system

Abstract

Provide a system. [Solution] A sensor device that measures soil condition, moisture content, and light intensity; a server device having a database that receives and stores measurement data from the sensor device; an artificial intelligence module that analyzes the data stored on the server device and generates predictions and advice regarding crop management; and means for notifying a user's terminal of the predictions and advice generated by the server device.

Description

[Technical Field]

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

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

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

[0004] In conventional agriculture, crop management and pest and disease predictions are often inefficient, leading to reduced yields and poor quality. In particular, it is difficult to grasp important environmental data such as soil moisture, light intensity, and temperature in real time, resulting in inappropriate timing for watering and pest control measures. Furthermore, analyzing this data and providing specific guidelines for action requires advanced knowledge and technology, placing a significant burden on many farmers and agricultural companies. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a sensor device that measures soil condition, moisture content, and light intensity; a server device equipped with a database that receives and stores measurement data from the sensor device; an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management; and means for notifying a user of the predictions and advice generated by the server device. In particular, the sensor device includes a soil moisture sensor, a temperature sensor, and a light sensor, enabling the acquisition of important environmental data in real time. Furthermore, the server device verifies the format and integrity of the received data and stores it in a database, enabling reliable data analysis. This system enables farmers and agricultural enterprises to manage their crops efficiently and effectively.

[0006] "Soil condition" is a general term for the physical and chemical properties of soil, specifically soil moisture content, nutrient concentration, pH value, etc.

[0007] "Moisture content" is numerical data that indicates the amount of water contained in a particular region or near the roots of a crop.

[0008] "Light intensity" is numerical data that indicates the intensity or illuminance of light received by crops.

[0009] A "sensor device" is a device that measures soil environmental data (e.g., moisture content, light intensity, temperature) in real time.

[0010] A "database" is an information repository that stores data measured by sensor devices and can be retrieved as needed.

[0011] The "server device" is a central management system that receives data sent from the sensor devices, stores it in a database, and performs further analysis.

[0012] An "artificial intelligence module" is a program that analyzes large amounts of data and generates predictions and advice on crop management using certain algorithms.

[0013] "Terminal" refers to a device through which a user receives analysis results and advice, and includes smartphones, tablets, and PCs.

[0014] The "notification means" is a mechanism for transmitting predictions and advice generated by the server device to the user's terminal.

[0015] "Data analysis" is the process of extracting useful information from measurement data stored in a database through statistical methods and machine learning algorithms.

[0016] "Prediction" refers to the advance indication of possible events regarding future crop management based on data analyzed by the artificial intelligence module.

[0017] "Advice" is information that provides the user with specific countermeasures and guidelines for action based on predictions. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system including a sensor device that measures soil condition, moisture content, and light intensity, a server device having a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device. An embodiment of the present invention will be described below with specific examples.

[0040] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0041] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0042] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the crop's growth status, water requirements, risk of pests, etc. These predictions and advice are generated based on the analysis results.

[0043] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0044] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if it predicts dryness in the next 48 hours, generates a notification such as "watering is required within the next 24 hours." This notification is sent to the user's smartphone, and the user can check the notification and water the plants at the appropriate time.

[0045] In this way, the system of the present invention efficiently and effectively supports crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the problems faced by conventional agriculture.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The sensors measure soil moisture, temperature, and light intensity, and each sensor collects data every three hours and transmits it wirelessly to a server.

[0049] Step 2:

[0050] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, the server logs this.

[0051] Step 3:

[0052] The server stores the received data in a database. When storing data, it records the measurement date and time and the sensor ID associated with each data.

[0053] Step 4:

[0054] The server performs pre-processing of the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0055] Step 5:

[0056] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a certain period of time and prepares it for analysis.

[0057] Step 6:

[0058] The artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water needs, and pest and disease risk.

[0059] Step 7:

[0060] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0061] Step 8:

[0062] The server compiles the analysis results and advice into a report that organizes the information in an easy-to-understand format.

[0063] Step 9:

[0064] The server sends the report to the user's device via a dedicated app, email, SMS, etc.

[0065] Step 10:

[0066] Users can check the report on their device, or log in to a dedicated app on their smartphone or tablet to check the latest forecast results and advice.

[0067] Step 11:

[0068] The user can take action based on the report, such as watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0069] This series of processing steps allows users to efficiently manage crops, resulting in increased yields and improved quality.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] Conventional agricultural management systems have fragmented data collection of soil conditions and weather, making it difficult to collect continuous, real-time data and analyze it. Furthermore, filling in abnormal and missing values ​​relies on manual work, making efficient crop management difficult. These issues increase the risk of impacting yields and crop quality.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes a means for the sensor device to transmit data wirelessly, a preprocessing means for checking the format and consistency of the received data and filling in outliers and missing values, and a means for analyzing the data using a generative AI model to predict the crop growth status, water requirements, and risk of pest and disease outbreaks. This enables continuous, real-time data collection and analysis, enabling efficient and effective crop management.

[0075] A "sensor device" is a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0076] "Server means" refers to means including a server for receiving, storing, and processing data transmitted from a sensor device.

[0077] A "database" is a storage system installed in a server for storing measurement data sent from sensor devices.

[0078] An "artificial intelligence module" is a software and hardware unit for analyzing stored data and generating predictions and advice regarding crop management.

[0079] The "communication means" is a means for notifying the user's terminal of the predictions and advice generated by the server means.

[0080] "Wireless communication" is a communication method for transmitting and receiving data wirelessly.

[0081] The "preprocessing means" is a means for checking the format and consistency of the data received by the server means and for complementing abnormal values ​​and missing values.

[0082] A "generative AI model" is an artificial intelligence algorithm that is trained using large amounts of data to perform a specific task (in this case, making predictions and giving advice about crop management).

[0083] "Crop management" refers to a series of activities that involve managing crop growth conditions and maintaining optimal conditions based on soil conditions and weather data.

[0084] A "prompt statement" is an instruction statement used when making an analysis request to an artificial intelligence model.

[0085] The present invention is a system for supporting efficient and effective crop management, which includes a plurality of sensor devices, a server means having a database, an artificial intelligence module, and a user terminal.

[0086] First, the user installs multiple sensor devices on farmland. These sensor devices measure the amount of moisture in the soil, temperature, and light. For example, the sensor devices measure data every three hours and transmit the data to the server means via wireless communication.

[0087] The server means stores the received data in a database (for example, a MySQL (registered trademark) database). The stored data is recorded along with the date and time. The server means checks the format and consistency of the data, and complements any abnormal or missing values. An abnormal value is an inaccurate value that occurs due to a sensor malfunction or a data transmission error. A data correction algorithm based on past data is used to complement the data.

[0088] The server then periodically provides the data to the artificial intelligence module. The artificial intelligence module analyzes the data using a generative AI model. Examples of AI frameworks used here include TENSORFLOW (registered trademark) and Keras, which are used to train large amounts of data to predict the crop's growth status, water requirements, risk of pest infestation, and so on. An example of a prompt is, "Based on the soil moisture, temperature, and light intensity data from the past week, please predict when watering is required within the next 48 hours." Based on this prompt, the artificial intelligence module performs analysis and generates predictions and advice.

[0089] The server sends the generated predictions and advice to the user's device. Notification methods include a dedicated application, email, and SMS. The user can check the prediction results and advice in real time through their device and take appropriate action. For example, if a user receives a notification that "watering is required within the next 24 hours," they can check their smartphone and water the plants at the appropriate time.

[0090] This system allows users to monitor the condition of farmland in real time and manage crops efficiently and effectively, which is expected to increase yields and improve quality. In addition, by filling in outliers and missing values, the reliability of the data is improved, allowing for faster and more accurate responses than traditional manual management methods.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1:

[0093] The sensor device collects the measurement data.

[0094] Input: Field environmental conditions (e.g., soil moisture, temperature, light intensity)

[0095] Data processing: Sensor devices measure these environmental conditions and convert them into digital data.

[0096] Output: Digitized measurement data

[0097] How it works: The sensor device measures the soil moisture, temperature, and light intensity every three hours.

[0098] Step 2:

[0099] The sensor device transmits measurement data to the server means.

[0100] Input: Digitized measurement data

[0101] Data processing: The sensor device transmits data via wireless communication (e.g., Wi-Fi, LTE).

[0102] Output: Measurement data sent to the server means

[0103] Specific operation: The sensor device transmits measurement data to the server means via Wi-Fi.

[0104] Step 3:

[0105] The server means stores the received data in a database.

[0106] Input: Measurement data sent from the sensor device

[0107] Data processing: The server checks the format and integrity of the received data and stores it in the database. If necessary, it will fill in any outliers or missing values.

[0108] Output: Consistent measurement data stored in a database

[0109] Specific operation: If the server detects an abnormal value, it complements the value based on past data.

[0110] Step 4:

[0111] A server means provides data to the artificial intelligence module.

[0112] Input: Measurement data stored in a database

[0113] Data processing: Periodically extract data and provide it to the artificial intelligence module.

[0114] Output: Data provided to the artificial intelligence module

[0115] Specific operation: At the end of each day, the server means transmits the data for the day to the artificial intelligence module.

[0116] Step 5:

[0117] An artificial intelligence module analyzes the data.

[0118] Input: Measurement data provided by the server means

[0119] Data processing: Generative AI models are used to analyze data and predict crop health, water needs, risk of pests and diseases, etc.

[0120] Output: Prediction results and advice

[0121] Specific behavior: Uses AI frameworks (e.g. TensorFlow, Keras) to analyze data and generate specific advice such as "watering is needed within the next 24 hours."

[0122] Step 6:

[0123] The server means notifies the user terminal of the prediction result and advice.

[0124] Input: Predictions and advice generated by the artificial intelligence module

[0125] Data processing: The data is formatted into a notification format and sent to the user's device via the appropriate communication method (dedicated app, email, SMS, etc.).

[0126] Output: Notification sent to the user's device

[0127] Specific operation: The server means sends the generated advice to the smartphone app, and the user confirms the notification.

[0128] Step 7:

[0129] The user acts on the advice.

[0130] Input: Prediction results and advice displayed on the user's device

[0131] Specific behavior: The user checks the notification, follows the advice such as "Watering is required within the next 24 hours," and waters the plant at the appropriate time.

[0132] Through these steps, the system can monitor the environmental conditions of farmland in real time and manage crops efficiently and effectively.

[0133] (Application example 1)

[0134] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0135] Conventional autonomous vehicles lack the ability to monitor the environment in real time to respond to changes in the driving environment and propose appropriate driving conditions based on that analysis. This means that driving optimization according to road and weather conditions is not fully achieved, increasing the risk of accidents and breakdowns.

[0136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0137] In this invention, the server includes means for receiving and storing measurement data from sensor devices that measure soil conditions, moisture content, and light intensity, means for having an artificial intelligence module that analyzes the measurement data and generates predictions and advice regarding crop management and the vehicle's driving environment, and means for notifying the user's terminal of the generated predictions and advice. This enables the vehicle to monitor the driving environment, propose optimal routes in real time, and generate and provide notifications to the user according to the driving conditions.

[0138] "Soil condition" refers to the physical and chemical properties of the soil, including soil moisture, temperature, and nutrient content.

[0139] "Moisture content" is the amount of water present in a particular environment, expressed as humidity in the soil or air.

[0140] "Light intensity" refers to the intensity or amount of light in a particular location, and is usually measured using a light meter or similar device.

[0141] A "sensor device" is a device for measuring physical environmental data, and is used to obtain information such as temperature, humidity, and light intensity.

[0142] "Measurement data" refers to the numerical and digital data of environmental information acquired by sensor devices.

[0143] A "database" is a system for storing measurement data in an organized manner so that it can be easily accessed later.

[0144] A "server device" is a computer device that receives, stores, and analyzes measurement data over a network.

[0145] An "artificial intelligence module" is a software component that analyzes accumulated data and generates predictions and advice.

[0146] "User Device" means an electronic device that a User uses to receive, display, and interact with business or data, including, for example, a smartphone or tablet.

[0147] "Driving environment" is a general term for the surrounding physical conditions, weather, road conditions, etc. when an autonomous vehicle is driving.

[0148] An "optimal route" is a route or driving path that allows a user or vehicle to reach a destination most efficiently and safely.

[0149] "Notification" refers to messages and alerts that convey information such as prediction results and advice to users.

[0150] This invention is a system that includes a sensor device that measures soil conditions, moisture content, and light intensity, a server device with a database that receives and stores the measurement data, an artificial intelligence module that analyzes the data and generates predictions and advice regarding crop management and vehicle driving environments, and means for notifying a user's terminal of the generated predictions and advice.

[0151] First, the user installs multiple sensor devices on the vehicle to monitor the driving environment. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the measurement data wirelessly to a server device. This allows data on the driving environment to be obtained in real time.

[0152] The server device stores the received data in a database. The data sent from each sensor device is recorded in the database along with the date and time. The server device checks the format and consistency of the data, and complements any abnormal or missing values ​​as necessary.

[0153] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the growth conditions of the driving environment, the amount of water needed, the condition of the road surface, etc. These predictions and advice are generated based on the analysis results.

[0154] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. For example, the server device can provide specific information about slippery roads and the optimal driving route.

[0155] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares the data with past data and, if it predicts that roads will become slippery in the next 48 hours, generates a notification such as "Please reduce your speed and drive carefully within the next three hours." This notification is sent to the user's smartphone, and the user can check the notification and take appropriate action at the appropriate time.

[0156] Example prompt sentence:

[0157] Based on the humidity and temperature data of the road surface acquired by the sensor, predict future driving risks and notify the user of appropriate driving route advice. For example, if the humidity is high and the temperature is low, give advice such as "Be careful as the road surface is slippery."

[0158] In this way, the system of the present invention efficiently and effectively supports vehicle driving management, improving safety and enabling efficient route selection. Real-time data acquisition and analysis enables rapid response, solving many of the problems faced by conventional self-driving vehicles.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1:

[0161] The sensor device measures soil conditions, moisture content, and light intensity. The input is the local soil condition, moisture content, and light intensity, and the output is the corresponding measurement data. Specifically, each sensor collects environmental data at regular intervals and converts it into digital data.

[0162] Step 2:

[0163] Measurement data from the sensor device is sent wirelessly to the server device. The input is the measurement data from the sensor device, and the output is raw data that is stored in the server device. Specifically, the sensor device sends data to the server device using wireless communication technology such as Wi-Fi or LoRa.

[0164] Step 3:

[0165] The server device checks the format and consistency of the data it receives. The input is raw data from the sensor device, and the output is formatted and accurate data. Specifically, the server device detects outliers and missing values ​​and complements the data as necessary.

[0166] Step 4:

[0167] The server device stores the organized data in a database. The input is the formatted sensor data, and the output is the data recorded in the database. Specifically, the server device uses a database management system to store the data along with date and time information.

[0168] Step 5:

[0169] The server device periodically provides data to the AI ​​module. The input is measurement data stored in the database, and the output is analysis data passed to the AI ​​module. Specifically, the server device uses queries to extract the required data from the database and send it to the AI ​​module.

[0170] Step 6:

[0171] The artificial intelligence module analyzes the provided data and generates predictions and advice regarding the driving environment. The input is measurement data provided by the server device, and the output is prediction results and advice. Specifically, it uses a generative AI model to compare with past data and predict future driving conditions. For example, it generates a prediction that "road surfaces will become slippery in the next 48 hours."

[0172] Step 7:

[0173] The server device notifies the user's device of the generated predictions and advice. The input is the prediction result and advice from the artificial intelligence module, and the output is a notification message sent to the user's device. Specifically, the server device sends a message to the user's device using a notification service (e.g., push notification, SMS, email).

