System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting environmental data from sensors; means for optimizing growing conditions based on the collected environmental data; means for calculating a yield of a crop based on the optimized growing conditions; and means for outputting the calculated yield of the crop.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] "Traditional food production methods are unable to adapt to rapid urbanization and limited space, affecting food security. Furthermore, efficient and sustainable food production is required, making effective use of resources and maximizing yields. This invention addresses these issues by optimizing vertical farming in urban areas, providing a system that increases efficiency and sustainability." [Means for solving the problem]

[0005] This invention is a system that includes the following means: first, means for collecting environmental data from sensors; second, means for optimizing cultivation conditions based on the collected environmental data; third, means for calculating crop yield based on the optimized cultivation conditions; and finally, means for outputting the calculated crop yield. This allows the system to collect environmental data including temperature, humidity, light intensity, nutrient solution concentration, and soil humidity, and automatically set efficient cultivation conditions based on the collected data. It is also possible to maximize crop yield by adjusting each condition to keep it within a specific range.

[0006] A "sensor" is a device that collects physical or environmental data.

[0007] "Environmental data" refers to information relating to physical conditions that affect cultivation, such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0008] "Cultivation conditions" refer to the settings of environmental parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture that affect the growth and yield of crops.

[0009] "Optimize" means adjusting the parameters of a system to achieve a particular goal.

[0010] "Crop yield" is the total amount of crop harvested within a certain period of time.

[0011] A "means" is a device, algorithm, or method used to accomplish a particular function or process.

[0012] "Output" means to display, print, or otherwise provide the calculated or processed results to a user. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. Specific embodiments of this system are described below.

[0035] Program Description

[0036] 1. Data collection

[0037] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, thereby accurately determining the current state of the growing environment.

[0038] 2. Running the optimization algorithm

[0039] The server optimizes growing conditions based on the collected environmental data. For example, if the temperature deviates from the specified range (20°C to 28°C), it adjusts it accordingly. The same goes for humidity, light intensity, nutrient solution concentration, and soil moisture, adjusting each parameter to stay within the optimal range.

[0040] 3. Yield Calculation

[0041] The server calculates the crop yield based on the optimized cultivation conditions by building a yield model using the average values ​​of each parameter and predicting the yield.

[0042] 4. Outputting the results

[0043] The server calculates and outputs the final environmental conditions and crop yield, allowing users to check the current cultivation conditions and predicted crop yield.

[0044] Specific examples

[0045] Example of processing time per day

[0046] Data collection

[0047] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[0048] Optimization process

[0049] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[0050] Yield calculation

[0051] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[0052] Result output

[0053] The server outputs and displays the adjusted environmental conditions and yield to the user, who can then check them and plan their next cultivation.

[0054] Thus, the system of the present invention can realize efficient and sustainable vertical farming, and has wide applicability as it can be adapted to different urban environments and cultivation conditions.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] Environmental data is collected from sensors. The server collects data in real time from various sensors installed in the vertical farming system (temperature sensors, humidity sensors, light sensors, nutrient solution sensors, soil moisture sensors), allowing the current environmental conditions to be understood.

[0058] Step 2:

[0059] Temporarily store collected data. The server stores the collected environmental data in a database or temporary memory. This storage process makes it easier to process and analyze the data later.

[0060] Step 3:

[0061] Various conditions are checked and optimized. Based on the collected data, the server checks whether temperature, humidity, light intensity, nutrient solution concentration, soil humidity, etc. are within the set ranges. If a condition is outside the range, adjustments are made to optimize each condition. For example, if the temperature is below 20°C, adjustments such as turning on the heater are made.

[0062] Step 4:

[0063] Calculates crop yield based on optimized conditions. The server calculates predicted yield based on adjusted environmental conditions. Yield is predicted using a simple model using the average and standard values ​​of each environmental parameter.

[0064] Step 5:

[0065] Temporarily save the calculation results. The server saves the calculated crop yields and optimized environmental conditions in a database or temporary memory, which makes it easier to refer to the results or recalculate later.

[0066] Step 6:

[0067] The results are output and displayed to the user. The server outputs the optimized environmental conditions and predicted crop yields, which are displayed on the user's device. The user can use the displayed information to plan the next cultivation steps and adjustments.

[0068] Step 7:

[0069] Gathering user feedback and tuning the system. The user checks the results and sends feedback to the server as needed. The server then readjusts the system parameters based on this feedback and applies it to the next data collection and optimization process.

[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] To achieve efficient and sustainable food production in urban agriculture, detailed adjustment and management of the cultivation environment is necessary. With conventional methods, collecting environmental data and optimizing cultivation conditions based on that data is time-consuming and labor-intensive, and the results are often unstable. To solve these problems, a new integrated system is needed.

[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 means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for adjusting each parameter using the collected environmental data to keep it within an appropriate range, and means for constructing and predicting a yield model based on the adjusted environmental data, thereby enabling detailed adjustment and management of the cultivation environment to be performed efficiently and effectively.

[0075] A "sensor" is a measuring device for collecting environmental data.

[0076] "Environmental data" is information that indicates the state of the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0077] A "server" is a device that processes environmental data collected from sensors and performs calculations to optimize cultivation conditions.

[0078] "Growth conditions" refers to the optimal range of environmental data required to support proper plant growth.

[0079] "Optimization" refers to the process of adjusting growing conditions to fall within ideal ranges based on collected environmental data.

[0080] "Yield" is a value that indicates the expected production of a crop under specific cultivation conditions.

[0081] A "yield model" is a mathematical or statistical model for calculating predicted crop yields based on environmental data.

[0082] "Output" refers to the processing to provide the final environmental conditions and predicted yields to the user.

[0083] "Adjustment" refers to the operation and control of various devices to keep each parameter of environmental data within an appropriate range.

[0084] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system collects environmental data from sensors, automatically adjusts optimal cultivation conditions, and predicts crop yields. This section describes a specific embodiment of the system.

[0085] First, the system is equipped with various sensors to measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. These sensors collect data in real time and send it to a server. The server receives this environmental data and performs the necessary calculations and data processing.

[0086] The server first analyzes the collected environmental data and checks whether each parameter is within the specified range. For example, assume the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this data, the server checks whether the temperature is within the appropriate range and makes adjustments as necessary. Because the humidity is below 50%, it turns on the humidifier and sets it to increase the humidity by 1%. The light intensity and nutrient solution concentration are also adjusted to fall within their respective optimal ranges.

