that takes into account the user's emotional state can be achieved.

[0563] 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.

[0564] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0565] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0566] [Fourth Embodiment]

[0567] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0568] 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.

[0569] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0570] 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.

[0571] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0572] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0573] 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.

[0574] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0575] 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.

[0576] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0577] The 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.

[0578] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0579] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0580] This invention provides a system for efficiently managing the cultivation of crops susceptible to the effects of climate change, particularly rice, which requires a large amount of water, and for realizing sustainable agriculture. This system supports crop growth by collecting and analyzing environmental data and generating an optimal growing schedule. Embodiments of the invention are shown below.

[0581] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from various sensors installed in farmland. This makes it possible to understand the current state of the agricultural environment. The collected data is stored in a database on the server and used as input data for analytical models.

[0582] The analysis model utilizes generative AI, and the server uses this model to perform detailed analysis based on the large amount of data collected. Specifically, it predicts future weather patterns based on past data and creates an optimal irrigation schedule to reduce soil moisture stress. Users receive the schedule generated by the server on their devices and further review and adjust it as needed.

[0583] This system features an automated irrigation function to improve the efficiency of irrigation water utilization. The user's terminal receives instructions from the server and activates irrigation equipment at the appropriate time. As a result, it can efficiently provide the water necessary for crops while reducing wasted water consumption.

[0584] Furthermore, the server analyzes information on the genetic characteristics of crops to support the development of new varieties resistant to drought and other environmental stresses. This functionality will facilitate collaboration with research institutions and breeding companies, accelerating the realization of sustainable agriculture.

[0585] By implementing this invention, farmers will be able to respond flexibly to fluctuations in climate conditions and water resources, thereby enhancing the sustainability of agriculture. This will lead to the stabilization of food production and the efficient use of water resources.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] The server acquires environmental data such as temperature, humidity, soil moisture content, and precipitation in real time from installed sensors. This is done via the network, ensuring data integrity and up-to-dateness.

[0589] Step 2:

[0590] The server stores the acquired environmental data in a database. During this process, a timestamp and geographical location data are assigned to each data point, organizing the data for later analysis.

[0591] Step 3:

[0592] The server uses generative AI to analyze accumulated data and predict weather patterns and soil moisture changes. The AI ​​model compares past and current data to predict future environmental conditions with high accuracy.

[0593] Step 4:

[0594] Based on the analysis results, the server generates an optimal irrigation schedule. This schedule includes specific timings and water volumes to ensure efficient water management, taking into account predicted weather conditions and the current growth stage of the crops.

[0595] Step 5:

[0596] The user's terminal receives the irrigation schedule sent from the server and reviews its contents. The user can then review the schedule and make adjustments as needed to suit local conditions.

[0597] Step 6:

[0598] The terminal controls the irrigation system based on the finalized irrigation schedule. This automates the process of pumps operating at the appropriate times and efficiently supplying the required amount of water.

[0599] Step 7:

[0600] The server analyzes information on the genetic characteristics of crops and generates data on new varieties that are in need of development. This data is provided to research institutions and other organizations to help develop drought-resistant varieties.

[0601] (Example 1)

[0602] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] In modern agriculture, sustainable crop production is becoming difficult due to the impacts of climate change and water resource instability. Water resource management, in particular, is a major challenge for environmentally friendly agriculture. Traditional methods often result in inefficient management of irrigation timing and water volume, negatively impacting crop growth and yield. Furthermore, rapid and accurate data analysis is crucial for analyzing the genetic characteristics of crops and developing new varieties.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0605] In this invention, the server includes means for acquiring information from a device for collecting environmental information, means for applying an analysis program for analyzing the acquired information, and means for automatically executing irrigation based on the generated plan. This makes it possible to achieve sustainable agriculture while efficiently managing water resources. Furthermore, the analysis program using generated AI can analyze the genetic characteristics of crops in detail, accelerating the development of new, resistant varieties.

[0606] "Environmental information" refers to data that indicates environmental conditions in agricultural land, such as temperature, humidity, soil moisture content, and precipitation.