[0174] Step 8:

[0175] The user uses the device to check the prediction results and advice in real time. The input is the notification message sent from the server device, and the output is the information received by the user. Specifically, the user launches a dedicated application and checks the received notification. For example, the user receives information such as "Please reduce your speed and drive carefully within the next three hours."

[0176] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0177] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device equipped with a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device, and further combines this with an emotion engine that recognizes the user's emotions.Embodiments of the present invention will be described below with specific examples.

[0178] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0179] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0180] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data to predict the crop's growth status, water requirements, risk of pests, etc. Based on the analysis results, it also generates advice as a concrete course of action.

[0181] The server also houses an emotion engine, which analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is tired or stressed, the engine adjusts the content of the notification to make it easier to understand or to include an encouraging message.

[0182] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0183] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an AI module. The AI ​​module compares it with past data and, if dryness is predicted for the next 48 hours, generates a notification such as "Watering is required within the next 24 hours." This notification is sent to the user's smartphone, and an emotion engine analyzes the user's emotional state and adjusts the content of the notification as needed. For example, if the user appears busy, a short message and concise advice will be provided.

[0184] In this way, the system of the present invention efficiently and effectively supports crop management, resulting in increased yields and improved quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the challenges faced by traditional agriculture. The addition of an emotion engine provides customized support based on the user's emotional state, improving ease of use and satisfaction.

[0185] The processing flow will be explained below.

[0186] Step 1:

[0187] The sensors measure soil moisture, temperature, and light intensity, collecting data every three hours and transmitting it wirelessly to a server.

[0188] Step 2:

[0189] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, they are logged.

[0190] Step 3:

[0191] The server stores the received data in a database. When storing data, it associates the measurement date and time and the sensor ID with each data.

[0192] Step 4:

[0193] The server pre-processes the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0194] Step 5:

[0195] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a period of time and sends it to the artificial intelligence for analysis.

[0196] Step 6:

[0197] An artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water requirements, and pest and disease risk.

[0198] Step 7:

[0199] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0200] Step 8:

[0201] The server compiles the analysis results and advice into a report, which is presented to the user in an easy-to-understand format.

[0202] Step 9:

[0203] The server provides the generated report to the emotion engine, which analyzes the user's reactions and behavior logs on the device to infer the user's emotional state.

[0204] Step 10:

[0205] An emotion engine tailors the report content based on the user's emotional state, for example providing short, concise advice if the user seems busy, or including more detailed information if the user seems interested.

[0206] Step 11:

[0207] The server then sends the final adjusted report to the user's device via a dedicated app, email, SMS, etc.

[0208] Step 12:

[0209] Users check the report on their devices. Users log in to a dedicated app on their smartphone or tablet and check the latest forecast results and advice.

[0210] Step 13:

[0211] The user can take action based on the report, for example, watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0212] This series of processing steps enables the server and emotion engine to efficiently and effectively support crop management and improve user satisfaction.

[0213] Example 2

[0214] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] Conventional agricultural land management systems lack accuracy due to inconsistencies in data obtained from sensors and insufficient correction of abnormal values. Furthermore, the generated forecasts and advice were not adjusted based on the user's emotional state, resulting in inappropriate notification content and reduced user convenience and satisfaction. These issues made it difficult to accurately predict crop growth conditions and implement appropriate countermeasures.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0217] In this invention, the server includes a means for correcting the received data using a consistency and complementation algorithm, an artificial intelligence module for analyzing the data and generating predictions and advice on crop management, and a means for analyzing the user's emotions using an embedded emotion recognition engine and adjusting the notification content based on the user's emotional state. This ensures the accuracy of the data and optimizes the predictions and advice on crop management to suit the user's emotional state, improving user convenience and satisfaction and enabling more accurate predictions of crop growth conditions and the implementation of appropriate measures.

[0218] "Sensor equipment" refers to devices that measure soil conditions, moisture content, or light levels, including soil moisture sensors, temperature sensors, and light sensors.

[0219] "Database" refers to an information management system for storing received data along with its date and time. Generally, a relational database management system (RDBMS) is used.

[0220] "Server device" refers to a computer system that has the function of receiving, storing, analyzing, and notifying data sent from a sensor device.

[0221] "Artificial Intelligence Module" refers to a system that includes software and algorithms for analyzing stored data and generating predictions and advice regarding crop management.

[0222] "Emotion recognition engine" refers to a system that includes software and hardware for recognizing and analyzing a user's emotions, including facial expression analysis and voice analysis.

[0223] "Notification means" refers to a method for transmitting the generated predictions and advice from the server device to the user's device, including a dedicated application, email, SMS, etc.

[0224] "Integrity and Imputation Algorithms" means mathematical and statistical methods for verifying the integrity of received data and correcting inconsistent or missing data.

[0225] "User Device" means the electronic device used by a User to view Notifications, including a smartphone or tablet.

[0226] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device that collects and analyzes the data, and notifies the user of the obtained predictions and advice via their device. It also has the function of recognizing the user's emotions and adjusting notifications according to their state.

[0227] Configuration of the embodiment

[0228] Sensor device

[0229] Users install multiple sensor devices on farmland, including soil moisture sensors, temperature sensors, and light sensors. Each sensor device uses sensors such as Bosch's BME680 to measure soil moisture, temperature, and light intensity. The sensor devices transmit measurement data to a server device using wireless communication technologies such as LoRa and ZigBee.

[0230] Server device

[0231] The server device stores the received data in a database such as MySQL along with the date, time, and sensor ID. The program checks the data's integrity, and if outliers or missing values ​​are detected, it corrects them using imputation algorithms such as the Reuter series model and linear interpolation. The server device periodically exports the accumulated data in CSV or JSON format and provides it to an artificial intelligence module for analysis.

[0232] The AI ​​module runs models trained using popular machine learning frameworks such as TensorFlow. These models analyze past and current data to predict crop growth conditions, water needs, risk of pest infestation, etc. Based on the analysis results, specific advice is generated as a course of action (e.g., "Watering is required within the next 24 hours").

[0233] The server is equipped with an emotion recognition engine that analyzes facial expressions, voice, and touch operations when a user checks a report on their device to recognize the user's emotional state. For example, if the user is tired or stressed, the notification content can be adjusted to include a more understandable explanation or an encouraging message.

[0234] Notification methods and devices

[0235] The server device notifies the user's device of the generated predictions and advice. Notification methods include a dedicated application developed with Flutter (registered trademark), email, and SMS. The user can use their device to check the prediction results and advice in real time. For example, specific guidelines such as "Watering is required within 24 hours" are displayed.

[0236] Specific examples

[0237] For example, suppose a user installs multiple BME680 sensors in their field, each measuring soil moisture and temperature every three hours. The measurement data is sent to a server using a ZigBee module. The server stores the received data in a MySQL database and exports the day's data in CSV format at 6 p.m. every day, providing it to an artificial intelligence module built with TensorFlow. The AI ​​module analyzes the data from the past 30 days and the latest data to generate a prediction that "watering is required within the next 24 hours." This prediction is sent back to the server, where an emotion recognition engine analyzes the user's emotions. If the user appears busy, a short message will be displayed, concisely stating that "watering is required within the next 24 hours."

[0238] Prompt Sentence Examples

[0239] "Analyze sensor data to predict environmental changes over the next 48 hours, and generate watering needs and specific advice."

[0240] As described above, the present invention can efficiently and effectively support crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, and provides customized support based on the user's emotional state.

[0241] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0242] Step 1: Data collection

[0243] Users install sensor devices in farmland to measure soil conditions, moisture content, and light intensity. Specific examples include soil moisture sensors, temperature sensors, and light sensors.

[0244] The sensor device uses a BME680 sensor to measure soil moisture and temperature every three hours.

[0245] The sensor device transmits measurement data (e.g., moisture content, temperature, light intensity) to a server via wireless communication (e.g., LoRa or ZigBee).

[0246] Input: Measurement data from sensor devices (e.g. moisture content, temperature, light intensity)

[0247] Output: Measurement data sent to the server

[0248] Step 2: Save data

[0249] The server stores the received measurement data in a MySQL database.

[0250] The server validates the data for format and integrity, for example checking that the data is formatted correctly and that all required fields are filled in.

[0251] The server detects outliers and missing values ​​and imputes the data using a Reuter series model and linear imputation algorithms.

[0252] Input: Received measurement data

[0253] Output: A database containing the validated and imputed data.

[0254] Step 3: Data analysis

[0255] The server periodically exports the stored data in CSV or JSON format and provides it to the AI ​​module, for example, exporting the day's data at 6:00 PM every day.

[0256] The artificial intelligence module analyzes historical and current data using TensorFlow.

[0257] The AI ​​module predicts crop growth conditions, water needs, risk of pest and disease outbreaks, and more.

[0258] Input: Historical and current data exported from databases

[0259] Output: Analysis results (e.g. predicted crop condition, water requirements, pest risk)

[0260] Step 4: Generate predictions and advice

[0261] Based on the analysis results, the AI ​​module generates advice for the user as a specific course of action, such as "watering is required within the next 24 hours."

[0262] The generated advice is sent back to the server in JSON format.

[0263] Input: Analysis results

[0264] Output: Generated advice (JSON format)

[0265] Step 5: Sentiment analysis and notification adjustment

[0266] When users check their reports, they use the device's camera and microphone, which allows the collection of facial expression and voice data.

[0267] The device transmits the collected facial expression and voice data to a server.

[0268] The emotion recognition engine analyzes facial expressions and voice data on the server to recognize the user's emotional state, for example, to determine whether the user is tired.

[0269] The server optimizes the notification content based on the user's emotional state, for example, if the user seems busy, it will send a short and concise notification.

[0270] Input: Collected facial and voice data

[0271] Output: Notification content adjusted based on emotional state

[0272] Step 6: Sending notifications

[0273] The server then sends optimized notifications to the user's device via a dedicated application, email, or SMS. The dedicated application is a mobile app developed with Flutter.

[0274] The terminal displays the received notification to the user in real time.

[0275] Input: Notification content tailored based on emotional state

[0276] Output: Notification displayed on the user's device

[0277] Through these steps, the system efficiently collects, stores, and analyzes data, and provides appropriate advice based on the user's emotional state. This is expected to improve the accuracy of crop management and enhance user convenience.

[0278] (Application example 2)

[0279] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0280] Modern home and office environments demand improved safety. However, current security systems simply use security cameras and simple sensors, making it difficult to prevent or respond immediately to environmental changes or sudden accidents. Furthermore, there is no flexible notification system that takes into account the user's emotional state, so there is a need for instantaneous detection of danger and effective countermeasures.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity, a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, an emotion engine that recognizes the user's emotional state, and a means for notifying the user's terminal of the predictions and advice generated by the server device. This makes it possible to instantly detect suspicious behavior while the user is away or dangers such as fires and water leaks, and to provide appropriate notifications according to the user's emotional state.

[0282] A "sensor device" is a device that measures environmental data such as soil condition, moisture content, and light intensity, and transmits this data wirelessly to a server.

[0283] A "database" is a storage system that stores data received from sensor devices and complements abnormal or missing values ​​as necessary.

[0284] A "server device" is a collection of hardware and software that has a database and manages a series of processes that receive and analyze data from sensor devices and notify the user's terminal.

[0285] An "artificial intelligence module" is a component that includes algorithms and models for analyzing accumulated data and generating predictions and advice.

[0286] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, touch operations, etc., and customizes the notification content based on the analysis results.

[0287] The "notification means" refers to the means for delivering the generated predictions and advice to the user's terminal, and may be a dedicated application, email, SMS, or the like.

[0288] This invention is a security system for improving indoor safety, and uses a server device equipped with a database and sensors that measure soil conditions, moisture content, and light intensity. The server device also incorporates an artificial intelligence module and an emotion engine, which work together to generate predictions and advice, which are then sent to the user's terminal.

[0289] First, the user installs multiple sensor devices in a room. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows the sensor devices to obtain environmental data in real time, independent of time and location.

[0290] The server device stores the received data in a database. The database records the data sent from each sensor device along with the date and time. The server checks the format and consistency of the data and fills in any outliers or missing values ​​as necessary. The software used in this process includes a database management system (DBMS) and a data processing tool.

[0291] The server device then periodically provides the data to an artificial intelligence module, which analyzes the accumulated data and predicts the indoor environmental conditions and potential hazards. For example, if the temperature is abnormally high, it generates a notification indicating a fire risk. It also notifies users of water leaks or dry conditions if there is an excess or shortage of moisture. These analyses are performed using machine learning algorithms and data analysis software.

[0292] Next, the server device is equipped with an emotion engine. The emotion engine analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is nervous, the notification content will be simplified to allow for a faster response. The facial recognition software and voice analysis tools used for this purpose estimate emotions by analyzing the user's facial and vocal characteristics.

[0293] The server device notifies the user device of the generated predictions and advice. This notification is done via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. This process utilizes mobile notification services and email delivery systems.

[0294] As a concrete example, consider a scenario in which a sensor device measures the temperature, moisture content, and light intensity in a room every hour and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if an abnormality is predicted for the next 24 hours, generates a notification, for example, "The temperature is too high, so there is a risk of fire." This notification is sent to the user's smartphone, and the emotion engine analyzes the user's emotional state and adjusts the content of the notification as necessary. For example, if the user appears busy, it may provide a short message and concise advice.

[0295] An example of an input prompt for the generative AI model is, "Simplify the notification messages that users receive when they are under stress. If there is a high security risk, generate a concise notification message that encourages urgent action." As this prompt shows, it is designed to be able to respond flexibly according to the user's situation.

[0296] In this way, the present invention efficiently and effectively improves indoor safety, enabling rapid risk prediction and response.The addition of an emotion engine provides customized support based on the user's emotional state, improving usability and satisfaction.

[0297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0298] Step 1:

[0299] The sensor device measures soil conditions, moisture content, and light intensity. As input, it uses data acquired in real time from the environment where the sensor is installed. This data is concrete numerical data such as soil moisture content, temperature, and light intensity. The sensor device transmits the measurement data wirelessly to a server.

[0300] Step 2:

[0301] The server receives measurement data sent from the sensor devices and stores it in a database. As input, it receives data sent from the sensors. It checks the format and consistency of the received data and imputes any abnormal or missing values. As output, the organized data is stored in the database.

[0302] Step 3:

[0303] The server provides the data stored in the database to the artificial intelligence module. The accumulated data in the database is used as input. The artificial intelligence module analyzes the data using machine learning algorithms to predict the indoor environmental conditions and potential hazards. The output is prediction results and advice.

[0304] Step 4:

[0305] The server rechecks the predictions and advice generated by the AI ​​module and passes them to the emotion engine. It uses the analysis results from the AI ​​module as input. The emotion engine analyzes the user's emotional state (facial expression, voice, touch operations, etc.) when checking the report on their device and generates appropriate notification content. As output, it generates a notification tailored to the user's emotional state.

[0306] Step 5:

[0307] The server notifies the user's device of the generated predictions and advice. As input, it receives the adjusted notification content from the emotion engine. As output, a notification is delivered to the user's device, such as a smartphone or smart glasses. This notification is sent via a dedicated application, email, or SMS.

[0308] Step 6:

[0309] The user uses the device to check the notification and take necessary action. The notification message received from the server is used as input. Suspicious behavior during absence, fire risk, water leak risk, etc. can be checked immediately. The output is that the user takes specific action.

[0310] This series of steps allows the security system to function effectively, enabling rapid response based on environmental data and improving indoor safety. Tailoring notification content to the user's emotional state provides a more user-friendly and satisfying system.

[0311] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0312] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0313] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0314] [Second embodiment]

[0315] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0316] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0317] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0318] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0319] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0320] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0321] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0322] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0323] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0324] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0325] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0326] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0327] The present invention relates to a system including a sensor device that measures soil condition, moisture content, and light intensity, a server device having a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device. An embodiment of the present invention will be described below with specific examples.