[0087] The server then calculates the yield based on the adjusted environmental conditions. For example, it uses average values ​​for temperature, humidity, light intensity, nutrient solution concentration, and soil moisture to build a yield model and calculate a predicted yield. This yield model can be refined using collected data. In a specific example, after all parameters are adjusted, the yield is calculated as 36.8.

[0088] Finally, the server outputs the calculated environmental conditions and predicted yield to the user. The user can use this information to plan their next cultivation. For example, the following prompt sentence can be input into the generative AI model and used.

[0089] Example prompts

[0090] In an AI-integrated vertical farming system, current environmental data collected from sensors shows the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this, set the optimal cultivation conditions and predict the yield that will result from increasing humidity by 1%. Also, output the environmental conditions and predicted yield after setting the settings.

[0091] In this way, this system realizes efficient and sustainable vertical farming using various sensors connected to a server and automated optimization algorithms. This system can be adapted to different urban environments and cultivation conditions, making it versatile.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] Environmental data collection

[0095] The server collects environmental data from sensors within the vertical farming system.

[0096] Input: Measurement data from each sensor (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0097] Data processing: The server receives data from various sensors in real time, organizes it, and records it.

[0098] Output: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%).

[0099] Specific operation: The server receives data of 22.5°C from the temperature sensor, data of 48% from the humidity sensor, and collects data from other sensors in the same way.

[0100] Step 2:

[0101] Optimizing cultivation conditions

[0102] Cultivation conditions are optimized based on the environmental data collected by the server.

[0103] Input: Collected environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0104] Data calculation: The server checks each parameter and calculates the adjustments to keep it within the specified range (e.g., temperature 20°C to 28°C, humidity 50% to 60%, etc.).

[0105] Output: Optimized cultivation environment conditions (e.g., temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%).

[0106] Specific operation: The server checks that the temperature is within the appropriate range at 22.5°C, and the humidity is below the lower limit of the appropriate range at 48%, so it activates the humidifier to adjust the humidity to 50%. It also optimizes other parameters in the same way.

[0107] Step 3:

[0108] Yield calculation

[0109] The server calculates the crop yield based on the optimized cultivation conditions.

[0110] Input: Optimized environmental condition data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0111] Data calculation: The server applies the yield model and predicts the yield based on the average value of each parameter.

[0112] Output: Predicted yield (e.g. 36.8).

[0113] Specific operation: The server calculates the yield model using data for temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, and soil moisture 30%, and predicts a yield of 36.8.

[0114] Step 4:

[0115] Output of results

[0116] The server outputs the final environmental conditions and crop yields to the user.

[0117] Input: Optimized environmental condition data and predicted yield.

[0118] Data processing: The server organizes the adjusted environmental conditions and predicted yields and converts them into a format that is easy for users to understand.

[0119] Output: Displayed on the user interface (optimized environmental conditions and predicted yield).

[0120] Specific operation: The server formats the data to display the adjusted temperature of 22.5°C, humidity of 50%, light intensity of 75%, nutrient solution concentration of 1.4 mL / L, soil humidity of 30%, and predicted yield of 36.8, and outputs it to the user interface.

[0121] In this way, efficient and sustainable vertical farming is achieved through data collection, processing, and output at each step.

[0122] (Application example 1)

[0123] 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."

[0124] Modern urban agriculture requires efficient and sustainable food production, but conventional systems have difficulty optimizing environmental conditions in real time and planning harvests and shipments. Current technology only optimizes conditions using data from individual sensors, and does not automate yield predictions or shipment plans. This leads to a lack of efficiency and accuracy in production and waste of resources.

[0125] 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.

[0126] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for monitoring the collected environmental data in real time, and means for automatically creating harvest and shipping plans based on the optimized cultivation conditions, thereby enabling efficient and sustainable food production in urban agriculture, reducing resource waste, and automatically optimizing harvest and shipping plans.

[0127] A "sensor" is a device that measures and collects environmental data.

[0128] "Environmental data" includes data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0129] "Cultivation conditions" refer to the environmental conditions necessary for crops to grow.

[0130] "Optimization" refers to adjusting to the most desirable state under given conditions and resources.

[0131] "Crop yield" is the total production of a crop grown for a particular period and under particular conditions.

[0132] "Collecting" refers to the act of obtaining data using sensors or other devices.

[0133] "Monitoring" "collected environmental data" means constantly observing it and, if necessary, recording and analyzing it.

[0134] "Real-time" refers to reacting immediately to the current time in progress.

[0135] "Automatically creating a harvest and shipping plan based on optimized cultivation conditions" means automatically setting a harvest and shipping schedule in accordance with optimized cultivation conditions.

[0136] To implement this invention, a vertical farming system installed in a distribution center, a server for managing the system, and a mobile device are required. The system collects environmental data from sensors, optimizes cultivation conditions based on the data, predicts yield, and automatically creates harvest and shipping plans.

[0137] Hardware and software used

[0138] Hardware:

[0139] Sensors: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor

[0140] Smart devices: smartphones, head-mounted displays (e.g. HoloLens)

[0141] Robot: Autonomous Guided Vehicle (AGV)

[0142] software:

[0143] Data analysis libraries: NumPy, scikit-learn

[0144] Program execution environment: Python

[0145] Smartphone app: Kotlin (for Android), Swift (for iOS)

[0146] System Operation

[0147] 1. Data Collection:

[0148] The server collects real-time data from various sensors on temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which serves as the basis for optimizing the growing environment for crops.

[0149] 2. Data optimization:

[0150] The server analyzes the collected environmental data and adjusts each parameter to suit the optimal cultivation conditions, thereby providing the optimal environment for crop growth.

[0151] 3. Yield prediction:

[0152] The server predicts crop yields based on the optimized data, using a yield model.

[0153] 4. Automated shipping planning:

[0154] Based on the optimized data and yield forecasts, the server automatically creates harvest and shipping plans that are dynamically updated based on data collected in real time.

[0155] Specific examples

[0156] For example, suppose the data collected by the server in the morning of a certain day is a temperature of 24°C, humidity of 58%, light intensity of 76%, nutrient solution concentration of 1.4 mL / L, and soil moisture of 36%. Based on this data, the server optimizes the environmental conditions and calculates a predicted yield of 36.8 kg. It then automatically creates a shipping plan based on the expected harvest date and prepares for delivery.

[0157] Prompt Sentence Examples

[0158] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[0159] In this way, this invention enables efficient and sustainable food production in urban agriculture within logistics centers, minimizing resource waste and optimizing cultivation conditions by automating harvesting and shipping planning.