[0607] "Devices" refer to various sensors installed on farmland, which are equipment used to acquire environmental information in real time.

[0608] "Means of acquisition" refers to a process that has the function of collecting environmental information from a device to a server and storing it in a database.

[0609] An "analysis program" is software that analyzes data based on acquired environmental information to evaluate the optimal irrigation schedule and the genetic characteristics of crops.

[0610] The "generated plan" refers to the optimal irrigation schedule created by the analysis program, designed to promote efficient crop growth.

[0611] "Means of automatic execution" refers to a function that controls the irrigation system based on the generated irrigation schedule and automatically supplies water at the appropriate timing and volume.

[0612] "Generative AI" refers to artificial intelligence technology that analyzes large amounts of data, generates predictive models, and supports crop growth.

[0613] "Communication equipment" refers to facilities or technologies equipped with network functions to notify users' terminals of generated schedules and to update information in real time as needed.

[0614] This invention provides a system for realizing sustainable agriculture by utilizing environmental information. Specifically, a server, terminals, and users work together to perform efficient irrigation management and support crop growth.

[0615] The server acquires environmental information from various sensors installed in farmland, such as temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. This data is stored in a database on the server. Next, the server analyzes the acquired data using a generative AI model. This AI model can process large amounts of data and predict future weather conditions.

[0616] Specifically, the server generates an optimal irrigation schedule based on the analysis results. This schedule includes the specific time periods and water amounts for irrigation. The user's terminal receives the irrigation schedule sent from the server and can view it through the terminal's interface. The user can fine-tune the schedule as needed.

[0617] Furthermore, the terminal is connected to an automated irrigation system that automatically performs irrigation based on a user-approved schedule. This operation promotes the efficient use of water resources by ensuring the appropriate timing and amount of water. The server also analyzes the genetic characteristics of crops to support the development of new varieties. This information is useful for selecting and developing sustainable varieties.

[0618] As a concrete example, for a rice farmer in a certain region, the server analyzes past weather data and current environmental information to predict drought due to high temperatures next week. Based on this, the server generates an optimal irrigation schedule to maintain soil moisture and notifies the user's terminal. This allows the user to automatically irrigate according to the schedule, thereby improving the efficiency of water resources.

[0619] An example of a prompt message is: "Generate an optimal irrigation schedule for rice farmers in Tokyo. Based on the weekly forecast for July, and taking into account weather data from the past five years, propose a new irrigation pattern."

[0620] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0621] Step 1:

[0622] The server acquires environmental information from sensors installed in farmland. Input data includes temperature, humidity, soil moisture, and precipitation sensors. This data is transmitted from the corresponding sensors and stored in the server's database. The stored data forms the basis for analysis, representing the first step towards efficient crop management.

[0623] Step 2:

[0624] The server inputs environmental information stored in the database into the analysis program. This input includes data such as temperature, humidity, soil moisture content, and precipitation. Using a generative AI model, the data is analyzed to predict future weather patterns. This analysis enables output such as irrigation schedules, achieving the goal of data analysis: improving agricultural efficiency.

[0625] Step 3:

[0626] The server generates an optimal irrigation schedule using the analysis results from a generated AI model. Input data includes future weather patterns and soil moisture levels. During this process, the server considers each piece of data to determine the required amount of water and the timing of irrigation. The output is a specific irrigation schedule, which can then be notified to the user.

[0627] Step 4:

[0628] The user's terminal receives the irrigation schedule sent from the server. The input is the irrigation schedule information from the server. The user checks the schedule on the terminal screen and makes adjustments as needed. The output is the finalized adjusted irrigation schedule, and the system is ready for automatic irrigation.

[0629] Step 5:

[0630] The terminal controls the irrigation system based on the irrigation schedule confirmed and adjusted by the user. The input is the confirmed irrigation schedule. Specifically, it activates the irrigation system at the appropriate time and supplies the specified amount of water. The output is the efficient use of water resources, supporting optimal crop growth.