[0328] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0329] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0330] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the crop's growth status, water requirements, risk of pests, etc. These predictions and advice are generated based on the analysis results.

[0331] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0332] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if it predicts dryness in the next 48 hours, generates a notification such as "watering is required within the next 24 hours." This notification is sent to the user's smartphone, and the user can check the notification and water the plants at the appropriate time.

[0333] In this way, the system of the present invention efficiently and effectively supports crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the problems faced by conventional agriculture.

[0334] The processing flow will be explained below.

[0335] Step 1:

[0336] The sensors measure soil moisture, temperature, and light intensity, and each sensor collects data every three hours and transmits it wirelessly to a server.

[0337] Step 2:

[0338] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, the server logs this.

[0339] Step 3:

[0340] The server stores the received data in a database. When storing data, it records the measurement date and time and the sensor ID associated with each data.

[0341] Step 4:

[0342] The server performs pre-processing of the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0343] Step 5:

[0344] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a certain period of time and prepares it for analysis.

[0345] Step 6:

[0346] The artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water needs, and pest and disease risk.

[0347] Step 7:

[0348] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0349] Step 8:

[0350] The server compiles the analysis results and advice into a report that organizes the information in an easy-to-understand format.

[0351] Step 9:

[0352] The server sends the report to the user's device via a dedicated app, email, SMS, etc.

[0353] Step 10:

[0354] Users can check the report on their device, or log in to a dedicated app on their smartphone or tablet to check the latest forecast results and advice.

[0355] Step 11:

[0356] The user can take action based on the report, such as watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0357] This series of processing steps allows users to efficiently manage crops, resulting in increased yields and improved quality.

[0358] Example 1

[0359] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0360] Conventional agricultural management systems have fragmented data collection of soil conditions and weather, making it difficult to collect continuous, real-time data and analyze it. Furthermore, filling in abnormal and missing values ​​relies on manual work, making efficient crop management difficult. These issues increase the risk of impacting yields and crop quality.

[0361] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0362] In this invention, the server includes a means for the sensor device to transmit data wirelessly, a preprocessing means for checking the format and consistency of the received data and filling in outliers and missing values, and a means for analyzing the data using a generative AI model to predict the crop growth status, water requirements, and risk of pest and disease outbreaks. This enables continuous, real-time data collection and analysis, enabling efficient and effective crop management.

[0363] A "sensor device" is a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0364] "Server means" refers to means including a server for receiving, storing, and processing data transmitted from a sensor device.

[0365] A "database" is a storage system installed in a server for storing measurement data sent from sensor devices.

[0366] An "artificial intelligence module" is a software and hardware unit for analyzing stored data and generating predictions and advice regarding crop management.

[0367] The "communication means" is a means for notifying the user's terminal of the predictions and advice generated by the server means.

[0368] "Wireless communication" is a communication method for transmitting and receiving data wirelessly.

[0369] The "preprocessing means" is a means for checking the format and consistency of the data received by the server means and for complementing abnormal values ​​and missing values.

[0370] A "generative AI model" is an artificial intelligence algorithm that is trained using large amounts of data to perform a specific task (in this case, making predictions and giving advice about crop management).

[0371] "Crop management" refers to a series of activities that involve managing crop growth conditions and maintaining optimal conditions based on soil conditions and weather data.

[0372] A "prompt statement" is an instruction statement used when making an analysis request to an artificial intelligence model.

[0373] The present invention is a system for supporting efficient and effective crop management, which includes a plurality of sensor devices, a server means having a database, an artificial intelligence module, and a user terminal.

[0374] First, the user installs multiple sensor devices on farmland. These sensor devices measure the amount of moisture in the soil, temperature, and light. For example, the sensor devices measure data every three hours and transmit the data to the server means via wireless communication.

[0375] The server means stores the received data in a database (for example, a MySQL database). The stored data is recorded along with the date and time. The server means checks the format and consistency of the data, and if there are any abnormal or missing values, it complements them. An abnormal value is an inaccurate value that occurs due to a sensor malfunction or a data transmission error. A data correction algorithm based on past data is used to complement the data.

[0376] The server means then periodically provides the data to the artificial intelligence module. The artificial intelligence module analyzes the data using a generative AI model. Examples of AI frameworks used here include TensorFlow and Keras, which are used to train large amounts of data to predict the crop's growth status, water requirements, risk of pest infestation, and so on. An example prompt is, "Based on the soil moisture, temperature, and light intensity data from the past week, please predict when watering is required within the next 48 hours." Based on this prompt, the artificial intelligence module performs analysis and generates predictions and advice.

[0377] The server sends the generated predictions and advice to the user's device. Notification methods include a dedicated application, email, and SMS. The user can check the prediction results and advice in real time through their device and take appropriate action. For example, if a user receives a notification that "watering is required within the next 24 hours," they can check their smartphone and water the plants at the appropriate time.

[0378] This system allows users to monitor the condition of farmland in real time and manage crops efficiently and effectively, which is expected to increase yields and improve quality. In addition, by filling in outliers and missing values, the reliability of the data is improved, allowing for faster and more accurate responses than traditional manual management methods.

[0379] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0380] Step 1:

[0381] The sensor device collects the measurement data.

[0382] Input: Field environmental conditions (e.g., soil moisture, temperature, light intensity)

[0383] Data processing: Sensor devices measure these environmental conditions and convert them into digital data.

[0384] Output: Digitized measurement data

[0385] How it works: The sensor device measures the soil moisture, temperature, and light intensity every three hours.

[0386] Step 2:

[0387] The sensor device transmits measurement data to the server means.

[0388] Input: Digitized measurement data

[0389] Data processing: The sensor device transmits data via wireless communication (e.g., Wi-Fi, LTE).

[0390] Output: Measurement data sent to the server means

[0391] Specific operation: The sensor device transmits measurement data to the server means via Wi-Fi.

[0392] Step 3:

[0393] The server means stores the received data in a database.

[0394] Input: Measurement data sent from the sensor device

[0395] Data processing: The server checks the format and integrity of the received data and stores it in the database. If necessary, it will fill in any outliers or missing values.

[0396] Output: Consistent measurement data stored in a database

[0397] Specific operation: If the server detects an abnormal value, it complements the value based on past data.

[0398] Step 4:

[0399] A server means provides data to the artificial intelligence module.

[0400] Input: Measurement data stored in a database

[0401] Data processing: Periodically extract data and provide it to the artificial intelligence module.

[0402] Output: Data provided to the artificial intelligence module

[0403] Specific operation: At the end of each day, the server means transmits the data for the day to the artificial intelligence module.

[0404] Step 5:

[0405] An artificial intelligence module analyzes the data.

[0406] Input: Measurement data provided by the server means

[0407] Data processing: Generative AI models are used to analyze data and predict crop health, water needs, risk of pests and diseases, etc.

[0408] Output: Prediction results and advice

[0409] Specific behavior: Uses AI frameworks (e.g. TensorFlow, Keras) to analyze data and generate specific advice such as "watering is needed within the next 24 hours."

[0410] Step 6:

[0411] The server means notifies the user terminal of the prediction result and advice.

[0412] Input: Predictions and advice generated by the artificial intelligence module

[0413] Data processing: The data is formatted into a notification format and sent to the user's device via the appropriate communication method (dedicated app, email, SMS, etc.).

[0414] Output: Notification sent to the user's device

[0415] Specific operation: The server means sends the generated advice to the smartphone app, and the user confirms the notification.

[0416] Step 7:

[0417] The user acts on the advice.

[0418] Input: Prediction results and advice displayed on the user's device

[0419] Specific behavior: The user checks the notification, follows the advice such as "Watering is required within the next 24 hours," and waters the plant at the appropriate time.

[0420] Through these steps, the system can monitor the environmental conditions of farmland in real time and manage crops efficiently and effectively.

[0421] (Application example 1)

[0422] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0423] Conventional autonomous vehicles lack the ability to monitor the environment in real time to respond to changes in the driving environment and propose appropriate driving conditions based on that analysis. This means that driving optimization according to road and weather conditions is not fully achieved, increasing the risk of accidents and breakdowns.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0425] In this invention, the server includes means for receiving and storing measurement data from sensor devices that measure soil conditions, moisture content, and light intensity, means for having an artificial intelligence module that analyzes the measurement data and generates predictions and advice regarding crop management and the vehicle's driving environment, and means for notifying the user's terminal of the generated predictions and advice. This enables the vehicle to monitor the driving environment, propose optimal routes in real time, and generate and provide notifications to the user according to the driving conditions.

[0426] "Soil condition" refers to the physical and chemical properties of the soil, including soil moisture, temperature, and nutrient content.

[0427] "Moisture content" is the amount of water present in a particular environment, expressed as humidity in the soil or air.

[0428] "Light intensity" refers to the intensity or amount of light in a particular location, and is usually measured using a light meter or similar device.

[0429] A "sensor device" is a device for measuring physical environmental data, and is used to obtain information such as temperature, humidity, and light intensity.

[0430] "Measurement data" refers to the numerical and digital data of environmental information acquired by sensor devices.

[0431] A "database" is a system for storing measurement data in an organized manner so that it can be easily accessed later.

[0432] A "server device" is a computer device that receives, stores, and analyzes measurement data over a network.

[0433] An "artificial intelligence module" is a software component that analyzes accumulated data and generates predictions and advice.

[0434] "User Device" means an electronic device that a User uses to receive, display, and interact with business or data, including, for example, a smartphone or tablet.

[0435] "Driving environment" is a general term for the surrounding physical conditions, weather, road conditions, etc. when an autonomous vehicle is driving.

[0436] An "optimal route" is a route or driving path that allows a user or vehicle to reach a destination most efficiently and safely.

[0437] "Notification" refers to messages and alerts that convey information such as prediction results and advice to users.

[0438] This invention is a system that includes a sensor device that measures soil conditions, moisture content, and light intensity, a server device with a database that receives and stores the measurement data, an artificial intelligence module that analyzes the data and generates predictions and advice regarding crop management and vehicle driving environments, and means for notifying a user's terminal of the generated predictions and advice.

[0439] First, the user installs multiple sensor devices on the vehicle to monitor the driving environment. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the measurement data wirelessly to a server device. This allows data on the driving environment to be obtained in real time.

[0440] The server device stores the received data in a database. The data sent from each sensor device is recorded in the database along with the date and time. The server device checks the format and consistency of the data, and complements any abnormal or missing values ​​as necessary.

[0441] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the growth conditions of the driving environment, the amount of water needed, the condition of the road surface, etc. These predictions and advice are generated based on the analysis results.

[0442] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. For example, the server device can provide specific information about slippery roads and the optimal driving route.

[0443] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares the data with past data and, if it predicts that roads will become slippery in the next 48 hours, generates a notification such as "Please reduce your speed and drive carefully within the next three hours." This notification is sent to the user's smartphone, and the user can check the notification and take appropriate action at the appropriate time.

[0444] Example prompt sentence:

[0445] Based on the humidity and temperature data of the road surface acquired by the sensor, predict future driving risks and notify the user of appropriate driving route advice. For example, if the humidity is high and the temperature is low, give advice such as "Be careful as the road surface is slippery."

[0446] In this way, the system of the present invention efficiently and effectively supports vehicle driving management, improving safety and enabling efficient route selection. Real-time data acquisition and analysis enables rapid response, solving many of the problems faced by conventional self-driving vehicles.

[0447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0448] Step 1:

[0449] The sensor device measures soil conditions, moisture content, and light intensity. The input is the local soil condition, moisture content, and light intensity, and the output is the corresponding measurement data. Specifically, each sensor collects environmental data at regular intervals and converts it into digital data.

[0450] Step 2:

[0451] Measurement data from the sensor device is sent wirelessly to the server device. The input is the measurement data from the sensor device, and the output is raw data that is stored in the server device. Specifically, the sensor device sends data to the server device using wireless communication technology such as Wi-Fi or LoRa.

[0452] Step 3:

[0453] The server device checks the format and consistency of the data it receives. The input is raw data from the sensor device, and the output is formatted and accurate data. Specifically, the server device detects outliers and missing values ​​and complements the data as necessary.

[0454] Step 4:

[0455] The server device stores the organized data in a database. The input is the formatted sensor data, and the output is the data recorded in the database. Specifically, the server device uses a database management system to store the data along with date and time information.

[0456] Step 5:

[0457] The server device periodically provides data to the AI ​​module. The input is measurement data stored in the database, and the output is analysis data passed to the AI ​​module. Specifically, the server device uses queries to extract the required data from the database and send it to the AI ​​module.

[0458] Step 6:

[0459] The artificial intelligence module analyzes the provided data and generates predictions and advice regarding the driving environment. The input is measurement data provided by the server device, and the output is prediction results and advice. Specifically, it uses a generative AI model to compare with past data and predict future driving conditions. For example, it generates a prediction that "road surfaces will become slippery in the next 48 hours."

[0460] Step 7:

[0461] The server device notifies the user's device of the generated predictions and advice. The input is the prediction result and advice from the artificial intelligence module, and the output is a notification message sent to the user's device. Specifically, the server device sends a message to the user's device using a notification service (e.g., push notification, SMS, email).

[0462] Step 8:

[0463] The user uses the device to check the prediction results and advice in real time. The input is the notification message sent from the server device, and the output is the information received by the user. Specifically, the user launches a dedicated application and checks the received notification. For example, the user receives information such as "Please reduce your speed and drive carefully within the next three hours."

[0464] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0465] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device equipped with a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device, and further combines this with an emotion engine that recognizes the user's emotions.Embodiments of the present invention will be described below with specific examples.

[0466] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0467] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0468] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data to predict the crop's growth status, water requirements, risk of pests, etc. Based on the analysis results, it also generates advice as a concrete course of action.

[0469] The server also houses an emotion engine, which analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is tired or stressed, the engine adjusts the content of the notification to make it easier to understand or to include an encouraging message.

[0470] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0471] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an AI module. The AI ​​module compares it with past data and, if dryness is predicted for the next 48 hours, generates a notification such as "Watering is required within the next 24 hours." This notification is sent to the user's smartphone, and an emotion engine analyzes the user's emotional state and adjusts the content of the notification as needed. For example, if the user appears busy, a short message and concise advice will be provided.

[0472] In this way, the system of the present invention efficiently and effectively supports crop management, resulting in increased yields and improved quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the challenges faced by traditional agriculture. The addition of an emotion engine provides customized support based on the user's emotional state, improving ease of use and satisfaction.

[0473] The processing flow will be explained below.

[0474] Step 1:

[0475] The sensors measure soil moisture, temperature, and light intensity, collecting data every three hours and transmitting it wirelessly to a server.

[0476] Step 2:

[0477] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, they are logged.

[0478] Step 3:

[0479] The server stores the received data in a database. When storing data, it associates the measurement date and time and the sensor ID with each data.

[0480] Step 4:

[0481] The server pre-processes the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0482] Step 5:

[0483] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a period of time and sends it to the artificial intelligence for analysis.

[0484] Step 6:

[0485] An artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water requirements, and pest and disease risk.

[0486] Step 7:

[0487] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0488] Step 8:

[0489] The server compiles the analysis results and advice into a report, which is presented to the user in an easy-to-understand format.

[0490] Step 9:

[0491] The server provides the generated report to the emotion engine, which analyzes the user's reactions and behavior logs on the device to infer the user's emotional state.

[0492] Step 10:

[0493] An emotion engine tailors the report content based on the user's emotional state, for example providing short, concise advice if the user seems busy, or including more detailed information if the user seems interested.