[0160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0161] Step 1:

[0162] The server collects environmental data from various sensors. Specifically, it is equipped with temperature, humidity, light intensity, nutrient solution concentration, and soil humidity sensors, and these sensors collect data in real time and send it to the server. The input is the real-time data (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) obtained from each sensor, and the output is environmental data that is a unified version of this data.

[0163] Step 2:

[0164] The server optimizes cultivation conditions based on the collected environmental data. Specifically, it runs an algorithm that adjusts each parameter (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) to fall within a specific range. The input is the collected environmental data, and the output is the adjusted environmental data. If the temperature is 24°C and the humidity is 58%, it checks whether they are within the optimal range and adjusts the environmental conditions as necessary.

[0165] Step 3:

[0166] The server reconfirms the cultivation conditions using the optimized environmental data. Specifically, it evaluates whether the optimized environmental data meets the ideal cultivation conditions and makes further fine adjustments if necessary. The input is the optimized environmental data, and the output is the environmental data after reconfirmation and fine adjustment.

[0167] Step 4:

[0168] The server predicts crop yields based on the optimized environmental data. Specifically, it uses a yield prediction model to calculate yields using each optimized parameter as input data. This yield prediction model uses, for example, a machine learning algorithm (such as Linear Regression). The input is the optimized environmental data, and the output is the predicted yield.

[0169] Step 5:

[0170] The server automatically creates harvest and shipping plans based on the yield prediction results. Specifically, it determines the harvest date and time based on the predicted yield and even sets the subsequent shipping plan. It also includes a process that uses autonomous vehicles (AGVs) to automatically prepare for harvest and shipping. The input is the predicted yield and the current schedule, and the output is the harvest and shipping plan.

[0171] Step 6:

[0172] The server or terminal displays the final environmental conditions, predicted yield, and harvest and shipping plans on the user's device. Specifically, data is sent to a smartphone or head-mounted display, allowing the user to check the current situation in real time. The input is the final environmental data, predicted yield, and harvest and shipping plans, and the output is the terminal screen displaying this information.

[0173] Prompt Sentence Examples

[0174] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[0175] 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.

[0176] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. It also incorporates an emotion engine to provide feedback based on the user's emotions, improving the user experience. A specific embodiment of this system is described below.

[0177] Program Description

[0178] 1. Data collection

[0179] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, providing a detailed understanding of the current state of the cultivation environment.

[0180] 2. Data storage

[0181] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[0182] 3. Optimizing cultivation conditions

[0183] The server optimizes settings such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity based on the collected environmental data. For example, if the temperature is outside the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity will also be adjusted to stay within the optimal range.

[0184] 4. Yield Calculation

[0185] The server calculates the crop yield based on the optimized cultivation conditions by predicting the yield using a simple model that uses the average and standard values ​​of each environmental parameter.

[0186] 5. Saving the results

[0187] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory, which makes it easy to refer to the results or perform recalculations later.

[0188] 6. Outputting the results

[0189] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. The user can then use the displayed information to plan their next cultivation or adjustment work.

[0190] 7. Running the Emotion Engine

[0191] The server collects the user's voice and facial expression data from the user's device and analyzes it using an emotion engine, which then recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[0192] 8. Providing emotional feedback

[0193] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the system displays encouraging messages or helpful advice for farming operations.

[0194] Specific examples

[0195] Example of processing time per day

[0196] Data collection

[0197] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[0198] Optimization process

[0199] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[0200] Yield calculation

[0201] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[0202] Result output

[0203] The server outputs the adjusted environmental conditions and yields and displays them on the user's terminal, allowing the user to check them and plan the next cultivation steps or adjustment work.

[0204] emotion recognition

[0205] The server analyzes the user's voice and facial expressions, and uses an emotion engine to recognize whether the user is satisfied or dissatisfied with the system's results.

[0206] Emotional Feedback

[0207] If the user is feeling stressed, the server will display positive feedback such as "You're doing well today! You're almost there!"

[0208] In this way, the system of the present invention can realize efficient and sustainable vertical farming, and can also improve the user experience by providing a feedback function based on the user's emotions.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] The server collects environmental data from sensors installed in the vertical farming system, which measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, and sends the data to the server.

[0212] Step 2:

[0213] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[0214] Step 3:

[0215] The server uses the collected data to check whether each cultivation condition (temperature, humidity, light intensity, nutrient solution concentration, soil humidity) is within the set range. For example, if the temperature is below 20°C, it will make adjustments such as turning on the heater.

[0216] Step 4:

[0217] The server adjusts environmental conditions as needed to set optimal growing conditions, such as increasing humidity or adjusting light intensity, to ensure all conditions are within the optimum range.

[0218] Step 5:

[0219] The server calculates the crop yield based on the optimized cultivation conditions by using a simple model to predict the yield using the average and standard values ​​of each environmental parameter.

[0220] Step 6:

[0221] The server stores the calculated crop yields and optimized environmental conditions in a database or temporary memory, which facilitates future reference and recalculation of the results.

[0222] Step 7:

[0223] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. Based on the displayed information, the user can plan their next cultivation and adjustment work.

[0224] Step 8:

[0225] The server collects the user's voice and facial expression data from the user's device, and uses an emotion engine to recognize the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[0226] Step 9:

[0227] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the server displays encouraging messages or helpful advice for farming operations.

[0228] Step 10:

[0229] Users can review the results and feedback, and then take the next steps or make adjustments, improving their cultivation efficiency and satisfaction.

[0230] Example 2

[0231] 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."

[0232] To achieve efficient and sustainable food production in urban agriculture, it is necessary to collect and manage detailed environmental data and maintain appropriate cultivation conditions. However, conventional systems require a complicated process for collecting environmental data, and adjustments to maintain optimal cultivation conditions are labor-intensive. Another issue is the lack of feedback functions to improve the user's farming experience.

[0233] 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.

[0234] In this invention, the server includes means for collecting environmental data from sensors, means for storing the collected environmental data in a database, means for optimizing cultivation conditions based on the collected environmental data, means for calculating a crop yield based on the optimized cultivation conditions, means for storing the optimized cultivation conditions and the calculated crop yield in a database, means for outputting the calculated crop yield and displaying it on a user terminal, means for collecting emotion data to analyze user emotions, and means for providing feedback based on the emotion data, thereby enabling efficient and sustainable cultivation and providing a feedback function that improves the user experience.