[0631] Step 6:

[0632] The server monitors the results of irrigation and reflects the results in a database. Inputs include changes in soil moisture content and environmental information after irrigation. Re-analyzing this data allows for further optimization of future irrigation schedules. The output provides highly accurate feedback, enabling continuous improvement of agricultural efficiency.

[0633] (Application Example 1)

[0634] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In agricultural work, it is necessary to effectively manage the growth of plants susceptible to the effects of climate change, minimize water waste, and achieve sustainable agriculture. Furthermore, developing new varieties based on collected environmental information and improving agricultural productivity are key challenges.

[0636] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0637] In this invention, the server includes means for acquiring information from a measuring device for collecting environmental information, means for applying a model for analyzing the acquired information, and means for generating a plan for optimizing plant growth based on the results of the analysis. This makes it possible to achieve efficient water supply and growth optimization, thereby promoting sustainable agriculture.

[0638] "Environmental information" refers to a variety of data used to indicate the conditions of agricultural land, such as temperature, humidity, and soil moisture content.

[0639] "Measuring devices" refer to sensors and devices installed on farmland to continuously collect environmental information.

[0640] "Means of acquisition" refers to the methods and technologies for receiving environmental information transmitted from a measuring device and importing it into a server.

[0641] A "model for analysis" is an algorithm or program that performs current situation analysis and future predictions based on collected environmental information.

[0642] "Generative means" refers to the process of creating work plans and schedules based on the results of the analysis model.

[0643] "Water supply" refers to the actions and systems for systematically providing the water necessary for plant growth.

[0644] "Means of operating the device" refers to a method of controlling automated equipment remotely or by program to perform a desired task.

[0645] "Work equipment" refers to hardware used to support agricultural work, such as irrigation systems and robots, that are placed on farmland.

[0646] The system for realizing this invention acquires environmental information from measuring devices installed in farmland and performs analysis on a server. The server uses hardware such as a Raspberry Pi to collect data such as temperature, humidity, and soil moisture content from sensors in real time. This information is analyzed using a generative AI model. The role of the analysis model is to create an optimal water supply plan, taking into account weather forecasts and soil moisture conditions. This plan is then transmitted to agricultural equipment that supports farm work. This equipment includes automatic irrigation systems and agricultural robots.

[0647] Users can receive information from the server via their terminals and review the generated water supply plan. Adjustments can be made to this plan as needed. For example, in agricultural areas, if there is no rain forecast for a week, the generating AI model adjusts the frequency and amount of water supply to achieve efficient irrigation. Such a system improves the efficiency and sustainability of agriculture.

[0648] As a concrete example, in farmland where rice is planted under arid climate conditions, a generative AI model analyzes weather data and creates an appropriate irrigation schedule. Based on this information, irrigation robots can automatically carry out the work, promoting crop growth while effectively utilizing water resources.

[0649] An example of a prompt message might be: "In a dry climate with no rain forecast for a week, what irrigation schedule should I set to ensure optimal water supply when growing rice?"

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The server acquires environmental information (temperature, humidity, soil moisture content, etc.) from measuring devices installed in farmland. This input data is received from the sensors in real time and aggregated by the server as initial processing. Basic filtering is performed to check for any missed data or outliers. The output is a set of scrutinized environmental information.

[0653] Step 2:

[0654] The server inputs the acquired environmental information into a generating AI model for analysis. This model uses past data patterns and weather forecasting algorithms to predict future weather patterns and soil moisture conditions. Data processing includes shaping time-series data and correcting outliers. The output is future weather forecast information and soil moisture stress predictions.

[0655] Step 3:

[0656] The server generates an optimal water supply plan based on the analysis results. An algorithm is used to determine the timing and amount of irrigation, utilizing the output of the generated AI model. This is a calculation result based on prompt statements and is formalized as a water supply plan. The output is a water supply plan document.

[0657] Step 4:

[0658] The user receives the water supply plan from the server via a terminal and reviews its contents. The terminal allows the user to adjust the plan, modifying it as needed to create a customized plan. At this stage, the input is the initial water supply plan, and the output is the modified final plan.