[0494] Step 11:

[0495] The server then sends the final adjusted report to the user's device via a dedicated app, email, SMS, etc.

[0496] Step 12:

[0497] Users check the report on their devices. Users log in to a dedicated app on their smartphone or tablet and check the latest forecast results and advice.

[0498] Step 13:

[0499] The user can take action based on the report, for example, watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0500] This series of processing steps enables the server and emotion engine to efficiently and effectively support crop management and improve user satisfaction.

[0501] Example 2

[0502] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0503] Conventional agricultural land management systems lack accuracy due to inconsistencies in data obtained from sensors and insufficient correction of abnormal values. Furthermore, the generated forecasts and advice were not adjusted based on the user's emotional state, resulting in inappropriate notification content and reduced user convenience and satisfaction. These issues made it difficult to accurately predict crop growth conditions and implement appropriate countermeasures.

[0504] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0505] In this invention, the server includes a means for correcting the received data using a consistency and complementation algorithm, an artificial intelligence module for analyzing the data and generating predictions and advice on crop management, and a means for analyzing the user's emotions using an embedded emotion recognition engine and adjusting the notification content based on the user's emotional state. This ensures the accuracy of the data and optimizes the predictions and advice on crop management to suit the user's emotional state, improving user convenience and satisfaction and enabling more accurate predictions of crop growth conditions and the implementation of appropriate measures.

[0506] "Sensor equipment" refers to devices that measure soil conditions, moisture content, or light levels, including soil moisture sensors, temperature sensors, and light sensors.

[0507] "Database" refers to an information management system for storing received data along with its date and time. Generally, a relational database management system (RDBMS) is used.

[0508] "Server device" refers to a computer system that has the function of receiving, storing, analyzing, and notifying data sent from a sensor device.

[0509] "Artificial Intelligence Module" refers to a system that includes software and algorithms for analyzing stored data and generating predictions and advice regarding crop management.

[0510] "Emotion recognition engine" refers to a system that includes software and hardware for recognizing and analyzing a user's emotions, including facial expression analysis and voice analysis.

[0511] "Notification means" refers to a method for transmitting the generated predictions and advice from the server device to the user's device, including a dedicated application, email, SMS, etc.

[0512] "Integrity and Imputation Algorithms" means mathematical and statistical methods for verifying the integrity of received data and correcting inconsistent or missing data.

[0513] "User Device" means the electronic device used by a User to view Notifications, including a smartphone or tablet.

[0514] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device that collects and analyzes the data, and notifies the user of the obtained predictions and advice via their device. It also has the function of recognizing the user's emotions and adjusting notifications according to their state.

[0515] Configuration of the embodiment

[0516] Sensor device

[0517] Users install multiple sensor devices on farmland, including soil moisture sensors, temperature sensors, and light sensors. Each sensor device uses sensors such as Bosch's BME680 to measure soil moisture, temperature, and light intensity. The sensor devices transmit measurement data to a server device using wireless communication technologies such as LoRa and ZigBee.

[0518] Server device

[0519] The server device stores the received data in a database such as MySQL along with the date, time, and sensor ID. The program checks the data's integrity, and if outliers or missing values ​​are detected, it corrects them using imputation algorithms such as the Reuter series model and linear interpolation. The server device periodically exports the accumulated data in CSV or JSON format and provides it to an artificial intelligence module for analysis.

[0520] The AI ​​module runs models trained using popular machine learning frameworks such as TensorFlow. These models analyze past and current data to predict crop growth conditions, water needs, risk of pest infestation, etc. Based on the analysis results, specific advice is generated as a course of action (e.g., "Watering is required within the next 24 hours").

[0521] The server is equipped with an emotion recognition engine that analyzes facial expressions, voice, and touch operations when a user checks a report on their device to recognize the user's emotional state. For example, if the user is tired or stressed, the notification content can be adjusted to include a more understandable explanation or an encouraging message.

[0522] Notification methods and devices

[0523] The server device notifies the user's device of the generated predictions and advice. Notification methods include a dedicated application developed with Flutter, email, and SMS. The user can use their device to check the prediction results and advice in real time. For example, specific guidelines such as "Watering is required within 24 hours" are displayed.

[0524] Specific examples

[0525] For example, suppose a user installs multiple BME680 sensors in their field, each measuring soil moisture and temperature every three hours. The measurement data is sent to a server using a ZigBee module. The server stores the received data in a MySQL database and exports the day's data in CSV format at 6 p.m. every day, providing it to an artificial intelligence module built with TensorFlow. The AI ​​module analyzes the data from the past 30 days and the latest data to generate a prediction that "watering is required within the next 24 hours." This prediction is sent back to the server, where an emotion recognition engine analyzes the user's emotions. If the user appears busy, a short message will be displayed, concisely stating that "watering is required within the next 24 hours."

[0526] Prompt Sentence Examples

[0527] "Analyze sensor data to predict environmental changes over the next 48 hours, and generate watering needs and specific advice."

[0528] As described above, the present invention can efficiently and effectively support crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, and provides customized support based on the user's emotional state.

[0529] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0530] Step 1: Data collection

[0531] Users install sensor devices in farmland to measure soil conditions, moisture content, and light intensity. Specific examples include soil moisture sensors, temperature sensors, and light sensors.

[0532] The sensor device uses a BME680 sensor to measure soil moisture and temperature every three hours.

[0533] The sensor device transmits measurement data (e.g., moisture content, temperature, light intensity) to a server via wireless communication (e.g., LoRa or ZigBee).

[0534] Input: Measurement data from sensor devices (e.g. moisture content, temperature, light intensity)

[0535] Output: Measurement data sent to the server

[0536] Step 2: Save data

[0537] The server stores the received measurement data in a MySQL database.

[0538] The server validates the data for format and integrity, for example checking that the data is formatted correctly and that all required fields are filled in.

[0539] The server detects outliers and missing values ​​and imputes the data using a Reuter series model and linear imputation algorithms.

[0540] Input: Received measurement data

[0541] Output: A database containing the validated and imputed data.

[0542] Step 3: Data analysis

[0543] The server periodically exports the stored data in CSV or JSON format and provides it to the AI ​​module, for example, exporting the day's data at 6:00 PM every day.

[0544] The artificial intelligence module analyzes historical and current data using TensorFlow.

[0545] The AI ​​module predicts crop growth conditions, water needs, risk of pest and disease outbreaks, and more.

[0546] Input: Historical and current data exported from databases

[0547] Output: Analysis results (e.g. predicted crop condition, water requirements, pest risk)

[0548] Step 4: Generate predictions and advice

[0549] Based on the analysis results, the AI ​​module generates advice for the user as a specific course of action, such as "watering is required within the next 24 hours."

[0550] The generated advice is sent back to the server in JSON format.

[0551] Input: Analysis results

[0552] Output: Generated advice (JSON format)

[0553] Step 5: Sentiment analysis and notification adjustment

[0554] When users check their reports, they use the device's camera and microphone, which allows the collection of facial expression and voice data.

[0555] The device transmits the collected facial expression and voice data to a server.

[0556] The emotion recognition engine analyzes facial expressions and voice data on the server to recognize the user's emotional state, for example, to determine whether the user is tired.

[0557] The server optimizes the notification content based on the user's emotional state, for example, if the user seems busy, it will send a short and concise notification.

[0558] Input: Collected facial and voice data

[0559] Output: Notification content adjusted based on emotional state

[0560] Step 6: Sending notifications

[0561] The server then sends optimized notifications to the user's device via a dedicated application, email, or SMS. The dedicated application is a mobile app developed with Flutter.

[0562] The terminal displays the received notification to the user in real time.

[0563] Input: Notification content tailored based on emotional state

[0564] Output: Notification displayed on the user's device

[0565] Through these steps, the system efficiently collects, stores, and analyzes data, and provides appropriate advice based on the user's emotional state. This is expected to improve the accuracy of crop management and enhance user convenience.

[0566] (Application example 2)

[0567] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0568] Modern home and office environments demand improved safety. However, current security systems simply use security cameras and simple sensors, making it difficult to prevent or respond immediately to environmental changes or sudden accidents. Furthermore, there is no flexible notification system that takes into account the user's emotional state, so there is a need for instantaneous detection of danger and effective countermeasures.

[0569] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity, a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, an emotion engine that recognizes the user's emotional state, and a means for notifying the user's terminal of the predictions and advice generated by the server device. This makes it possible to instantly detect suspicious behavior while the user is away or dangers such as fires and water leaks, and to provide appropriate notifications according to the user's emotional state.

[0570] A "sensor device" is a device that measures environmental data such as soil condition, moisture content, and light intensity, and transmits this data wirelessly to a server.

[0571] A "database" is a storage system that stores data received from sensor devices and complements abnormal or missing values ​​as necessary.

[0572] A "server device" is a collection of hardware and software that has a database and manages a series of processes that receive and analyze data from sensor devices and notify the user's terminal.

[0573] An "artificial intelligence module" is a component that includes algorithms and models for analyzing accumulated data and generating predictions and advice.

[0574] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, touch operations, etc., and customizes the notification content based on the analysis results.

[0575] The "notification means" refers to the means for delivering the generated predictions and advice to the user's terminal, and may be a dedicated application, email, SMS, or the like.

[0576] This invention is a security system for improving indoor safety, and uses a server device equipped with a database and sensors that measure soil conditions, moisture content, and light intensity. The server device also incorporates an artificial intelligence module and an emotion engine, which work together to generate predictions and advice, which are then sent to the user's terminal.

[0577] First, the user installs multiple sensor devices in a room. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows the sensor devices to obtain environmental data in real time, independent of time and location.

[0578] The server device stores the received data in a database. The database records the data sent from each sensor device along with the date and time. The server checks the format and consistency of the data and fills in any outliers or missing values ​​as necessary. The software used in this process includes a database management system (DBMS) and a data processing tool.

[0579] The server device then periodically provides the data to an artificial intelligence module, which analyzes the accumulated data and predicts the indoor environmental conditions and potential hazards. For example, if the temperature is abnormally high, it generates a notification indicating a fire risk. It also notifies users of water leaks or dry conditions if there is an excess or shortage of moisture. These analyses are performed using machine learning algorithms and data analysis software.

[0580] Next, the server device is equipped with an emotion engine. The emotion engine analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is nervous, the notification content will be simplified to allow for a faster response. The facial recognition software and voice analysis tools used for this purpose estimate emotions by analyzing the user's facial and vocal characteristics.

[0581] The server device notifies the user device of the generated predictions and advice. This notification is done via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. This process utilizes mobile notification services and email delivery systems.

[0582] As a concrete example, consider a scenario in which a sensor device measures the temperature, moisture content, and light intensity in a room every hour and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if an abnormality is predicted for the next 24 hours, generates a notification, for example, "The temperature is too high, so there is a risk of fire." This notification is sent to the user's smartphone, and the emotion engine analyzes the user's emotional state and adjusts the content of the notification as necessary. For example, if the user appears busy, it may provide a short message and concise advice.

[0583] An example of an input prompt for the generative AI model is, "Simplify the notification messages that users receive when they are under stress. If there is a high security risk, generate a concise notification message that encourages urgent action." As this prompt shows, it is designed to be able to respond flexibly according to the user's situation.

[0584] In this way, the present invention efficiently and effectively improves indoor safety, enabling rapid risk prediction and response.The addition of an emotion engine provides customized support based on the user's emotional state, improving usability and satisfaction.

[0585] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0586] Step 1:

[0587] The sensor device measures soil conditions, moisture content, and light intensity. As input, it uses data acquired in real time from the environment where the sensor is installed. This data is concrete numerical data such as soil moisture content, temperature, and light intensity. The sensor device transmits the measurement data wirelessly to a server.

[0588] Step 2:

[0589] The server receives measurement data sent from the sensor devices and stores it in a database. As input, it receives data sent from the sensors. It checks the format and consistency of the received data and imputes any abnormal or missing values. As output, the organized data is stored in the database.

[0590] Step 3:

[0591] The server provides the data stored in the database to the artificial intelligence module. The accumulated data in the database is used as input. The artificial intelligence module analyzes the data using machine learning algorithms to predict the indoor environmental conditions and potential hazards. The output is prediction results and advice.

[0592] Step 4:

[0593] The server rechecks the predictions and advice generated by the AI ​​module and passes them to the emotion engine. It uses the analysis results from the AI ​​module as input. The emotion engine analyzes the user's emotional state (facial expression, voice, touch operations, etc.) when checking the report on their device and generates appropriate notification content. As output, it generates a notification tailored to the user's emotional state.

[0594] Step 5:

[0595] The server notifies the user's device of the generated predictions and advice. As input, it receives the adjusted notification content from the emotion engine. As output, a notification is delivered to the user's device, such as a smartphone or smart glasses. This notification is sent via a dedicated application, email, or SMS.

[0596] Step 6:

[0597] The user uses the device to check the notification and take necessary action. The notification message received from the server is used as input. Suspicious behavior during absence, fire risk, water leak risk, etc. can be checked immediately. The output is that the user takes specific action.

[0598] This series of steps allows the security system to function effectively, enabling rapid response based on environmental data and improving indoor safety. Tailoring notification content to the user's emotional state provides a more user-friendly and satisfying system.

[0599] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0600] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0601] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0602] [Third embodiment]

[0603] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0604] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0605] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0606] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0607] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0608] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0609] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0610] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0611] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0612] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0613] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0614] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0615] The present invention relates to a system including a sensor device that measures soil condition, moisture content, and light intensity, a server device having a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device. An embodiment of the present invention will be described below with specific examples.

[0616] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0617] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0618] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the crop's growth status, water requirements, risk of pests, etc. These predictions and advice are generated based on the analysis results.

[0619] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0620] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if it predicts dryness in the next 48 hours, generates a notification such as "watering is required within the next 24 hours." This notification is sent to the user's smartphone, and the user can check the notification and water the plants at the appropriate time.

[0621] In this way, the system of the present invention efficiently and effectively supports crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the problems faced by conventional agriculture.

[0622] The processing flow will be explained below.

[0623] Step 1:

[0624] The sensors measure soil moisture, temperature, and light intensity, and each sensor collects data every three hours and transmits it wirelessly to a server.

[0625] Step 2:

[0626] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, the server logs this.

[0627] Step 3:

[0628] The server stores the received data in a database. When storing data, it records the measurement date and time and the sensor ID associated with each data.

[0629] Step 4:

[0630] The server performs pre-processing of the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0631] Step 5:

[0632] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a certain period of time and prepares it for analysis.

[0633] Step 6:

[0634] The artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water needs, and pest and disease risk.

[0635] Step 7:

[0636] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0637] Step 8:

[0638] The server compiles the analysis results and advice into a report that organizes the information in an easy-to-understand format.

[0639] Step 9:

[0640] The server sends the report to the user's device via a dedicated app, email, SMS, etc.

[0641] Step 10:

[0642] Users can check the report on their device, or log in to a dedicated app on their smartphone or tablet to check the latest forecast results and advice.

[0643] Step 11:

[0644] The user can take action based on the report, such as watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0645] This series of processing steps allows users to efficiently manage crops, resulting in increased yields and improved quality.

[0646] Example 1

[0647] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0648] Conventional agricultural management systems have fragmented data collection of soil conditions and weather, making it difficult to collect continuous, real-time data and analyze it. Furthermore, filling in abnormal and missing values ​​relies on manual work, making efficient crop management difficult. These issues increase the risk of impacting yields and crop quality.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0650] In this invention, the server includes a means for the sensor device to transmit data wirelessly, a preprocessing means for checking the format and consistency of the received data and filling in outliers and missing values, and a means for analyzing the data using a generative AI model to predict the crop growth status, water requirements, and risk of pest and disease outbreaks. This enables continuous, real-time data collection and analysis, enabling efficient and effective crop management.