[0235] A "sensor" is a device for collecting environmental data, and is capable of measuring parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0236] "Environmental data" refers to various data that indicate the plant cultivation environment, and specifically includes information such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0237] A "database" is a system for systematically storing and managing large amounts of data, and is capable of efficiently storing, searching, and updating data.

[0238] "Optimization means" refers to algorithms and control systems that analyze collected environmental data and maintain optimal cultivation conditions.

[0239] "Yield" is an indicator of the amount of harvested crops grown and is predicted based on optimized environmental conditions.

[0240] "Emotion data" is data collected from the user's voice, facial expressions, etc., and is basic data for analyzing the user's emotional state.

[0241] A "means for providing feedback" is a system or function for providing a corresponding message or advice to a user based on the analyzed emotional data of the user.

[0242] A "user terminal" is a device used by a user to receive information, and includes electronic devices such as smartphones, tablets, and personal computers.

[0243] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system integrates means for collecting, storing, and analyzing environmental data, and means for providing feedback based on user emotions.

[0244] Hardware and Software Configuration

[0245] server:

[0246] The server plays a central role in data collection, data storage, data analysis, environmental control, and feedback provision. Specific functions are as follows:

[0247] sensor:

[0248] The sensors used are temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, each connected to a data collection device such as a Raspberry Pi.

[0249] Database:

[0250] The database uses MySQL and stores collected environmental data, optimized cultivation conditions, and yield predictions.

[0251] Analysis and optimization software:

[0252] The analysis and optimization software is implemented using Python, and the data science library scikit-learn is used for the optimization algorithms.

[0253] Emotion Engine:

[0254] The Emotion API from Microsoft Azure is used to analyze user emotions, and emotional states are analyzed based on voice and facial expression data.

[0255] User device:

[0256] User terminals are electronic devices such as smartphones, tablets, and personal computers. These terminals receive data from the server and display it to the user.

[0257] Specific processing flow

[0258] First, the server periodically collects environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture) from sensors, and stores the collected data in a database.

[0259] The server then uses Python and scikit-learn to calculate optimal growing conditions based on the collected data. For example, if the temperature is outside the set range (20°C to 28°C), the temperature control system will automatically adjust. Similarly, humidity, light intensity, nutrient solution concentration, and soil moisture will also be optimized.

[0260] Using the optimized conditions, the server predicts crop yields by building a predictive model based on past data and current environmental conditions, and then calculating yields. The results are then stored in a database.

[0261] The saved optimization conditions and yield information are sent to the user's device and displayed to the user. Specific display information could be "Temperature: 22.5°C, Humidity: 49%, Light Intensity: 70%, Predicted Yield: 36.8".

[0262] Furthermore, the server collects voice and facial expression data from the user's smartphone or computer and sends it to the Emotion API to analyze the user's emotions. Based on the analysis results, the system provides positive feedback, such as "You're doing well today! You're almost there!"

[0263] Specific examples

[0264] Example prompt sentence:

[0265] 1. Collect from the sensors the current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%.

[0266] 2. The server checks the temperature and determines that it is within the normal range. At the same time, it determines that the humidity is below 50% and issues a command to use a humidifier to increase the humidity by 1%.

[0267] 3. Based on the collected data, the yield is calculated using the optimized environmental conditions. The yield is predicted using a linear regression model in scikit-learn.

[0268] 4. The server stores the predicted yield of 36.8 and the optimized environmental conditions in a database and displays them on the user's terminal.

[0269] 5. Send the user's facial expressions and voice data to the Emotion API to analyze their emotional state. If the user is feeling stressed, provide positive feedback.

[0270] In this way, by combining detailed collection and management of environmental data, optimization of cultivation conditions, calculation of predicted yields, and feedback based on user emotions, the system of the present invention can achieve efficient and sustainable food production in urban agriculture while also improving the user experience.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1: Collect data

[0273] Description: The server periodically collects environmental data from various sensors (temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor) installed in the vertical farming system.

[0274] How it works: The server obtains the current temperature data (e.g., 22.5°C) from the temperature sensor connected to the Raspberry Pi. Similarly, it obtains the humidity (e.g., 48%), light intensity (e.g., 75%), nutrient solution concentration (e.g., 1.4 mL / L), and soil moisture (e.g., 30%) from other sensors.

[0275] Input: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil humidity sensor

[0276] Output: Temperature, humidity, light intensity, nutrient solution concentration, soil humidity data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[0277] Step 2: Save your data

[0278] Description: The server stores the collected environmental data in a database.

[0279] Specific operation: The server saves the collected data of temperature (22.5°C), humidity (48%), light intensity (75%), nutrient solution concentration (1.4 mL / L), and soil moisture (30%) in a MySQL database using INSERT statements.

[0280] Input: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%)

[0281] Output: Environmental data stored in a database

[0282] Step 3: Optimizing cultivation conditions

[0283] Description: The server optimizes the cultivation conditions based on the collected environmental data. It uses an optimization algorithm written in Python.

[0284] What happens: The server passes the collected data to a Python script, which verifies that the temperature is within the normal range at 22.5°C. The humidity is out of range at 48%, so it issues a command to use a humidifier to increase the humidity by 1%. It then adjusts other parameters in the same way.

[0285] Input: Environmental data stored in the database (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[0286] Output: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0287] Step 4: Calculate yield

[0288] Description: The server calculates crop yields based on optimized cultivation conditions. It uses the data science library scikit-learn.

[0289] How it works: The server inputs the optimized environmental conditions into a linear regression model in scikit-learn to calculate the crop yield. For example, based on the optimized temperature of 22.5°C, humidity of 49%, light intensity of 70%, nutrient solution concentration of 1.3 mL / L, and soil moisture of 28%, the predicted yield is calculated as 36.8.

[0290] Input: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0291] Output: Expected yield (e.g. 36.8)

[0292] Step 5: Save the results

[0293] Description: The server stores the calculated crop yields and optimized cultivation conditions in a database.

[0294] Specific operation: The server uses an INSERT statement to save the predicted yield of 36.8 and the optimized environmental conditions data into the MySQL database.

[0295] Input: Predicted yield and optimized cultivation conditions (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0296] Output: Predicted yield and optimized cultivation conditions stored in a database

[0297] Step 6: Output the results

[0298] Description: The server displays the optimized environmental conditions and crop yields on the user's device.

[0299] Specific operation: The server uses HTML and JavaScript to generate a web page containing the optimized data (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, predicted yield 36.8), sends it to the user's device, and displays it in the browser.