[0659] Step 5:

[0660] The server transmits the finalized water supply plan to the farm's automated irrigation systems and agricultural robots. A communication protocol is used, and the equipment receives operational instructions based on the plan. The input is the user-adjusted water supply plan, and the output is the activation signal for the irrigation system at the appropriate time.

[0661] Step 6:

[0662] The irrigation system operates according to the server's instructions, supplying water as planned. Specific actions include starting pumps and opening / closing valves, thereby supplying the plants with the necessary water. The input is the activation signal, and the output is the actual irrigation activity.

[0663] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0664] This invention provides an advanced system to support the sustainable cultivation of crops. In addition to collecting and analyzing environmental data, generating irrigation schedules, and executing automated irrigation, this system incorporates an emotion engine that recognizes user emotions to improve efficiency.

[0665] The server acquires data from various sensors installed on the farm. This includes environmental data such as temperature, humidity, soil moisture, and precipitation, and this data is stored in a database for analysis. The collected data is input into an analysis model using generative AI to precisely predict future weather patterns and crop growth conditions. Based on this prediction, the server generates an irrigation schedule to optimize crop growth.

[0666] The user's device not only receives irrigation schedules from the server, but also uses an emotion engine to recognize the user's emotional state. This engine analyzes emotions through voice and facial expressions, and can then adjust the system interface and notification content accordingly. For example, if the system analyzes that the user is stressed, it can be configured to reduce the frequency of notifications and provide more concise information.

[0667] The emotion engine, receiving feedback from the device, records long-term emotional data of the user and optimizes the system to suit each individual user based on that data. This is expected to improve the user experience and make the system more user-friendly.

[0668] The terminal automatically controls the irrigation system based on an optimized irrigation schedule. This allows crops to efficiently receive the water they need, reducing the wasteful use of water resources. Specifically, in small-scale farm irrigation, optimizing irrigation based on daily weather conditions and emotions supports healthy crop growth.

[0669] This system, configured in this way, is an innovative technology that enables efficient use of water resources and improved productivity in agriculture, while reducing the burden on users.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The server acquires environmental data from sensors placed on the farm. Specifically, it collects data such as temperature, humidity, soil moisture content, and precipitation at regular intervals and stores this data.

[0673] Step 2:

[0674] The server stores the collected data in a database and performs analysis using generating AI. This analysis compares past and present data to predict future weather conditions and soil changes.

[0675] Step 3:

[0676] The server generates an optimal irrigation schedule based on the analysis results. This schedule includes irrigation timing and water volume to mitigate predicted water stress.

[0677] Step 4:

[0678] The user's device receives the irrigation schedule sent from the server. The received schedule is displayed on the user interface, allowing the user to review its contents. The user can also adjust the schedule as needed.

[0679] Step 5:

[0680] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. Based on this, it adjusts the content and timing of notifications. For example, if it determines that the user is tired, the notification settings are changed to be more concise, conveying only the essential points.

[0681] Step 6:

[0682] The terminal automatically controls the irrigation system based on a confirmed irrigation schedule. Signals from the terminal activate the pumps at the appropriate times, supplying the specified amount of water.

[0683] Step 7:

[0684] The server receives and records long-term user sentiment data from the sentiment engine. This past sentiment data is then analyzed to optimize the interface so that the system better suits the user in the future.

[0685] (Example 2)

[0686] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] Modern agriculture demands flexible responses to environmental conditions and climate change. Furthermore, efficiently utilizing water resources while providing optimal growing conditions for individual crops is a challenge. Additionally, a lack of information provision and operational support that considers the emotions and stress levels of agricultural workers hinders improvements in work efficiency and user experience. Therefore, a system is needed to address these complex challenges.

[0688] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0689] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an analysis algorithm for analyzing the acquired information, means for generating a plan for optimizing plant growth based on the analysis results, means for analyzing a person's emotional state from voice and facial expressions and adjusting the system's operation, and means for automatically supplying water based on the generated plan. This enables adaptation to environmental changes in agriculture, optimization of crop growth, and provision of information and operational support that takes into account the user's emotions.