[0651] A "sensor device" is a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0652] "Server means" refers to means including a server for receiving, storing, and processing data transmitted from a sensor device.

[0653] A "database" is a storage system installed in a server for storing measurement data sent from sensor devices.

[0654] An "artificial intelligence module" is a software and hardware unit for analyzing stored data and generating predictions and advice regarding crop management.

[0655] The "communication means" is a means for notifying the user's terminal of the predictions and advice generated by the server means.

[0656] "Wireless communication" is a communication method for transmitting and receiving data wirelessly.

[0657] The "preprocessing means" is a means for checking the format and consistency of the data received by the server means and for complementing abnormal values ​​and missing values.

[0658] A "generative AI model" is an artificial intelligence algorithm that is trained using large amounts of data to perform a specific task (in this case, making predictions and giving advice about crop management).

[0659] "Crop management" refers to a series of activities that involve managing crop growth conditions and maintaining optimal conditions based on soil conditions and weather data.

[0660] A "prompt statement" is an instruction statement used when making an analysis request to an artificial intelligence model.

[0661] The present invention is a system for supporting efficient and effective crop management, which includes a plurality of sensor devices, a server means having a database, an artificial intelligence module, and a user terminal.

[0662] First, the user installs multiple sensor devices on farmland. These sensor devices measure the amount of moisture in the soil, temperature, and light. For example, the sensor devices measure data every three hours and transmit the data to the server means via wireless communication.

[0663] The server means stores the received data in a database (for example, a MySQL database). The stored data is recorded along with the date and time. The server means checks the format and consistency of the data, and if there are any abnormal or missing values, it complements them. An abnormal value is an inaccurate value that occurs due to a sensor malfunction or a data transmission error. A data correction algorithm based on past data is used to complement the data.

[0664] The server means then periodically provides the data to the artificial intelligence module. The artificial intelligence module analyzes the data using a generative AI model. Examples of AI frameworks used here include TensorFlow and Keras, which are used to train large amounts of data to predict the crop's growth status, water requirements, risk of pest infestation, and so on. An example prompt is, "Based on the soil moisture, temperature, and light intensity data from the past week, please predict when watering is required within the next 48 hours." Based on this prompt, the artificial intelligence module performs analysis and generates predictions and advice.

[0665] The server sends the generated predictions and advice to the user's device. Notification methods include a dedicated application, email, and SMS. The user can check the prediction results and advice in real time through their device and take appropriate action. For example, if a user receives a notification that "watering is required within the next 24 hours," they can check their smartphone and water the plants at the appropriate time.

[0666] This system allows users to monitor the condition of farmland in real time and manage crops efficiently and effectively, which is expected to increase yields and improve quality. In addition, by filling in outliers and missing values, the reliability of the data is improved, allowing for faster and more accurate responses than traditional manual management methods.

[0667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0668] Step 1:

[0669] The sensor device collects the measurement data.

[0670] Input: Field environmental conditions (e.g., soil moisture, temperature, light intensity)

[0671] Data processing: Sensor devices measure these environmental conditions and convert them into digital data.

[0672] Output: Digitized measurement data

[0673] How it works: The sensor device measures the soil moisture, temperature, and light intensity every three hours.

[0674] Step 2:

[0675] The sensor device transmits measurement data to the server means.

[0676] Input: Digitized measurement data

[0677] Data processing: The sensor device transmits data via wireless communication (e.g., Wi-Fi, LTE).

[0678] Output: Measurement data sent to the server means

[0679] Specific operation: The sensor device transmits measurement data to the server means via Wi-Fi.

[0680] Step 3:

[0681] The server means stores the received data in a database.

[0682] Input: Measurement data sent from the sensor device

[0683] Data processing: The server checks the format and integrity of the received data and stores it in the database. If necessary, it will fill in any outliers or missing values.

[0684] Output: Consistent measurement data stored in a database

[0685] Specific operation: If the server detects an abnormal value, it complements the value based on past data.

[0686] Step 4:

[0687] A server means provides data to the artificial intelligence module.

[0688] Input: Measurement data stored in a database

[0689] Data processing: Periodically extract data and provide it to the artificial intelligence module.

[0690] Output: Data provided to the artificial intelligence module

[0691] Specific operation: At the end of each day, the server means transmits the data for the day to the artificial intelligence module.

[0692] Step 5:

[0693] An artificial intelligence module analyzes the data.

[0694] Input: Measurement data provided by the server means

[0695] Data processing: Generative AI models are used to analyze data and predict crop health, water needs, risk of pests and diseases, etc.

[0696] Output: Prediction results and advice

[0697] Specific behavior: Uses AI frameworks (e.g. TensorFlow, Keras) to analyze data and generate specific advice such as "watering is needed within the next 24 hours."

[0698] Step 6:

[0699] The server means notifies the user terminal of the prediction result and advice.

[0700] Input: Predictions and advice generated by the artificial intelligence module

[0701] Data processing: The data is formatted into a notification format and sent to the user's device via the appropriate communication method (dedicated app, email, SMS, etc.).

[0702] Output: Notification sent to the user's device

[0703] Specific operation: The server means sends the generated advice to the smartphone app, and the user confirms the notification.

[0704] Step 7:

[0705] The user acts on the advice.

[0706] Input: Prediction results and advice displayed on the user's device

[0707] Specific behavior: The user checks the notification, follows the advice such as "Watering is required within the next 24 hours," and waters the plant at the appropriate time.

[0708] Through these steps, the system can monitor the environmental conditions of farmland in real time and manage crops efficiently and effectively.

[0709] (Application example 1)

[0710] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0711] Conventional autonomous vehicles lack the ability to monitor the environment in real time to respond to changes in the driving environment and propose appropriate driving conditions based on that analysis. This means that driving optimization according to road and weather conditions is not fully achieved, increasing the risk of accidents and breakdowns.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0713] In this invention, the server includes means for receiving and storing measurement data from sensor devices that measure soil conditions, moisture content, and light intensity, means for having an artificial intelligence module that analyzes the measurement data and generates predictions and advice regarding crop management and the vehicle's driving environment, and means for notifying the user's terminal of the generated predictions and advice. This enables the vehicle to monitor the driving environment, propose optimal routes in real time, and generate and provide notifications to the user according to the driving conditions.

[0714] "Soil condition" refers to the physical and chemical properties of the soil, including soil moisture, temperature, and nutrient content.

[0715] "Moisture content" is the amount of water present in a particular environment, expressed as humidity in the soil or air.

[0716] "Light intensity" refers to the intensity or amount of light in a particular location, and is usually measured using a light meter or similar device.

[0717] A "sensor device" is a device for measuring physical environmental data, and is used to obtain information such as temperature, humidity, and light intensity.

[0718] "Measurement data" refers to the numerical and digital data of environmental information acquired by sensor devices.

[0719] A "database" is a system for storing measurement data in an organized manner so that it can be easily accessed later.

[0720] A "server device" is a computer device that receives, stores, and analyzes measurement data over a network.

[0721] An "artificial intelligence module" is a software component that analyzes accumulated data and generates predictions and advice.

[0722] "User Device" means an electronic device that a User uses to receive, display, and interact with business or data, including, for example, a smartphone or tablet.

[0723] "Driving environment" is a general term for the surrounding physical conditions, weather, road conditions, etc. when an autonomous vehicle is driving.

[0724] An "optimal route" is a route or driving path that allows a user or vehicle to reach a destination most efficiently and safely.

[0725] "Notification" refers to messages and alerts that convey information such as prediction results and advice to users.

[0726] This invention is a system that includes a sensor device that measures soil conditions, moisture content, and light intensity, a server device with a database that receives and stores the measurement data, an artificial intelligence module that analyzes the data and generates predictions and advice regarding crop management and vehicle driving environments, and means for notifying a user's terminal of the generated predictions and advice.

[0727] First, the user installs multiple sensor devices on the vehicle to monitor the driving environment. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the measurement data wirelessly to a server device. This allows data on the driving environment to be obtained in real time.

[0728] The server device stores the received data in a database. The data sent from each sensor device is recorded in the database along with the date and time. The server device checks the format and consistency of the data, and complements any abnormal or missing values ​​as necessary.

[0729] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the growth conditions of the driving environment, the amount of water needed, the condition of the road surface, etc. These predictions and advice are generated based on the analysis results.

[0730] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. For example, the server device can provide specific information about slippery roads and the optimal driving route.

[0731] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares the data with past data and, if it predicts that roads will become slippery in the next 48 hours, generates a notification such as "Please reduce your speed and drive carefully within the next three hours." This notification is sent to the user's smartphone, and the user can check the notification and take appropriate action at the appropriate time.

[0732] Example prompt sentence:

[0733] Based on the humidity and temperature data of the road surface acquired by the sensor, predict future driving risks and notify the user of appropriate driving route advice. For example, if the humidity is high and the temperature is low, give advice such as "Be careful as the road surface is slippery."

[0734] In this way, the system of the present invention efficiently and effectively supports vehicle driving management, improving safety and enabling efficient route selection. Real-time data acquisition and analysis enables rapid response, solving many of the problems faced by conventional self-driving vehicles.

[0735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0736] Step 1:

[0737] The sensor device measures soil conditions, moisture content, and light intensity. The input is the local soil condition, moisture content, and light intensity, and the output is the corresponding measurement data. Specifically, each sensor collects environmental data at regular intervals and converts it into digital data.

[0738] Step 2:

[0739] Measurement data from the sensor device is sent wirelessly to the server device. The input is the measurement data from the sensor device, and the output is raw data that is stored in the server device. Specifically, the sensor device sends data to the server device using wireless communication technology such as Wi-Fi or LoRa.

[0740] Step 3:

[0741] The server device checks the format and consistency of the data it receives. The input is raw data from the sensor device, and the output is formatted and accurate data. Specifically, the server device detects outliers and missing values ​​and complements the data as necessary.

[0742] Step 4:

[0743] The server device stores the organized data in a database. The input is the formatted sensor data, and the output is the data recorded in the database. Specifically, the server device uses a database management system to store the data along with date and time information.

[0744] Step 5:

[0745] The server device periodically provides data to the AI ​​module. The input is measurement data stored in the database, and the output is analysis data passed to the AI ​​module. Specifically, the server device uses queries to extract the required data from the database and send it to the AI ​​module.

[0746] Step 6:

[0747] The artificial intelligence module analyzes the provided data and generates predictions and advice regarding the driving environment. The input is measurement data provided by the server device, and the output is prediction results and advice. Specifically, it uses a generative AI model to compare with past data and predict future driving conditions. For example, it generates a prediction that "road surfaces will become slippery in the next 48 hours."

[0748] Step 7:

[0749] The server device notifies the user's device of the generated predictions and advice. The input is the prediction result and advice from the artificial intelligence module, and the output is a notification message sent to the user's device. Specifically, the server device sends a message to the user's device using a notification service (e.g., push notification, SMS, email).

[0750] Step 8:

[0751] The user uses the device to check the prediction results and advice in real time. The input is the notification message sent from the server device, and the output is the information received by the user. Specifically, the user launches a dedicated application and checks the received notification. For example, the user receives information such as "Please reduce your speed and drive carefully within the next three hours."

[0752] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0753] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device equipped with a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device, and further combines this with an emotion engine that recognizes the user's emotions.Embodiments of the present invention will be described below with specific examples.

[0754] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0755] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0756] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data to predict the crop's growth status, water requirements, risk of pests, etc. Based on the analysis results, it also generates advice as a concrete course of action.

[0757] The server also houses an emotion engine, which analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is tired or stressed, the engine adjusts the content of the notification to make it easier to understand or to include an encouraging message.

[0758] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0759] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an AI module. The AI ​​module compares it with past data and, if dryness is predicted for the next 48 hours, generates a notification such as "Watering is required within the next 24 hours." This notification is sent to the user's smartphone, and an emotion engine analyzes the user's emotional state and adjusts the content of the notification as needed. For example, if the user appears busy, a short message and concise advice will be provided.

[0760] In this way, the system of the present invention efficiently and effectively supports crop management, resulting in increased yields and improved quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the challenges faced by traditional agriculture. The addition of an emotion engine provides customized support based on the user's emotional state, improving ease of use and satisfaction.

[0761] The processing flow will be explained below.

[0762] Step 1:

[0763] The sensors measure soil moisture, temperature, and light intensity, collecting data every three hours and transmitting it wirelessly to a server.

[0764] Step 2:

[0765] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, they are logged.

[0766] Step 3:

[0767] The server stores the received data in a database. When storing data, it associates the measurement date and time and the sensor ID with each data.

[0768] Step 4:

[0769] The server pre-processes the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0770] Step 5:

[0771] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a period of time and sends it to the artificial intelligence for analysis.

[0772] Step 6:

[0773] An artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water requirements, and pest and disease risk.

[0774] Step 7:

[0775] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0776] Step 8:

[0777] The server compiles the analysis results and advice into a report, which is presented to the user in an easy-to-understand format.

[0778] Step 9:

[0779] The server provides the generated report to the emotion engine, which analyzes the user's reactions and behavior logs on the device to infer the user's emotional state.

[0780] Step 10:

[0781] An emotion engine tailors the report content based on the user's emotional state, for example providing short, concise advice if the user seems busy, or including more detailed information if the user seems interested.

[0782] Step 11:

[0783] The server then sends the final adjusted report to the user's device via a dedicated app, email, SMS, etc.

[0784] Step 12:

[0785] Users check the report on their devices. Users log in to a dedicated app on their smartphone or tablet and check the latest forecast results and advice.

[0786] Step 13:

[0787] The user can take action based on the report, for example, watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0788] This series of processing steps enables the server and emotion engine to efficiently and effectively support crop management and improve user satisfaction.

[0789] Example 2

[0790] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0791] Conventional agricultural land management systems lack accuracy due to inconsistencies in data obtained from sensors and insufficient correction of abnormal values. Furthermore, the generated forecasts and advice were not adjusted based on the user's emotional state, resulting in inappropriate notification content and reduced user convenience and satisfaction. These issues made it difficult to accurately predict crop growth conditions and implement appropriate countermeasures.

[0792] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0793] In this invention, the server includes a means for correcting the received data using a consistency and complementation algorithm, an artificial intelligence module for analyzing the data and generating predictions and advice on crop management, and a means for analyzing the user's emotions using an embedded emotion recognition engine and adjusting the notification content based on the user's emotional state. This ensures the accuracy of the data and optimizes the predictions and advice on crop management to suit the user's emotional state, improving user convenience and satisfaction and enabling more accurate predictions of crop growth conditions and the implementation of appropriate measures.

[0794] "Sensor equipment" refers to devices that measure soil conditions, moisture content, or light levels, including soil moisture sensors, temperature sensors, and light sensors.

[0795] "Database" refers to an information management system for storing received data along with its date and time. Generally, a relational database management system (RDBMS) is used.

[0796] "Server device" refers to a computer system that has the function of receiving, storing, analyzing, and notifying data sent from a sensor device.

[0797] "Artificial Intelligence Module" refers to a system that includes software and algorithms for analyzing stored data and generating predictions and advice regarding crop management.

[0798] "Emotion recognition engine" refers to a system that includes software and hardware for recognizing and analyzing a user's emotions, including facial expression analysis and voice analysis.

[0799] "Notification means" refers to a method for transmitting the generated predictions and advice from the server device to the user's device, including a dedicated application, email, SMS, etc.

[0800] "Integrity and Imputation Algorithms" means mathematical and statistical methods for verifying the integrity of received data and correcting inconsistent or missing data.

[0801] "User Device" means the electronic device used by a User to view Notifications, including a smartphone or tablet.