[0300] Input: Predicted yield and optimized cultivation conditions stored in the database (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0301] Output: Environmental conditions and predicted yield displayed on the user's terminal

[0302] Step 7: Run the Emotion Engine

[0303] Description: The server passes the voice and facial expression data collected from the user's device to the emotion engine and analyzes the user's emotional state.

[0304] How it works: When a user is checking the displayed information on their smartphone, the camera and microphone collect the user's facial expressions and voice, and send this data to the server. The server then passes this data to the Emotion API for analysis.

[0305] Input: Voice data, facial expression data (e.g., user's facial expressions and voice)

[0306] Output: Parsed emotional state (e.g., stressed, satisfied, dissatisfied)

[0307] Step 8: Provide emotional feedback

[0308] Description: The server customizes results and provides feedback based on the user's emotional state as recognized by the emotion engine.

[0309] Specific operation: If the server recognizes that the user is feeling stressed, it generates a positive message such as "Today's work is going well! You're almost there!" and displays it on the user's device.

[0310] Input: Parsed emotional state (e.g., stress)

[0311] Output: A customized feedback message (e.g., "You're doing great today! You're almost there!")

[0312] (Application example 2)

[0313] 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."

[0314] Conventional vertical farming systems achieve efficient cultivation through the collection and optimization of environmental data, but no systems have taken user emotions and feedback into consideration. This has resulted in a lack of improvement in the user experience and limitations in sustainable agricultural operations. There is a need for more personalized support that reflects the emotions users feel about system operation and results.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0316] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for collecting user emotion data, and means for analyzing the collected emotion data and providing feedback, thereby realizing efficient and sustainable vertical farming and providing feedback based on the user's emotions.

[0317] A "sensor" is a device for measuring environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0318] "Environmental data" refers to information related to the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0319] "Cultivation conditions" refer to the settings of temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which are environmental parameters optimal for crop growth.

[0320] "Yield" refers to the amount of crop produced within a specific period of time.

[0321] "Emotion data" is information about the emotional state obtained by analyzing the user's facial expressions and voice.

[0322] An "optimization means" is a process or device that adjusts cultivation conditions within set ranges based on collected environmental data.

[0323] The "collection means" is a process or device that acquires environmental data and user emotion data using sensors or the like.

[0324] An "analysis means" is a process or device for processing acquired data and extracting useful information.

[0325] A "feedback means" is a process or device that provides appropriate messages or advice based on the user's emotional data.

[0326] The "system" is a comprehensive device that combines the above sensors, collection means, optimization means, analysis means and feedback means to manage vertical farming and provide user support.

[0327] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable urban agriculture. The invention improves the user experience by incorporating a function to collect user emotion data and provide feedback based on that data.

[0328] First, the system is equipped with multiple sensors. These sensors periodically collect environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. This allows for a detailed understanding of the current cultivation environment. The collected data is sent to a server and stored in a database or temporary memory.

[0329] The server uses AI to optimize cultivation conditions based on the collected environmental data. The AI ​​optimization engine adjusts parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain the optimal cultivation environment. For example, if the temperature deviates from the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity are also controlled to stay within their respective optimal ranges.

[0330] The server then predicts crop yields based on the optimized cultivation conditions. Using a prediction model, it calculates yields based on the average and standard values ​​of each environmental parameter. The calculated yields and optimized cultivation conditions are stored in a database or temporary memory, allowing for future reference and recalculation.

[0331] Furthermore, the system has the ability to collect and analyze the user's emotional data. The server collects voice and facial expression data from the user's device and analyzes it using an emotion engine. This emotion engine recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.) and customizes the information output based on the results. For example, if the user is feeling stressed, the system will provide encouraging messages and helpful advice.

[0332] Specific examples

[0333] The following is an example of a day's processing. Sensors collect the following information: current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%. The server uses this data to determine whether the temperature is appropriate and adjusts the humidity to approach 50%. Light intensity and nutrient solution concentration are also adjusted to their optimal ranges. Crop yield is predicted based on this data; for example, a yield of 36.8% is calculated. The user's emotional data is then collected and analyzed by the emotion engine. If the system recognizes that the user is satisfied with the system's results, no special feedback is provided. However, if the user is feeling stressed, a positive message such as "Today's work is going well! You're almost there!" is displayed.

[0334] Prompt Sentence Examples

[0335] For example, you could use the prompt "If the temperature is 30°C, what can you do to keep it within the optimal range?"

[0336] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0337] Step 1:

[0338] The server collects environmental data from sensors installed in the vertical farming system. The sensors include temperature, humidity, light, nutrient solution concentration, and soil moisture sensors. The data measured by these sensors (input) is sent to the server and stored in a database or temporary memory in real time (output).

[0339] Step 2:

[0340] The server analyzes the collected environmental data and determines the current state of the cultivation environment. Input data such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are compared with a set optimal range (e.g., temperature: 20°C to 28°C). The server outputs the comparison result (e.g., temperature outside the optimal range) and generates instructions to adjust the situation.

[0341] Step 3:

[0342] The server uses an AI optimization engine to automatically optimize cultivation conditions based on collected environmental data. Based on the input data, temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are adjusted to fall within optimal ranges (for example, if the temperature is 30°C, the cooling system is activated and set to 22°C). The optimized environmental conditions (output) after adjustment are stored in a database.

[0343] Step 4:

[0344] The server predicts crop yields based on the optimized cultivation conditions. The yield prediction model uses average or standard values ​​for the optimized temperature, humidity, light intensity, nutrient solution concentration, and soil moisture as input data. The prediction algorithm processes these parameters and outputs a predicted yield (e.g., a yield of 36.8).

[0345] Step 5:

[0346] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory. The stored data (input) is prepared so that it can be accessed from the user's device. This allows the user to plan cultivation and adjustment work based on the stored data (output).

[0347] Step 6:

[0348] The server collects user voice and facial expression data from the user's device. The device's built-in camera and microphone capture the user's facial expressions and voice and send them to the server as input data. The emotion analysis engine analyzes this data and detects the user's emotional state (e.g., satisfaction, dissatisfaction, stress) (output).

[0349] Step 7:

[0350] The server provides feedback based on the user's emotions recognized by the emotion analysis engine. The result of the emotion analysis is used as input to generate a feedback message (e.g., "You're doing well today! You're almost there!") that corresponds to the user's emotional state. This message is then sent to the user's device and displayed (output).