[0690] "Environmental information" refers to data on external conditions in agriculture, such as temperature, humidity, soil moisture content, and precipitation.

[0691] "Sensing devices" refer to sensors and measuring instruments used to collect environmental information.

[0692] "Means of acquiring information" refers to methods and technologies for receiving data obtained from sensing devices and incorporating it into a system.

[0693] An "analysis algorithm" refers to the computational procedures and models used to derive important insights and predictions based on collected data.

[0694] "Means of generating plans" refers to methods for designing schedules and actions to optimize crop cultivation and resource utilization based on the results of analysis.

[0695] "Means for analyzing emotional states" refers to methods and technologies for evaluating a user's emotions through voice and facial expressions, and for understanding that state within a system.

[0696] "Means of automatically supplying water" refers to the automatic control of equipment and systems that supply the required amount of water to crops based on a planned schedule.

[0697] This invention relates to an agricultural support system based on data collection, analysis, optimization, and emotion recognition. The system is configured as follows:

[0698] The server collects environmental information using various sensors. These sensors include temperature sensors, humidity sensors, soil moisture sensors, and precipitation sensors. These sensors are installed on the farm and transmit data to the server in real time. This data is stored in a database and processed by the server's analysis algorithms.

[0699] The server analyzes the collected data through a generative AI model. Based on the predictive information obtained from this analysis, it generates a plan to optimize plant growth and formulates an irrigation schedule. This model utilizes machine learning technology, enabling future predictions based on historical data.

[0700] The user's device uses an emotion engine to analyze voice and facial expressions and evaluate the user's emotional state. Based on this evaluation, the device can provide information and operate the system according to the user's state. Furthermore, the device automatically controls the irrigation equipment based on the irrigation schedule sent from the server.

[0701] For example, if the server predicts high temperatures the following day based on data from sensors on a given day, irrigation will be carried out with an increased water volume. On the other hand, if the system determines that the user is experiencing stress, the frequency of notifications will be reduced.

[0702] An example of a prompt message might be: "Analyze the farm's environmental data to generate an optimal irrigation schedule and adjust notifications based on the user's stress level."

[0703] This system will improve the efficiency of agricultural work, the efficient use of water resources, and provide users with a comfortable operating experience.

[0704] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0705] Step 1:

[0706] The server acquires environmental information from various sensing devices placed on the farm. This information includes temperature, humidity, soil moisture content, and precipitation. It receives data transmitted in real time from the sensing devices and stores it in a database. In this process, sensor data is the input, and a structured environmental information database is the output.

[0707] Step 2:

[0708] The server inputs environmental information stored in the database into a generating AI model. This model uses machine learning algorithms to predict future weather patterns and crop growth conditions based on past data. In this analysis process, environmental information serves as input data, and future weather and crop growth predictions are output.

[0709] Step 3:

[0710] The server develops an irrigation plan to optimize plant growth based on the output from the generating AI model. In generating the plan, it analyzes predictive data to determine the required water volume and irrigation timing. The input is the prediction result from the generating AI, and the output is a detailed irrigation schedule.

[0711] Step 4:

[0712] The user's device receives the irrigation schedule sent from the server and uses an emotion engine to analyze the user's voice and facial expressions. This analysis evaluates the user's emotional state. The input to the analysis process is voice and facial expression data obtained from the user, and the output is information about the user's emotional state.

[0713] Step 5:

[0714] The device generates information and notifications tailored to the user's state based on the results of emotion analysis. It adjusts the frequency and content of notifications according to the user's stress level, providing a more user-friendly interface. The input is emotional state information, and the output is the adjusted notifications and interface.

[0715] Step 6:

[0716] The terminal automatically controls the irrigation system based on the irrigation schedule from the server. Specifically, it operates valves and pumps at the appropriate times to supply the crops with the necessary water. The irrigation schedule functions as the input, and the actual water supply is the output.

[0717] In this way, servers and terminals work together to achieve automation and efficiency in agriculture.