[0802] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device that collects and analyzes the data, and notifies the user of the obtained predictions and advice via their device. It also has the function of recognizing the user's emotions and adjusting notifications according to their state.

[0803] Configuration of the embodiment

[0804] Sensor device

[0805] Users install multiple sensor devices on farmland, including soil moisture sensors, temperature sensors, and light sensors. Each sensor device uses sensors such as Bosch's BME680 to measure soil moisture, temperature, and light intensity. The sensor devices transmit measurement data to a server device using wireless communication technologies such as LoRa and ZigBee.

[0806] Server device

[0807] The server device stores the received data in a database such as MySQL along with the date, time, and sensor ID. The program checks the data's integrity, and if outliers or missing values ​​are detected, it corrects them using imputation algorithms such as the Reuter series model and linear interpolation. The server device periodically exports the accumulated data in CSV or JSON format and provides it to an artificial intelligence module for analysis.

[0808] The AI ​​module runs models trained using popular machine learning frameworks such as TensorFlow. These models analyze past and current data to predict crop growth conditions, water needs, risk of pest infestation, etc. Based on the analysis results, specific advice is generated as a course of action (e.g., "Watering is required within the next 24 hours").

[0809] The server is equipped with an emotion recognition engine that analyzes facial expressions, voice, and touch operations when a user checks a report on their device to recognize the user's emotional state. For example, if the user is tired or stressed, the notification content can be adjusted to include a more understandable explanation or an encouraging message.

[0810] Notification methods and devices

[0811] The server device notifies the user's device of the generated predictions and advice. Notification methods include a dedicated application developed with Flutter, email, and SMS. The user can use their device to check the prediction results and advice in real time. For example, specific guidelines such as "Watering is required within 24 hours" are displayed.

[0812] Specific examples

[0813] For example, suppose a user installs multiple BME680 sensors in their field, each measuring soil moisture and temperature every three hours. The measurement data is sent to a server using a ZigBee module. The server stores the received data in a MySQL database and exports the day's data in CSV format at 6 p.m. every day, providing it to an artificial intelligence module built with TensorFlow. The AI ​​module analyzes the data from the past 30 days and the latest data to generate a prediction that "watering is required within the next 24 hours." This prediction is sent back to the server, where an emotion recognition engine analyzes the user's emotions. If the user appears busy, a short message will be displayed, concisely stating that "watering is required within the next 24 hours."

[0814] Prompt Sentence Examples

[0815] "Analyze sensor data to predict environmental changes over the next 48 hours, and generate watering needs and specific advice."

[0816] As described above, the present invention can efficiently and effectively support crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, and provides customized support based on the user's emotional state.

[0817] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0818] Step 1: Data collection

[0819] Users install sensor devices in farmland to measure soil conditions, moisture content, and light intensity. Specific examples include soil moisture sensors, temperature sensors, and light sensors.

[0820] The sensor device uses a BME680 sensor to measure soil moisture and temperature every three hours.

[0821] The sensor device transmits measurement data (e.g., moisture content, temperature, light intensity) to a server via wireless communication (e.g., LoRa or ZigBee).

[0822] Input: Measurement data from sensor devices (e.g. moisture content, temperature, light intensity)

[0823] Output: Measurement data sent to the server

[0824] Step 2: Save data

[0825] The server stores the received measurement data in a MySQL database.

[0826] The server validates the data for format and integrity, for example checking that the data is formatted correctly and that all required fields are filled in.

[0827] The server detects outliers and missing values ​​and imputes the data using a Reuter series model and linear imputation algorithms.

[0828] Input: Received measurement data

[0829] Output: A database containing the validated and imputed data.

[0830] Step 3: Data analysis

[0831] The server periodically exports the stored data in CSV or JSON format and provides it to the AI ​​module, for example, exporting the day's data at 6:00 PM every day.

[0832] The artificial intelligence module analyzes historical and current data using TensorFlow.

[0833] The AI ​​module predicts crop growth conditions, water needs, risk of pest and disease outbreaks, and more.

[0834] Input: Historical and current data exported from databases

[0835] Output: Analysis results (e.g. predicted crop condition, water requirements, pest risk)

[0836] Step 4: Generate predictions and advice

[0837] Based on the analysis results, the AI ​​module generates advice for the user as a specific course of action, such as "watering is required within the next 24 hours."

[0838] The generated advice is sent back to the server in JSON format.

[0839] Input: Analysis results

[0840] Output: Generated advice (JSON format)

[0841] Step 5: Sentiment analysis and notification adjustment

[0842] When users check their reports, they use the device's camera and microphone, which allows the collection of facial expression and voice data.

[0843] The device transmits the collected facial expression and voice data to a server.

[0844] The emotion recognition engine analyzes facial expressions and voice data on the server to recognize the user's emotional state, for example, to determine whether the user is tired.

[0845] The server optimizes the notification content based on the user's emotional state, for example, if the user seems busy, it will send a short and concise notification.

[0846] Input: Collected facial and voice data

[0847] Output: Notification content adjusted based on emotional state

[0848] Step 6: Sending notifications

[0849] The server then sends optimized notifications to the user's device via a dedicated application, email, or SMS. The dedicated application is a mobile app developed with Flutter.

[0850] The terminal displays the received notification to the user in real time.

[0851] Input: Notification content tailored based on emotional state

[0852] Output: Notification displayed on the user's device

[0853] Through these steps, the system efficiently collects, stores, and analyzes data, and provides appropriate advice based on the user's emotional state. This is expected to improve the accuracy of crop management and enhance user convenience.

[0854] (Application example 2)

[0855] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0856] Modern home and office environments demand improved safety. However, current security systems simply use security cameras and simple sensors, making it difficult to prevent or respond immediately to environmental changes or sudden accidents. Furthermore, there is no flexible notification system that takes into account the user's emotional state, so there is a need for instantaneous detection of danger and effective countermeasures.

[0857] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity, a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, an emotion engine that recognizes the user's emotional state, and a means for notifying the user's terminal of the predictions and advice generated by the server device. This makes it possible to instantly detect suspicious behavior while the user is away or dangers such as fires and water leaks, and to provide appropriate notifications according to the user's emotional state.

[0858] A "sensor device" is a device that measures environmental data such as soil condition, moisture content, and light intensity, and transmits this data wirelessly to a server.

[0859] A "database" is a storage system that stores data received from sensor devices and complements abnormal or missing values ​​as necessary.

[0860] A "server device" is a collection of hardware and software that has a database and manages a series of processes that receive and analyze data from sensor devices and notify the user's terminal.

[0861] An "artificial intelligence module" is a component that includes algorithms and models for analyzing accumulated data and generating predictions and advice.

[0862] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, touch operations, etc., and customizes the notification content based on the analysis results.

[0863] The "notification means" refers to the means for delivering the generated predictions and advice to the user's terminal, and may be a dedicated application, email, SMS, or the like.

[0864] This invention is a security system for improving indoor safety, and uses a server device equipped with a database and sensors that measure soil conditions, moisture content, and light intensity. The server device also incorporates an artificial intelligence module and an emotion engine, which work together to generate predictions and advice, which are then sent to the user's terminal.

[0865] First, the user installs multiple sensor devices in a room. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows the sensor devices to obtain environmental data in real time, independent of time and location.

[0866] The server device stores the received data in a database. The database records the data sent from each sensor device along with the date and time. The server checks the format and consistency of the data and fills in any outliers or missing values ​​as necessary. The software used in this process includes a database management system (DBMS) and a data processing tool.

[0867] The server device then periodically provides the data to an artificial intelligence module, which analyzes the accumulated data and predicts the indoor environmental conditions and potential hazards. For example, if the temperature is abnormally high, it generates a notification indicating a fire risk. It also notifies users of water leaks or dry conditions if there is an excess or shortage of moisture. These analyses are performed using machine learning algorithms and data analysis software.

[0868] Next, the server device is equipped with an emotion engine. The emotion engine analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is nervous, the notification content will be simplified to allow for a faster response. The facial recognition software and voice analysis tools used for this purpose estimate emotions by analyzing the user's facial and vocal characteristics.

[0869] The server device notifies the user device of the generated predictions and advice. This notification is done via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. This process utilizes mobile notification services and email delivery systems.

[0870] As a concrete example, consider a scenario in which a sensor device measures the temperature, moisture content, and light intensity in a room every hour and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if an abnormality is predicted for the next 24 hours, generates a notification, for example, "The temperature is too high, so there is a risk of fire." This notification is sent to the user's smartphone, and the emotion engine analyzes the user's emotional state and adjusts the content of the notification as necessary. For example, if the user appears busy, it may provide a short message and concise advice.

[0871] An example of an input prompt for the generative AI model is, "Simplify the notification messages that users receive when they are under stress. If there is a high security risk, generate a concise notification message that encourages urgent action." As this prompt shows, it is designed to be able to respond flexibly according to the user's situation.

[0872] In this way, the present invention efficiently and effectively improves indoor safety, enabling rapid risk prediction and response.The addition of an emotion engine provides customized support based on the user's emotional state, improving usability and satisfaction.

[0873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0874] Step 1:

[0875] The sensor device measures soil conditions, moisture content, and light intensity. As input, it uses data acquired in real time from the environment where the sensor is installed. This data is concrete numerical data such as soil moisture content, temperature, and light intensity. The sensor device transmits the measurement data wirelessly to a server.

[0876] Step 2:

[0877] The server receives measurement data sent from the sensor devices and stores it in a database. As input, it receives data sent from the sensors. It checks the format and consistency of the received data and imputes any abnormal or missing values. As output, the organized data is stored in the database.

[0878] Step 3:

[0879] The server provides the data stored in the database to the artificial intelligence module. The accumulated data in the database is used as input. The artificial intelligence module analyzes the data using machine learning algorithms to predict the indoor environmental conditions and potential hazards. The output is prediction results and advice.

[0880] Step 4:

[0881] The server rechecks the predictions and advice generated by the AI ​​module and passes them to the emotion engine. It uses the analysis results from the AI ​​module as input. The emotion engine analyzes the user's emotional state (facial expression, voice, touch operations, etc.) when checking the report on their device and generates appropriate notification content. As output, it generates a notification tailored to the user's emotional state.

[0882] Step 5:

[0883] The server notifies the user's device of the generated predictions and advice. As input, it receives the adjusted notification content from the emotion engine. As output, a notification is delivered to the user's device, such as a smartphone or smart glasses. This notification is sent via a dedicated application, email, or SMS.

[0884] Step 6:

[0885] The user uses the device to check the notification and take necessary action. The notification message received from the server is used as input. Suspicious behavior during absence, fire risk, water leak risk, etc. can be checked immediately. The output is that the user takes specific action.

[0886] This series of steps allows the security system to function effectively, enabling rapid response based on environmental data and improving indoor safety. Tailoring notification content to the user's emotional state provides a more user-friendly and satisfying system.

[0887] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0888] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0889] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0890] [Fourth embodiment]

[0891] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0892] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0893] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0894] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0895] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0896] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0897] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0898] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0899] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0900] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0901] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0902] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0903] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0904] The present invention relates to a system including a sensor device that measures soil condition, moisture content, and light intensity, a server device having a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device. An embodiment of the present invention will be described below with specific examples.

[0905] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[0906] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[0907] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the crop's growth status, water requirements, risk of pests, etc. These predictions and advice are generated based on the analysis results.

[0908] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[0909] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if it predicts dryness in the next 48 hours, generates a notification such as "watering is required within the next 24 hours." This notification is sent to the user's smartphone, and the user can check the notification and water the plants at the appropriate time.

[0910] In this way, the system of the present invention efficiently and effectively supports crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the problems faced by conventional agriculture.

[0911] The processing flow will be explained below.

[0912] Step 1:

[0913] The sensors measure soil moisture, temperature, and light intensity, and each sensor collects data every three hours and transmits it wirelessly to a server.

[0914] Step 2:

[0915] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, the server logs this.

[0916] Step 3:

[0917] The server stores the received data in a database. When storing data, it records the measurement date and time and the sensor ID associated with each data.

[0918] Step 4:

[0919] The server performs pre-processing of the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[0920] Step 5:

[0921] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a certain period of time and prepares it for analysis.

[0922] Step 6:

[0923] The artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water needs, and pest and disease risk.

[0924] Step 7:

[0925] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[0926] Step 8:

[0927] The server compiles the analysis results and advice into a report that organizes the information in an easy-to-understand format.

[0928] Step 9:

[0929] The server sends the report to the user's device via a dedicated app, email, SMS, etc.

[0930] Step 10:

[0931] Users can check the report on their device, or log in to a dedicated app on their smartphone or tablet to check the latest forecast results and advice.

[0932] Step 11:

[0933] The user can take action based on the report, such as watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[0934] This series of processing steps allows users to efficiently manage crops, resulting in increased yields and improved quality.

[0935] Example 1

[0936] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0937] Conventional agricultural management systems have fragmented data collection of soil conditions and weather, making it difficult to collect continuous, real-time data and analyze it. Furthermore, filling in abnormal and missing values ​​relies on manual work, making efficient crop management difficult. These issues increase the risk of impacting yields and crop quality.

[0938] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0939] In this invention, the server includes a means for the sensor device to transmit data wirelessly, a preprocessing means for checking the format and consistency of the received data and filling in outliers and missing values, and a means for analyzing the data using a generative AI model to predict the crop growth status, water requirements, and risk of pest and disease outbreaks. This enables continuous, real-time data collection and analysis, enabling efficient and effective crop management.

[0940] A "sensor device" is a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0941] "Server means" refers to means including a server for receiving, storing, and processing data transmitted from a sensor device.

[0942] A "database" is a storage system installed in a server for storing measurement data sent from sensor devices.

[0943] An "artificial intelligence module" is a software and hardware unit for analyzing stored data and generating predictions and advice regarding crop management.

[0944] The "communication means" is a means for notifying the user's terminal of the predictions and advice generated by the server means.

[0945] "Wireless communication" is a communication method for transmitting and receiving data wirelessly.

[0946] The "preprocessing means" is a means for checking the format and consistency of the data received by the server means and for complementing abnormal values ​​and missing values.

[0947] A "generative AI model" is an artificial intelligence algorithm that is trained using large amounts of data to perform a specific task (in this case, making predictions and giving advice about crop management).

[0948] "Crop management" refers to a series of activities that involve managing crop growth conditions and maintaining optimal conditions based on soil conditions and weather data.

[0949] A "prompt statement" is an instruction statement used when making an analysis request to an artificial intelligence model.

[0950] The present invention is a system for supporting efficient and effective crop management, which includes a plurality of sensor devices, a server means having a database, an artificial intelligence module, and a user terminal.

[0951] First, the user installs multiple sensor devices on farmland. These sensor devices measure the amount of moisture in the soil, temperature, and light. For example, the sensor devices measure data every three hours and transmit the data to the server means via wireless communication.

[0952] The server means stores the received data in a database (for example, a MySQL database). The stored data is recorded along with the date and time. The server means checks the format and consistency of the data, and if there are any abnormal or missing values, it complements them. An abnormal value is an inaccurate value that occurs due to a sensor malfunction or a data transmission error. A data correction algorithm based on past data is used to complement the data.

[0953] The server means then periodically provides the data to the artificial intelligence module. The artificial intelligence module analyzes the data using a generative AI model. Examples of AI frameworks used here include TensorFlow and Keras, which are used to train large amounts of data to predict the crop's growth status, water requirements, risk of pest infestation, and so on. An example prompt is, "Based on the soil moisture, temperature, and light intensity data from the past week, please predict when watering is required within the next 48 hours." Based on this prompt, the artificial intelligence module performs analysis and generates predictions and advice.