[0351] Step 8:

[0352] The user checks the feedback messages and the latest cultivation environment information provided by the server. For example, based on information such as whether the temperature is within the appropriate range or the predicted yield is 36.8, the user can plan the next cultivation step or adjustment work (based on the confirmed data as input, a specific work plan is created as output).

[0353] 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.

[0354] 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.

[0355] 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.

[0356] [Second embodiment]

[0357] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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."

[0369] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. Specific embodiments of this system are described below.

[0370] Program Description

[0371] 1. Data collection

[0372] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, thereby accurately determining the current state of the growing environment.

[0373] 2. Running the optimization algorithm

[0374] The server optimizes growing conditions based on the collected environmental data. For example, if the temperature deviates from the specified range (20°C to 28°C), it adjusts it accordingly. The same goes for humidity, light intensity, nutrient solution concentration, and soil moisture, adjusting each parameter to stay within the optimal range.

[0375] 3. Yield Calculation

[0376] The server calculates the crop yield based on the optimized cultivation conditions by building a yield model using the average values ​​of each parameter and predicting the yield.

[0377] 4. Outputting the results

[0378] The server calculates and outputs the final environmental conditions and crop yield, allowing users to check the current cultivation conditions and predicted crop yield.

[0379] Specific examples

[0380] Example of processing time per day

[0381] Data collection

[0382] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[0383] Optimization process

[0384] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[0385] Yield calculation

[0386] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[0387] Result output

[0388] The server outputs and displays the adjusted environmental conditions and yield to the user, who can then check them and plan their next cultivation.

[0389] Thus, the system of the present invention can realize efficient and sustainable vertical farming, and has wide applicability as it can be adapted to different urban environments and cultivation conditions.

[0390] The processing flow will be explained below.

[0391] Step 1:

[0392] Environmental data is collected from sensors. The server collects data in real time from various sensors installed in the vertical farming system (temperature sensors, humidity sensors, light sensors, nutrient solution sensors, soil moisture sensors), allowing the current environmental conditions to be understood.

[0393] Step 2:

[0394] Temporarily store collected data. The server stores the collected environmental data in a database or temporary memory. This storage process makes it easier to process and analyze the data later.

[0395] Step 3:

[0396] Various conditions are checked and optimized. Based on the collected data, the server checks whether temperature, humidity, light intensity, nutrient solution concentration, soil humidity, etc. are within the set ranges. If a condition is outside the range, adjustments are made to optimize each condition. For example, if the temperature is below 20°C, adjustments such as turning on the heater are made.

[0397] Step 4:

[0398] Calculates crop yield based on optimized conditions. The server calculates predicted yield based on adjusted environmental conditions. Yield is predicted using a simple model using the average and standard values ​​of each environmental parameter.

[0399] Step 5:

[0400] Temporarily save the calculation results. The server saves the calculated crop yields and optimized environmental conditions in a database or temporary memory, which makes it easier to refer to the results or recalculate later.

[0401] Step 6:

[0402] The results are output and displayed to the user. The server outputs the optimized environmental conditions and predicted crop yields, which are displayed on the user's device. The user can use the displayed information to plan the next cultivation steps and adjustments.

[0403] Step 7:

[0404] Gathering user feedback and tuning the system. The user checks the results and sends feedback to the server as needed. The server then readjusts the system parameters based on this feedback and applies it to the next data collection and optimization process.

[0405] Example 1

[0406] 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."

[0407] To achieve efficient and sustainable food production in urban agriculture, detailed adjustment and management of the cultivation environment is necessary. With conventional methods, collecting environmental data and optimizing cultivation conditions based on that data is time-consuming and labor-intensive, and the results are often unstable. To solve these problems, a new integrated system is needed.

[0408] 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.

[0409] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for adjusting each parameter using the collected environmental data to keep it within an appropriate range, and means for constructing and predicting a yield model based on the adjusted environmental data, thereby enabling detailed adjustment and management of the cultivation environment to be performed efficiently and effectively.

[0410] A "sensor" is a measuring device for collecting environmental data.

[0411] "Environmental data" is information that indicates the state of the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0412] A "server" is a device that processes environmental data collected from sensors and performs calculations to optimize cultivation conditions.

[0413] "Growth conditions" refers to the optimal range of environmental data required to support proper plant growth.

[0414] "Optimization" refers to the process of adjusting growing conditions to fall within ideal ranges based on collected environmental data.

[0415] "Yield" is a value that indicates the expected production of a crop under specific cultivation conditions.

[0416] A "yield model" is a mathematical or statistical model for calculating predicted crop yields based on environmental data.

[0417] "Output" refers to the processing to provide the final environmental conditions and predicted yields to the user.

[0418] "Adjustment" refers to the operation and control of various devices to keep each parameter of environmental data within an appropriate range.

[0419] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system collects environmental data from sensors, automatically adjusts optimal cultivation conditions, and predicts crop yields. This section describes a specific embodiment of the system.

[0420] First, the system is equipped with various sensors to measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. These sensors collect data in real time and send it to a server. The server receives this environmental data and performs the necessary calculations and data processing.

[0421] The server first analyzes the collected environmental data and checks whether each parameter is within the specified range. For example, assume the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this data, the server checks whether the temperature is within the appropriate range and makes adjustments as necessary. Because the humidity is below 50%, it turns on the humidifier and sets it to increase the humidity by 1%. The light intensity and nutrient solution concentration are also adjusted to fall within their respective optimal ranges.

[0422] The server then calculates the yield based on the adjusted environmental conditions. For example, it uses average values ​​for temperature, humidity, light intensity, nutrient solution concentration, and soil moisture to build a yield model and calculate a predicted yield. This yield model can be refined using collected data. In a specific example, after all parameters are adjusted, the yield is calculated as 36.8.

[0423] Finally, the server outputs the calculated environmental conditions and predicted yield to the user. The user can use this information to plan their next cultivation. For example, the following prompt sentence can be input into the generative AI model and used.

[0424] Example prompts

[0425] In an AI-integrated vertical farming system, current environmental data collected from sensors shows the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this, set the optimal cultivation conditions and predict the yield that will result from increasing humidity by 1%. Also, output the environmental conditions and predicted yield after setting the settings.

[0426] In this way, this system realizes efficient and sustainable vertical farming using various sensors connected to a server and automated optimization algorithms. This system can be adapted to different urban environments and cultivation conditions, making it versatile.

[0427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0428] Step 1:

[0429] Environmental data collection

[0430] The server collects environmental data from sensors within the vertical farming system.

[0431] Input: Measurement data from each sensor (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0432] Data processing: The server receives data from various sensors in real time, organizes it, and records it.