[0718] (Application Example 2)

[0719] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] In modern agriculture and factory automation, there is a need for efficient process management while flexibly responding to environmental changes. Furthermore, reducing user stress and building interactive systems are also crucial challenges. Existing systems lack adaptive adjustment functions that respond to user needs, making it difficult to improve the user experience.

[0721] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0722] In this invention, the server includes means for acquiring information from a sensing device for collecting environmental information, means for applying an evaluation method for analyzing the acquired information, and means for using an emotion engine for analyzing the user's emotional state. This enables flexible and efficient environmental control while providing notifications adjusted according to the user's emotional state.

[0723] "Environmental information" refers to data about the conditions of the target environment, such as ambient temperature, humidity, and noise level.

[0724] A "sensing device" is a device that detects and acquires environmental information, and incorporates sensors.

[0725] "Means of acquiring information" refers to the functions and methods for collecting environmental information obtained from sensing devices.

[0726] An "evaluation method" is a process or algorithm used to analyze acquired environmental information and formulate optimal control and planning strategies based on the results.

[0727] "User's emotional state" refers to the user's feelings and psychological state, which are factors that trigger specific behaviors and responses.

[0728] An "emotion engine" is a system that analyzes the user's emotional state and dynamically adjusts the system's response and interface based on the results.

[0729] "Means of providing notifications" refers to devices or functions used to convey information or alerts to users, such as messages or alert sounds.

[0730] The system in this invention is composed of several hardware and software components in order to improve the efficiency of environmental control and process management in factories and agricultural sites.

[0731] The server acquires environmental information from multiple sensing devices and stores it in a database. These sensing devices include various sensors that detect temperature, humidity, and noise levels. This data is stored on a cloud-based platform, such as AWS. The acquired data undergoes preprocessing using Python, after which an evaluation method is applied. A generative AI model is used to analyze the data for evaluation purposes, and based on the obtained information, optimal processes and control plans are generated.

[0732] The device notifies the user of necessary information based on the results of the evaluation method. This includes the end user's smartphone or PC. Notifications are provided flexibly after analyzing the user's emotional state using an emotion engine, thus improving the user experience. This emotion engine performs speech recognition and facial expression analysis, and adjusts the system's feedback function.

[0733] As a concrete example, consider a manager leading a parts assembly process in a small factory. If the emotion engine detects that the manager is under high stress, the notifications the user receives will be limited to important progress information, reducing the user's burden and providing a more efficient work environment.

[0734] As an example of a prompt message, it can be set in text format as, "Generate the optimal process control schedule based on today's factory temperature and humidity data." This enables efficient control, resulting in improved efficiency and productivity.

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] The server acquires environmental information from various sensing devices. This environmental information includes temperature, humidity, and noise levels. The acquired data is sent to a cloud-based platform, where the environmental information is stored in a database.

[0738] Step 2:

[0739] The server retrieves environmental information stored in the database and preprocesses the data using Python. This preprocessing includes correcting missing values ​​and removing noise. The preprocessed data is then input into an evaluation method and analyzed using a generative AI model. This analysis outputs an optimal process control schedule based on the environmental information.

[0740] Step 3:

[0741] The terminal receives an optimal process control schedule sent from the server. The received schedule is then notified to the user. At this time, the terminal analyzes the user's emotional state using an emotion engine. The emotion engine performs speech recognition and facial expression analysis, and adjusts the notification content based on the results. Specifically, if the user is feeling stressed, the notification content is narrowed down to the essentials and adjusted to prevent information overload.

[0742] Step 4:

[0743] The user controls the process based on notifications from their device. Based on the information received on the device, the user makes decisions and provides feedback to the environment. This feedback information is sent to the server and incorporated into the next cycle of the system. This allows the entire system to dynamically adapt to the user's situation.

[0744] By following these steps, efficient environmental control and process management that takes into account the user's emotional state can be achieved.

[0745] 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.

[0746] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0747] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0748] 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.

[0749] Figure 9 shows an 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.

[0750] 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.