[0954] The server sends the generated predictions and advice to the user's device. Notification methods include a dedicated application, email, and SMS. The user can check the prediction results and advice in real time through their device and take appropriate action. For example, if a user receives a notification that "watering is required within the next 24 hours," they can check their smartphone and water the plants at the appropriate time.

[0955] This system allows users to monitor the condition of farmland in real time and manage crops efficiently and effectively, which is expected to increase yields and improve quality. In addition, by filling in outliers and missing values, the reliability of the data is improved, allowing for faster and more accurate responses than traditional manual management methods.

[0956] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] The sensor device collects the measurement data.

[0959] Input: Field environmental conditions (e.g., soil moisture, temperature, light intensity)

[0960] Data processing: Sensor devices measure these environmental conditions and convert them into digital data.

[0961] Output: Digitized measurement data

[0962] How it works: The sensor device measures the soil moisture, temperature, and light intensity every three hours.

[0963] Step 2:

[0964] The sensor device transmits measurement data to the server means.

[0965] Input: Digitized measurement data

[0966] Data processing: The sensor device transmits data via wireless communication (e.g., Wi-Fi, LTE).

[0967] Output: Measurement data sent to the server means

[0968] Specific operation: The sensor device transmits measurement data to the server means via Wi-Fi.

[0969] Step 3:

[0970] The server means stores the received data in a database.

[0971] Input: Measurement data sent from the sensor device

[0972] Data processing: The server checks the format and integrity of the received data and stores it in the database. If necessary, it will fill in any outliers or missing values.

[0973] Output: Consistent measurement data stored in a database

[0974] Specific operation: If the server detects an abnormal value, it complements the value based on past data.

[0975] Step 4:

[0976] A server means provides data to the artificial intelligence module.

[0977] Input: Measurement data stored in a database

[0978] Data processing: Periodically extract data and provide it to the artificial intelligence module.

[0979] Output: Data provided to the artificial intelligence module

[0980] Specific operation: At the end of each day, the server means transmits the data for the day to the artificial intelligence module.

[0981] Step 5:

[0982] An artificial intelligence module analyzes the data.

[0983] Input: Measurement data provided by the server means

[0984] Data processing: Generative AI models are used to analyze data and predict crop health, water needs, risk of pests and diseases, etc.

[0985] Output: Prediction results and advice

[0986] Specific behavior: Uses AI frameworks (e.g. TensorFlow, Keras) to analyze data and generate specific advice such as "watering is needed within the next 24 hours."

[0987] Step 6:

[0988] The server means notifies the user terminal of the prediction result and advice.

[0989] Input: Predictions and advice generated by the artificial intelligence module

[0990] Data processing: The data is formatted into a notification format and sent to the user's device via the appropriate communication method (dedicated app, email, SMS, etc.).

[0991] Output: Notification sent to the user's device

[0992] Specific operation: The server means sends the generated advice to the smartphone app, and the user confirms the notification.

[0993] Step 7:

[0994] The user acts on the advice.

[0995] Input: Prediction results and advice displayed on the user's device

[0996] Specific behavior: The user checks the notification, follows the advice such as "Watering is required within the next 24 hours," and waters the plant at the appropriate time.

[0997] Through these steps, the system can monitor the environmental conditions of farmland in real time and manage crops efficiently and effectively.

[0998] (Application example 1)

[0999] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1000] Conventional autonomous vehicles lack the ability to monitor the environment in real time to respond to changes in the driving environment and propose appropriate driving conditions based on that analysis. This means that driving optimization according to road and weather conditions is not fully achieved, increasing the risk of accidents and breakdowns.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1002] In this invention, the server includes means for receiving and storing measurement data from sensor devices that measure soil conditions, moisture content, and light intensity, means for having an artificial intelligence module that analyzes the measurement data and generates predictions and advice regarding crop management and the vehicle's driving environment, and means for notifying the user's terminal of the generated predictions and advice. This enables the vehicle to monitor the driving environment, propose optimal routes in real time, and generate and provide notifications to the user according to the driving conditions.

[1003] "Soil condition" refers to the physical and chemical properties of the soil, including soil moisture, temperature, and nutrient content.

[1004] "Moisture content" is the amount of water present in a particular environment, expressed as humidity in the soil or air.

[1005] "Light intensity" refers to the intensity or amount of light in a particular location, and is usually measured using a light meter or similar device.

[1006] A "sensor device" is a device for measuring physical environmental data, and is used to obtain information such as temperature, humidity, and light intensity.

[1007] "Measurement data" refers to the numerical and digital data of environmental information acquired by sensor devices.

[1008] A "database" is a system for storing measurement data in an organized manner so that it can be easily accessed later.

[1009] A "server device" is a computer device that receives, stores, and analyzes measurement data over a network.

[1010] An "artificial intelligence module" is a software component that analyzes accumulated data and generates predictions and advice.

[1011] "User Device" means an electronic device that a User uses to receive, display, and interact with business or data, including, for example, a smartphone or tablet.

[1012] "Driving environment" is a general term for the surrounding physical conditions, weather, road conditions, etc. when an autonomous vehicle is driving.

[1013] An "optimal route" is a route or driving path that allows a user or vehicle to reach a destination most efficiently and safely.

[1014] "Notification" refers to messages and alerts that convey information such as prediction results and advice to users.

[1015] This invention is a system that includes a sensor device that measures soil conditions, moisture content, and light intensity, a server device with a database that receives and stores the measurement data, an artificial intelligence module that analyzes the data and generates predictions and advice regarding crop management and vehicle driving environments, and means for notifying a user's terminal of the generated predictions and advice.

[1016] First, the user installs multiple sensor devices on the vehicle to monitor the driving environment. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the measurement data wirelessly to a server device. This allows data on the driving environment to be obtained in real time.

[1017] The server device stores the received data in a database. The data sent from each sensor device is recorded in the database along with the date and time. The server device checks the format and consistency of the data, and complements any abnormal or missing values ​​as necessary.

[1018] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data and predicts the growth conditions of the driving environment, the amount of water needed, the condition of the road surface, etc. These predictions and advice are generated based on the analysis results.

[1019] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. For example, the server device can provide specific information about slippery roads and the optimal driving route.

[1020] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares the data with past data and, if it predicts that roads will become slippery in the next 48 hours, generates a notification such as "Please reduce your speed and drive carefully within the next three hours." This notification is sent to the user's smartphone, and the user can check the notification and take appropriate action at the appropriate time.

[1021] Example prompt sentence:

[1022] Based on the humidity and temperature data of the road surface acquired by the sensor, predict future driving risks and notify the user of appropriate driving route advice. For example, if the humidity is high and the temperature is low, give advice such as "Be careful as the road surface is slippery."

[1023] In this way, the system of the present invention efficiently and effectively supports vehicle driving management, improving safety and enabling efficient route selection. Real-time data acquisition and analysis enables rapid response, solving many of the problems faced by conventional self-driving vehicles.

[1024] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1025] Step 1:

[1026] The sensor device measures soil conditions, moisture content, and light intensity. The input is the local soil condition, moisture content, and light intensity, and the output is the corresponding measurement data. Specifically, each sensor collects environmental data at regular intervals and converts it into digital data.

[1027] Step 2:

[1028] Measurement data from the sensor device is sent wirelessly to the server device. The input is the measurement data from the sensor device, and the output is raw data that is stored in the server device. Specifically, the sensor device sends data to the server device using wireless communication technology such as Wi-Fi or LoRa.

[1029] Step 3:

[1030] The server device checks the format and consistency of the data it receives. The input is raw data from the sensor device, and the output is formatted and accurate data. Specifically, the server device detects outliers and missing values ​​and complements the data as necessary.

[1031] Step 4:

[1032] The server device stores the organized data in a database. The input is the formatted sensor data, and the output is the data recorded in the database. Specifically, the server device uses a database management system to store the data along with date and time information.

[1033] Step 5:

[1034] The server device periodically provides data to the AI ​​module. The input is measurement data stored in the database, and the output is analysis data passed to the AI ​​module. Specifically, the server device uses queries to extract the required data from the database and send it to the AI ​​module.

[1035] Step 6:

[1036] The artificial intelligence module analyzes the provided data and generates predictions and advice regarding the driving environment. The input is measurement data provided by the server device, and the output is prediction results and advice. Specifically, it uses a generative AI model to compare with past data and predict future driving conditions. For example, it generates a prediction that "road surfaces will become slippery in the next 48 hours."

[1037] Step 7:

[1038] The server device notifies the user's device of the generated predictions and advice. The input is the prediction result and advice from the artificial intelligence module, and the output is a notification message sent to the user's device. Specifically, the server device sends a message to the user's device using a notification service (e.g., push notification, SMS, email).

[1039] Step 8:

[1040] The user uses the device to check the prediction results and advice in real time. The input is the notification message sent from the server device, and the output is the information received by the user. Specifically, the user launches a dedicated application and checks the received notification. For example, the user receives information such as "Please reduce your speed and drive carefully within the next three hours."

[1041] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1042] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device equipped with a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, and means for notifying a user's terminal of the predictions and advice generated by the server device, and further combines this with an emotion engine that recognizes the user's emotions.Embodiments of the present invention will be described below with specific examples.

[1043] First, the user installs multiple sensor devices on farmland. Each sensor device measures the soil moisture, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows environmental data to be obtained in real time.

[1044] The server then stores the received data in a database, which records the data sent from each sensor along with the date and time. The server checks the format and consistency of the data, and fills in any outliers or missing values ​​as necessary.

[1045] Furthermore, the server device periodically provides data to an artificial intelligence module, which analyzes the accumulated data to predict the crop's growth status, water requirements, risk of pests, etc. Based on the analysis results, it also generates advice as a concrete course of action.

[1046] The server also houses an emotion engine, which analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is tired or stressed, the engine adjusts the content of the notification to make it easier to understand or to include an encouraging message.

[1047] The server device notifies the user of the generated predictions and advice via a dedicated application, email, SMS, etc. The user can then use their device to check the prediction results and advice in real time. For example, specific information such as when watering is necessary and preventative measures against pests and diseases is provided.

[1048] As a concrete example, consider a scenario in which a sensor device measures soil moisture and temperature every three hours and sends the data to a server device. The server device stores the received data and provides it to an AI module. The AI ​​module compares it with past data and, if dryness is predicted for the next 48 hours, generates a notification such as "Watering is required within the next 24 hours." This notification is sent to the user's smartphone, and an emotion engine analyzes the user's emotional state and adjusts the content of the notification as needed. For example, if the user appears busy, a short message and concise advice will be provided.

[1049] In this way, the system of the present invention efficiently and effectively supports crop management, resulting in increased yields and improved quality. It also enables rapid response through real-time data acquisition and analysis, solving many of the challenges faced by traditional agriculture. The addition of an emotion engine provides customized support based on the user's emotional state, improving ease of use and satisfaction.

[1050] The processing flow will be explained below.

[1051] Step 1:

[1052] The sensors measure soil moisture, temperature, and light intensity, collecting data every three hours and transmitting it wirelessly to a server.

[1053] Step 2:

[1054] The server receives the data received from the sensors, checks the format and integrity of the data, and performs a validation process. If there are any outliers or missing values, they are logged.

[1055] Step 3:

[1056] The server stores the received data in a database. When storing data, it associates the measurement date and time and the sensor ID with each data.

[1057] Step 4:

[1058] The server pre-processes the stored data, which includes filtering outliers, imputing missing values, and aggregating the data over time (e.g., averaging over an hour).

[1059] Step 5:

[1060] The server provides the pre-processed data to the artificial intelligence module, which then constructs a data set for a period of time and sends it to the artificial intelligence for analysis.

[1061] Step 6:

[1062] An artificial intelligence module analyzes the provided data using machine learning algorithms and statistical methods to predict crop health, water requirements, and pest and disease risk.

[1063] Step 7:

[1064] The AI ​​module sends the analysis results back to the server, which include specific advice and recommended actions (e.g., watering schedules and pest control measures).

[1065] Step 8:

[1066] The server compiles the analysis results and advice into a report, which is presented to the user in an easy-to-understand format.

[1067] Step 9:

[1068] The server provides the generated report to the emotion engine, which analyzes the user's reactions and behavior logs on the device to infer the user's emotional state.

[1069] Step 10:

[1070] An emotion engine tailors the report content based on the user's emotional state, for example providing short, concise advice if the user seems busy, or including more detailed information if the user seems interested.

[1071] Step 11:

[1072] The server then sends the final adjusted report to the user's device via a dedicated app, email, SMS, etc.

[1073] Step 12:

[1074] Users check the report on their devices. Users log in to a dedicated app on their smartphone or tablet and check the latest forecast results and advice.

[1075] Step 13:

[1076] The user can take action based on the report, for example, watering if necessary, or implementing appropriate pest control measures if pests or diseases are predicted.

[1077] This series of processing steps enables the server and emotion engine to efficiently and effectively support crop management and improve user satisfaction.

[1078] Example 2

[1079] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1080] Conventional agricultural land management systems lack accuracy due to inconsistencies in data obtained from sensors and insufficient correction of abnormal values. Furthermore, the generated forecasts and advice were not adjusted based on the user's emotional state, resulting in inappropriate notification content and reduced user convenience and satisfaction. These issues made it difficult to accurately predict crop growth conditions and implement appropriate countermeasures.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1082] In this invention, the server includes a means for correcting the received data using a consistency and complementation algorithm, an artificial intelligence module for analyzing the data and generating predictions and advice on crop management, and a means for analyzing the user's emotions using an embedded emotion recognition engine and adjusting the notification content based on the user's emotional state. This ensures the accuracy of the data and optimizes the predictions and advice on crop management to suit the user's emotional state, improving user convenience and satisfaction and enabling more accurate predictions of crop growth conditions and the implementation of appropriate measures.

[1083] "Sensor equipment" refers to devices that measure soil conditions, moisture content, or light levels, including soil moisture sensors, temperature sensors, and light sensors.

[1084] "Database" refers to an information management system for storing received data along with its date and time. Generally, a relational database management system (RDBMS) is used.

[1085] "Server device" refers to a computer system that has the function of receiving, storing, analyzing, and notifying data sent from a sensor device.

[1086] "Artificial Intelligence Module" refers to a system that includes software and algorithms for analyzing stored data and generating predictions and advice regarding crop management.

[1087] "Emotion recognition engine" refers to a system that includes software and hardware for recognizing and analyzing a user's emotions, including facial expression analysis and voice analysis.

[1088] "Notification means" refers to a method for transmitting the generated predictions and advice from the server device to the user's device, including a dedicated application, email, SMS, etc.

[1089] "Integrity and Imputation Algorithms" means mathematical and statistical methods for verifying the integrity of received data and correcting inconsistent or missing data.

[1090] "User Device" means the electronic device used by a User to view Notifications, including a smartphone or tablet.

[1091] The present invention is a system that includes a sensor device that measures soil condition, moisture content, and light intensity, a server device that collects and analyzes the data, and notifies the user of the obtained predictions and advice via their device. It also has the function of recognizing the user's emotions and adjusting notifications according to their state.

[1092] Configuration of the embodiment

[1093] Sensor device

[1094] Users install multiple sensor devices on farmland, including soil moisture sensors, temperature sensors, and light sensors. Each sensor device uses sensors such as Bosch's BME680 to measure soil moisture, temperature, and light intensity. The sensor devices transmit measurement data to a server device using wireless communication technologies such as LoRa and ZigBee.

[1095] Server device

[1096] The server device stores the received data in a database such as MySQL along with the date, time, and sensor ID. The program checks the data's integrity, and if outliers or missing values ​​are detected, it corrects them using imputation algorithms such as the Reuter series model and linear interpolation. The server device periodically exports the accumulated data in CSV or JSON format and provides it to an artificial intelligence module for analysis.