[0433] Output: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%).

[0434] Specific operation: The server receives data of 22.5°C from the temperature sensor, data of 48% from the humidity sensor, and collects data from other sensors in the same way.

[0435] Step 2:

[0436] Optimizing cultivation conditions

[0437] Cultivation conditions are optimized based on the environmental data collected by the server.

[0438] Input: Collected environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0439] Data calculation: The server checks each parameter and calculates the adjustments to keep it within the specified range (e.g., temperature 20°C to 28°C, humidity 50% to 60%, etc.).

[0440] Output: Optimized cultivation environment conditions (e.g., temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%).

[0441] Specific operation: The server checks that the temperature is within the appropriate range at 22.5°C, and the humidity is below the lower limit of the appropriate range at 48%, so it activates the humidifier to adjust the humidity to 50%. It also optimizes other parameters in the same way.

[0442] Step 3:

[0443] Yield calculation

[0444] The server calculates the crop yield based on the optimized cultivation conditions.

[0445] Input: Optimized environmental condition data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0446] Data calculation: The server applies the yield model and predicts the yield based on the average value of each parameter.

[0447] Output: Predicted yield (e.g. 36.8).

[0448] Specific operation: The server calculates the yield model using data for temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, and soil moisture 30%, and predicts a yield of 36.8.

[0449] Step 4:

[0450] Output of results

[0451] The server outputs the final environmental conditions and crop yields to the user.

[0452] Input: Optimized environmental condition data and predicted yield.

[0453] Data processing: The server organizes the adjusted environmental conditions and predicted yields and converts them into a format that is easy for users to understand.

[0454] Output: Displayed on the user interface (optimized environmental conditions and predicted yield).

[0455] Specific operation: The server formats the data to display the adjusted temperature of 22.5°C, humidity of 50%, light intensity of 75%, nutrient solution concentration of 1.4 mL / L, soil humidity of 30%, and predicted yield of 36.8, and outputs it to the user interface.

[0456] In this way, efficient and sustainable vertical farming is achieved through data collection, processing, and output at each step.

[0457] (Application example 1)

[0458] 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."

[0459] Modern urban agriculture requires efficient and sustainable food production, but conventional systems have difficulty optimizing environmental conditions in real time and planning harvests and shipments. Current technology only optimizes conditions using data from individual sensors, and does not automate yield predictions or shipment plans. This leads to a lack of efficiency and accuracy in production and waste of resources.

[0460] 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.

[0461] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for monitoring the collected environmental data in real time, and means for automatically creating harvest and shipping plans based on the optimized cultivation conditions, thereby enabling efficient and sustainable food production in urban agriculture, reducing resource waste, and automatically optimizing harvest and shipping plans.

[0462] A "sensor" is a device that measures and collects environmental data.

[0463] "Environmental data" includes data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0464] "Cultivation conditions" refer to the environmental conditions necessary for crops to grow.

[0465] "Optimization" refers to adjusting to the most desirable state under given conditions and resources.

[0466] "Crop yield" is the total production of a crop grown for a particular period and under particular conditions.

[0467] "Collecting" refers to the act of obtaining data using sensors or other devices.

[0468] "Monitoring" "collected environmental data" means constantly observing it and, if necessary, recording and analyzing it.

[0469] "Real-time" refers to reacting immediately to the current time in progress.

[0470] "Automatically creating a harvest and shipping plan based on optimized cultivation conditions" means automatically setting a harvest and shipping schedule in accordance with optimized cultivation conditions.

[0471] To implement this invention, a vertical farming system installed in a distribution center, a server for managing the system, and a mobile device are required. The system collects environmental data from sensors, optimizes cultivation conditions based on the data, predicts yield, and automatically creates harvest and shipping plans.

[0472] Hardware and software used

[0473] Hardware:

[0474] Sensors: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor

[0475] Smart devices: smartphones, head-mounted displays (e.g. HoloLens)

[0476] Robot: Autonomous Guided Vehicle (AGV)

[0477] software:

[0478] Data analysis libraries: NumPy, scikit-learn

[0479] Program execution environment: Python

[0480] Smartphone app: Kotlin (for Android), Swift (for iOS)

[0481] System Operation

[0482] 1. Data Collection:

[0483] The server collects real-time data from various sensors on temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which serves as the basis for optimizing the growing environment for crops.

[0484] 2. Data optimization:

[0485] The server analyzes the collected environmental data and adjusts each parameter to suit the optimal cultivation conditions, thereby providing the optimal environment for crop growth.

[0486] 3. Yield prediction:

[0487] The server predicts crop yields based on the optimized data, using a yield model.

[0488] 4. Automated shipping planning:

[0489] Based on the optimized data and yield forecasts, the server automatically creates harvest and shipping plans that are dynamically updated based on data collected in real time.

[0490] Specific examples

[0491] For example, suppose the data collected by the server in the morning of a certain day is a temperature of 24°C, humidity of 58%, light intensity of 76%, nutrient solution concentration of 1.4 mL / L, and soil moisture of 36%. Based on this data, the server optimizes the environmental conditions and calculates a predicted yield of 36.8 kg. It then automatically creates a shipping plan based on the expected harvest date and prepares for delivery.

[0492] Prompt Sentence Examples

[0493] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[0494] In this way, this invention enables efficient and sustainable food production in urban agriculture within logistics centers, minimizing resource waste and optimizing cultivation conditions by automating harvesting and shipping planning.

[0495] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0496] Step 1:

[0497] The server collects environmental data from various sensors. Specifically, it is equipped with temperature, humidity, light intensity, nutrient solution concentration, and soil humidity sensors, and these sensors collect data in real time and send it to the server. The input is the real-time data (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) obtained from each sensor, and the output is environmental data that is a unified version of this data.

[0498] Step 2:

[0499] The server optimizes cultivation conditions based on the collected environmental data. Specifically, it runs an algorithm that adjusts each parameter (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) to fall within a specific range. The input is the collected environmental data, and the output is the adjusted environmental data. If the temperature is 24°C and the humidity is 58%, it checks whether they are within the optimal range and adjusts the environmental conditions as necessary.

[0500] Step 3:

[0501] The server reconfirms the cultivation conditions using the optimized environmental data. Specifically, it evaluates whether the optimized environmental data meets the ideal cultivation conditions and makes further fine adjustments if necessary. The input is the optimized environmental data, and the output is the environmental data after reconfirmation and fine adjustment.