[0751] 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.

[0752] 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, motorcycles, etc., 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, for example, based 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.

[0753] 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."

[0754] 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.

[0755] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0756] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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 the like 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.

[0765] 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.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] Means for acquiring data from sensors to collect environmental data,

[0769] A means of applying an analytical model to analyze the acquired data,

[0770] A means for generating a schedule to optimize crop cultivation based on the results of the analysis,

[0771] A means for analyzing the genetic characteristics of crops and developing varieties with specific traits,

[0772] A system including means for automatically performing irrigation based on a generated schedule.

[0773] (Claim 2)

[0774] The system according to claim 1, wherein the analysis model uses generative AI.

[0775] (Claim 3)

[0776] The system according to claim 1, comprising communication means for notifying a user of the generated irrigation schedule.

[0777] "Example 1"

[0778] (Claim 1)

[0779] Means for acquiring information from a device for collecting environmental information,

[0780] A means of applying an analysis program to analyze the acquired information,

[0781] A means for generating a plan to optimize the cultivation of agricultural products based on the results of the analysis,

[0782] Methods for analyzing the genetic characteristics of agricultural products and developing varieties with specific characteristics,

[0783] A means for automatically carrying out irrigation based on the generated plan,

[0784] A means equipped with an automatic irrigation function to improve the efficiency of irrigation water use,

[0785] A system including communication equipment for distributing irrigation schedules optimized by an analysis program to users.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the analysis program uses a generation AI.

[0788] (Claim 3)

[0789] The system according to claim 1, comprising a function that allows the user to check the generated irrigation plan on a terminal in order to adjust it.

[0790] "Application Example 1"

[0791] (Claim 1)

[0792] Means for acquiring information from measuring devices for collecting environmental information,

[0793] A means of applying a model to analyze the acquired information,

[0794] Means for generating a plan to optimize plant growth based on the results of the analysis,

[0795] A means of analyzing the genetic characteristics of plants to develop species with specific traits,

[0796] Means for operating a device that automatically performs water supply based on a generated plan,

[0797] A means for notifying work equipment of the generated water supply plan, and for that equipment to automatically operate the water supply device,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, wherein the analysis model utilizes generative AI.

[0801] (Claim 3)

[0802] The system according to claim 1, comprising communication means for notifying users of the generated water supply plan.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] Means for acquiring information from sensing devices for collecting environmental information,

[0806] A means for applying an analysis algorithm to analyze the acquired information,

[0807] A means for generating a plan to optimize plant growth based on the results of the analysis,

[0808] A means of analyzing a person's emotional state from their voice and facial expressions to adjust the system's operation,

[0809] A system including means for automatically executing water supply based on a generated plan.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the analysis algorithm uses machine learning techniques.

[0812] (Claim 3)

[0813] The system according to claim 1, comprising a communication function for notifying users of the generated water supply plan.

[0814] "Application example 2 when combining with an emotional engine"

[0815] (Claim 1)

[0816] Means for acquiring information from sensing devices for collecting environmental information,

[0817] Means for applying evaluation methods to analyze acquired information,

[0818] A means for generating a plan to optimize the growth of agricultural products based on the evaluation results,

[0819] A method using an emotion engine to analyze the user's emotional state,

[0820] A system including means for automatically controlling irrigation equipment based on a generated plan.

[0821] (Claim 2)

[0822] The system according to claim 1, wherein the evaluation method uses a generative AI.

[0823] (Claim 3)

[0824] The system according to claim 1, comprising communication means for providing notifications adjusted according to the user's emotional state. [Explanation of symbols]

[0825] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring data from sensors to collect environmental data, A means of applying an analytical model to analyze the acquired data, A means for generating a schedule to optimize crop cultivation based on the results of the analysis, A means for analyzing the genetic characteristics of crops and developing varieties with specific traits, A system including means for automatically performing irrigation based on a generated schedule.

2. The system according to claim 1, wherein the analysis model uses generative AI.

3. The system according to claim 1, comprising communication means for notifying a user of the generated irrigation schedule.

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