[1097] The AI ​​module runs models trained using popular machine learning frameworks such as TensorFlow. These models analyze past and current data to predict crop growth conditions, water needs, risk of pest infestation, etc. Based on the analysis results, specific advice is generated as a course of action (e.g., "Watering is required within the next 24 hours").

[1098] The server is equipped with an emotion recognition engine that analyzes facial expressions, voice, and touch operations when a user checks a report on their device to recognize the user's emotional state. For example, if the user is tired or stressed, the notification content can be adjusted to include a more understandable explanation or an encouraging message.

[1099] Notification methods and devices

[1100] The server device notifies the user's device of the generated predictions and advice. Notification methods include a dedicated application developed with Flutter, email, and SMS. The user can use their device to check the prediction results and advice in real time. For example, specific guidelines such as "Watering is required within 24 hours" are displayed.

[1101] Specific examples

[1102] For example, suppose a user installs multiple BME680 sensors in their field, each measuring soil moisture and temperature every three hours. The measurement data is sent to a server using a ZigBee module. The server stores the received data in a MySQL database and exports the day's data in CSV format at 6 p.m. every day, providing it to an artificial intelligence module built with TensorFlow. The AI ​​module analyzes the data from the past 30 days and the latest data to generate a prediction that "watering is required within the next 24 hours." This prediction is sent back to the server, where an emotion recognition engine analyzes the user's emotions. If the user appears busy, a short message will be displayed, concisely stating that "watering is required within the next 24 hours."

[1103] Prompt Sentence Examples

[1104] "Analyze sensor data to predict environmental changes over the next 48 hours, and generate watering needs and specific advice."

[1105] As described above, the present invention can efficiently and effectively support crop management, increasing yields and improving quality. It also enables rapid response through real-time data acquisition and analysis, and provides customized support based on the user's emotional state.

[1106] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1107] Step 1: Data collection

[1108] Users install sensor devices in farmland to measure soil conditions, moisture content, and light intensity. Specific examples include soil moisture sensors, temperature sensors, and light sensors.

[1109] The sensor device uses a BME680 sensor to measure soil moisture and temperature every three hours.

[1110] The sensor device transmits measurement data (e.g., moisture content, temperature, light intensity) to a server via wireless communication (e.g., LoRa or ZigBee).

[1111] Input: Measurement data from sensor devices (e.g. moisture content, temperature, light intensity)

[1112] Output: Measurement data sent to the server

[1113] Step 2: Save data

[1114] The server stores the received measurement data in a MySQL database.

[1115] The server validates the data for format and integrity, for example checking that the data is formatted correctly and that all required fields are filled in.

[1116] The server detects outliers and missing values ​​and imputes the data using a Reuter series model and linear imputation algorithms.

[1117] Input: Received measurement data

[1118] Output: A database containing the validated and imputed data.

[1119] Step 3: Data analysis

[1120] The server periodically exports the stored data in CSV or JSON format and provides it to the AI ​​module, for example, exporting the day's data at 6:00 PM every day.

[1121] The artificial intelligence module analyzes historical and current data using TensorFlow.

[1122] The AI ​​module predicts crop growth conditions, water needs, risk of pest and disease outbreaks, and more.

[1123] Input: Historical and current data exported from databases

[1124] Output: Analysis results (e.g. predicted crop condition, water requirements, pest risk)

[1125] Step 4: Generate predictions and advice

[1126] Based on the analysis results, the AI ​​module generates advice for the user as a specific course of action, such as "watering is required within the next 24 hours."

[1127] The generated advice is sent back to the server in JSON format.

[1128] Input: Analysis results

[1129] Output: Generated advice (JSON format)

[1130] Step 5: Sentiment analysis and notification adjustment

[1131] When users check their reports, they use the device's camera and microphone, which allows the collection of facial expression and voice data.

[1132] The device transmits the collected facial expression and voice data to a server.

[1133] The emotion recognition engine analyzes facial expressions and voice data on the server to recognize the user's emotional state, for example, to determine whether the user is tired.

[1134] The server optimizes the notification content based on the user's emotional state, for example, if the user seems busy, it will send a short and concise notification.

[1135] Input: Collected facial and voice data

[1136] Output: Notification content adjusted based on emotional state

[1137] Step 6: Sending notifications

[1138] The server then sends optimized notifications to the user's device via a dedicated application, email, or SMS. The dedicated application is a mobile app developed with Flutter.

[1139] The terminal displays the received notification to the user in real time.

[1140] Input: Notification content tailored based on emotional state

[1141] Output: Notification displayed on the user's device

[1142] Through these steps, the system efficiently collects, stores, and analyzes data, and provides appropriate advice based on the user's emotional state. This is expected to improve the accuracy of crop management and enhance user convenience.

[1143] (Application example 2)

[1144] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1145] Modern home and office environments demand improved safety. However, current security systems simply use security cameras and simple sensors, making it difficult to prevent or respond immediately to environmental changes or sudden accidents. Furthermore, there is no flexible notification system that takes into account the user's emotional state, so there is a need for instantaneous detection of danger and effective countermeasures.

[1146] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor device that measures soil condition, moisture content, and light intensity, a database that receives and stores measurement data from the sensor device, an artificial intelligence module that analyzes the data stored in the server device and generates predictions and advice regarding crop management, an emotion engine that recognizes the user's emotional state, and a means for notifying the user's terminal of the predictions and advice generated by the server device. This makes it possible to instantly detect suspicious behavior while the user is away or dangers such as fires and water leaks, and to provide appropriate notifications according to the user's emotional state.

[1147] A "sensor device" is a device that measures environmental data such as soil condition, moisture content, and light intensity, and transmits this data wirelessly to a server.

[1148] A "database" is a storage system that stores data received from sensor devices and complements abnormal or missing values ​​as necessary.

[1149] A "server device" is a collection of hardware and software that has a database and manages a series of processes that receive and analyze data from sensor devices and notify the user's terminal.

[1150] An "artificial intelligence module" is a component that includes algorithms and models for analyzing accumulated data and generating predictions and advice.

[1151] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, touch operations, etc., and customizes the notification content based on the analysis results.

[1152] The "notification means" refers to the means for delivering the generated predictions and advice to the user's terminal, and may be a dedicated application, email, SMS, or the like.

[1153] This invention is a security system for improving indoor safety, and uses a server device equipped with a database and sensors that measure soil conditions, moisture content, and light intensity. The server device also incorporates an artificial intelligence module and an emotion engine, which work together to generate predictions and advice, which are then sent to the user's terminal.

[1154] First, the user installs multiple sensor devices in a room. Each sensor device measures the soil moisture content, temperature, and light intensity, and transmits the data wirelessly to a server device. This allows the sensor devices to obtain environmental data in real time, independent of time and location.

[1155] The server device stores the received data in a database. The database records the data sent from each sensor device along with the date and time. The server checks the format and consistency of the data and fills in any outliers or missing values ​​as necessary. The software used in this process includes a database management system (DBMS) and a data processing tool.

[1156] The server device then periodically provides the data to an artificial intelligence module, which analyzes the accumulated data and predicts the indoor environmental conditions and potential hazards. For example, if the temperature is abnormally high, it generates a notification indicating a fire risk. It also notifies users of water leaks or dry conditions if there is an excess or shortage of moisture. These analyses are performed using machine learning algorithms and data analysis software.

[1157] Next, the server device is equipped with an emotion engine. The emotion engine analyzes facial expressions, voice, and touch operations when the user checks the report on the device to recognize the user's emotional state. For example, if the user is nervous, the notification content will be simplified to allow for a faster response. The facial recognition software and voice analysis tools used for this purpose estimate emotions by analyzing the user's facial and vocal characteristics.

[1158] The server device notifies the user device of the generated predictions and advice. This notification is done via a dedicated application, email, SMS, etc. The user can check the prediction results and advice in real time using the device. This process utilizes mobile notification services and email delivery systems.

[1159] As a concrete example, consider a scenario in which a sensor device measures the temperature, moisture content, and light intensity in a room every hour and sends the data to a server device. The server device stores the received data and provides it to an artificial intelligence module. The artificial intelligence module compares it with past data and, if an abnormality is predicted for the next 24 hours, generates a notification, for example, "The temperature is too high, so there is a risk of fire." This notification is sent to the user's smartphone, and the emotion engine analyzes the user's emotional state and adjusts the content of the notification as necessary. For example, if the user appears busy, it may provide a short message and concise advice.

[1160] An example of an input prompt for the generative AI model is, "Simplify the notification messages that users receive when they are under stress. If there is a high security risk, generate a concise notification message that encourages urgent action." As this prompt shows, it is designed to be able to respond flexibly according to the user's situation.

[1161] In this way, the present invention efficiently and effectively improves indoor safety, enabling rapid risk prediction and response.The addition of an emotion engine provides customized support based on the user's emotional state, improving usability and satisfaction.

[1162] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1163] Step 1:

[1164] The sensor device measures soil conditions, moisture content, and light intensity. As input, it uses data acquired in real time from the environment where the sensor is installed. This data is concrete numerical data such as soil moisture content, temperature, and light intensity. The sensor device transmits the measurement data wirelessly to a server.

[1165] Step 2:

[1166] The server receives measurement data sent from the sensor devices and stores it in a database. As input, it receives data sent from the sensors. It checks the format and consistency of the received data and imputes any abnormal or missing values. As output, the organized data is stored in the database.

[1167] Step 3:

[1168] The server provides the data stored in the database to the artificial intelligence module. The accumulated data in the database is used as input. The artificial intelligence module analyzes the data using machine learning algorithms to predict the indoor environmental conditions and potential hazards. The output is prediction results and advice.

[1169] Step 4:

[1170] The server rechecks the predictions and advice generated by the AI ​​module and passes them to the emotion engine. It uses the analysis results from the AI ​​module as input. The emotion engine analyzes the user's emotional state (facial expression, voice, touch operations, etc.) when checking the report on their device and generates appropriate notification content. As output, it generates a notification tailored to the user's emotional state.

[1171] Step 5:

[1172] The server notifies the user's device of the generated predictions and advice. As input, it receives the adjusted notification content from the emotion engine. As output, a notification is delivered to the user's device, such as a smartphone or smart glasses. This notification is sent via a dedicated application, email, or SMS.

[1173] Step 6:

[1174] The user uses the device to check the notification and take necessary action. The notification message received from the server is used as input. Suspicious behavior during absence, fire risk, water leak risk, etc. can be checked immediately. The output is that the user takes specific action.

[1175] This series of steps allows the security system to function effectively, enabling rapid response based on environmental data and improving indoor safety. Tailoring notification content to the user's emotional state provides a more user-friendly and satisfying system.

[1176] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1177] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1178] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1179] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1180] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1181] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1182] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1184] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1186] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1187] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1189] 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.

[1190] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1191] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1192] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1196] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1197] The following is further disclosed regarding the above embodiment.

[1198] (Claim 1)

[1199] A sensor device that measures soil condition, moisture content, and light intensity;

[1200] a server device having a database that receives and stores measurement data from the sensor device;

[1201] an artificial intelligence module that analyzes the data stored on the server device and generates predictions and advice regarding crop management;

[1202] The system includes a means for notifying a user's terminal of the predictions and advice generated by the server device.

[1203] (Claim 2)

[1204] The system of claim 1 , wherein the sensor devices include a soil moisture sensor, a temperature sensor, and a light sensor.

[1205] (Claim 3)

[1206] 2. The system of claim 1, wherein the server device includes means for verifying the format and integrity of the received data and storing the data in a database.

[1207] "Example 1"

[1208] (Claim 1)

[1209] A sensor device that measures soil condition, moisture content, and light intensity;

[1210] a server means having a database for receiving and storing measurement data from the sensor device;

[1211] analysis means for periodically providing the data stored in said server means to an artificial intelligence module for generating predictions and advice regarding crop management;

[1212] a communication means for notifying a user's terminal of the prediction and advice generated by the server means;

[1213] means for transmitting data to the server means using wireless communication by the sensor device;

[1214] a pre-processing means for checking the format and consistency of the data received by the server means and for complementing abnormal values ​​and missing values;

[1215] A system including:

[1216] (Claim 2)

[1217] 10. The system of claim 1, wherein the sensor devices include a soil moisture sensor, a temperature sensor, and a light sensor.

[1218] (Claim 3)

[1219] 2. The system of claim 1, wherein the server means includes means for analyzing data using a generative AI model to predict crop growth conditions, water requirements, and risk of pest infestation.

[1220] "Application Example 1"

[1221] (Claim 1)

[1222] A sensor device that measures soil condition, moisture content, and light intensity;

[1223] a server device having a database that receives and stores measurement data from the sensor device;

[1224] an artificial intelligence module that analyzes the data stored on the server device and generates predictions and advice regarding crop management;

[1225] means for notifying a user's terminal of the prediction and advice generated by the server device;

[1226] A means for monitoring the vehicle's driving environment and proposing the optimal route;

[1227] The system includes means for analyzing driving environment data from the sensor device and generating a notification according to the driving conditions of the vehicle.

[1228] (Claim 2)

[1229] 10. The system of claim 1, wherein the sensor device is a means for monitoring the vehicle's surrounding environment, the sensor device including a soil moisture sensor, a temperature sensor, and a light sensor.

[1230] (Claim 3)

[1231] 2. The system according to claim 1, wherein the server device includes means for checking the format and consistency of the received data, complementing abnormal values ​​and missing values, and storing the data in a database.

[1232] "Example 2: Combining Emotion Engines"

[1233] (Claim 1)

[1234] A sensor device that measures soil condition, moisture content, and light intensity;

[1235] a server device having a database that receives and stores measurement data from the sensor device;

[1236] means for correcting the data stored in said database by a consistency and completion algorithm;

[1237] an artificial intelligence module that analyzes the data stored on the server device and generates predictions and advice regarding crop management;

[1238] an emotion recognition engine that is incorporated in the server device and that analyzes the emotion of a user;

[1239] means for notifying a user's terminal of the prediction and advice generated by the server device;

[1240] means for adjusting notification content based on the emotional state of the user analyzed by the emotion recognition engine.

[1241] (Claim 2)

[1242] 10. The system of claim 1, wherein the sensor devices include a soil moisture sensor, a temperature sensor, and a light sensor.

[1243] (Claim 3)

[1244] 2. The system according to claim 1, wherein the server device includes means for checking the format and consistency of the received data and storing the data in a database, and means for complementing abnormal values ​​and missing values.

[1245] "Application example 2 when combining emotion engines"

[1246] (Claim 1)

[1247] A sensor device that measures soil condition, moisture content, and light intensity;

[1248] a server device having a database that receives and stores measurement data from the sensor device;

[1249] an artificial intelligence module that analyzes the data stored on the server device and generates predictions and advice regarding crop management;

[1250] an emotion engine that recognizes the user's emotional state;

[1251] The system includes a means for notifying a user's terminal of the predictions and advice generated by the server device.

[1252] (Claim 2)

[1253] The system of claim 1 , wherein the sensor devices include a soil moisture sensor, a temperature sensor, and a light sensor.

[1254] (Claim 3)

[1255] 2. The system of claim 1, wherein the server device includes means for verifying the format and integrity of the received data and storing the data in a database. [Explanation of symbols]

[1256] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor device that measures soil condition, moisture content, and light intensity; a server device having a database that receives and stores measurement data from the sensor device; an artificial intelligence module that analyzes the data stored on the server device and generates predictions and advice regarding crop management; and means for notifying a user's terminal of the predictions and advice generated by the server device.

2. The system of claim 1 , wherein the sensor devices include a soil moisture sensor, a temperature sensor, and a light sensor.

3. 2. The system according to claim 1, wherein said server device includes means for checking the format and integrity of the received data and storing the data in a database.

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