[0502] Step 4:

[0503] The server predicts crop yields based on the optimized environmental data. Specifically, it uses a yield prediction model to calculate yields using each optimized parameter as input data. This yield prediction model uses, for example, a machine learning algorithm (such as Linear Regression). The input is the optimized environmental data, and the output is the predicted yield.

[0504] Step 5:

[0505] The server automatically creates harvest and shipping plans based on the yield prediction results. Specifically, it determines the harvest date and time based on the predicted yield and even sets the subsequent shipping plan. It also includes a process that uses autonomous vehicles (AGVs) to automatically prepare for harvest and shipping. The input is the predicted yield and the current schedule, and the output is the harvest and shipping plan.

[0506] Step 6:

[0507] The server or terminal displays the final environmental conditions, predicted yield, and harvest and shipping plans on the user's device. Specifically, data is sent to a smartphone or head-mounted display, allowing the user to check the current situation in real time. The input is the final environmental data, predicted yield, and harvest and shipping plans, and the output is the terminal screen displaying this information.

[0508] Prompt Sentence Examples

[0509] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[0510] 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.

[0511] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. It also incorporates an emotion engine to provide feedback based on the user's emotions, improving the user experience. A specific embodiment of this system is described below.

[0512] Program Description

[0513] 1. Data collection

[0514] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, providing a detailed understanding of the current state of the cultivation environment.

[0515] 2. Data storage

[0516] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[0517] 3. Optimizing cultivation conditions

[0518] The server optimizes settings such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity based on the collected environmental data. For example, if the temperature is outside the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity will also be adjusted to stay within the optimal range.

[0519] 4. Yield Calculation

[0520] The server calculates the crop yield based on the optimized cultivation conditions by predicting the yield using a simple model that uses the average and standard values ​​of each environmental parameter.

[0521] 5. Saving the results

[0522] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory, which makes it easy to refer to the results or perform recalculations later.

[0523] 6. Outputting the results

[0524] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. The user can then use the displayed information to plan their next cultivation or adjustment work.

[0525] 7. Running the Emotion Engine

[0526] The server collects the user's voice and facial expression data from the user's device and analyzes it using an emotion engine, which then recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[0527] 8. Providing emotional feedback

[0528] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the system displays encouraging messages or helpful advice for farming operations.

[0529] Specific examples

[0530] Example of processing time per day

[0531] Data collection

[0532] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[0533] Optimization process

[0534] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[0535] Yield calculation

[0536] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[0537] Result output

[0538] The server outputs the adjusted environmental conditions and yields and displays them on the user's terminal, allowing the user to check them and plan the next cultivation steps or adjustment work.

[0539] emotion recognition

[0540] The server analyzes the user's voice and facial expressions, and uses an emotion engine to recognize whether the user is satisfied or dissatisfied with the system's results.

[0541] Emotional Feedback

[0542] If the user is feeling stressed, the server will display positive feedback such as "You're doing well today! You're almost there!"

[0543] In this way, the system of the present invention can realize efficient and sustainable vertical farming, and can also improve the user experience by providing a feedback function based on the user's emotions.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] The server collects environmental data from sensors installed in the vertical farming system, which measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, and sends the data to the server.

[0547] Step 2:

[0548] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[0549] Step 3:

[0550] The server uses the collected data to check whether each cultivation condition (temperature, humidity, light intensity, nutrient solution concentration, soil humidity) is within the set range. For example, if the temperature is below 20°C, it will make adjustments such as turning on the heater.

[0551] Step 4:

[0552] The server adjusts environmental conditions as needed to set optimal growing conditions, such as increasing humidity or adjusting light intensity, to ensure all conditions are within the optimum range.

[0553] Step 5:

[0554] The server calculates the crop yield based on the optimized cultivation conditions by using a simple model to predict the yield using the average and standard values ​​of each environmental parameter.

[0555] Step 6:

[0556] The server stores the calculated crop yields and optimized environmental conditions in a database or temporary memory, which facilitates future reference and recalculation of the results.

[0557] Step 7:

[0558] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. Based on the displayed information, the user can plan their next cultivation and adjustment work.

[0559] Step 8:

[0560] The server collects the user's voice and facial expression data from the user's device, and uses an emotion engine to recognize the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[0561] Step 9:

[0562] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the server displays encouraging messages or helpful advice for farming operations.

[0563] Step 10:

[0564] Users can review the results and feedback, and then take the next steps or make adjustments, improving their cultivation efficiency and satisfaction.

[0565] Example 2

[0566] 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."

[0567] To achieve efficient and sustainable food production in urban agriculture, it is necessary to collect and manage detailed environmental data and maintain appropriate cultivation conditions. However, conventional systems require a complicated process for collecting environmental data, and adjustments to maintain optimal cultivation conditions are labor-intensive. Another issue is the lack of feedback functions to improve the user's farming experience.

[0568] 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.

[0569] In this invention, the server includes means for collecting environmental data from sensors, means for storing the collected environmental data in a database, means for optimizing cultivation conditions based on the collected environmental data, means for calculating a crop yield based on the optimized cultivation conditions, means for storing the optimized cultivation conditions and the calculated crop yield in a database, means for outputting the calculated crop yield and displaying it on a user terminal, means for collecting emotion data to analyze user emotions, and means for providing feedback based on the emotion data, thereby enabling efficient and sustainable cultivation and providing a feedback function that improves the user experience.

[0570] A "sensor" is a device for collecting environmental data, and is capable of measuring parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0571] "Environmental data" refers to various data that indicate the plant cultivation environment, and specifically includes information such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0572] A "database" is a system for systematically storing and managing large amounts of data, and is capable of efficiently storing, searching, and updating data.

[0573] "Optimization means" refers to algorithms and control systems that analyze collected environmental data and maintain optimal cultivation conditions.

[0574] "Yield" is an indicator of the amount of harvested crops grown and is predicted based on optimized environmental conditions.

[0575] "Emotion data" is data collected from the user's voice, facial expressions, etc., and is basic data for analyzing the user's emotional state.

[0576] A "means for providing feedback" is a system or function for providing a corresponding message or advice to a user based on the analyzed emotional data of the user.

[0577] A "user terminal" is a device used by a user to receive information, and includes electronic devices such as smartphones, tablets, and personal computers.

[0578] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system integrates means for collecting, storing, and analyzing environmental data, and means for providing feedback based on user emotions.

[0579] Hardware and Software Configuration

[0580] server:

[0581] The server plays a central role in data collection, data storage, data analysis, environmental control, and feedback provision. Specific functions are as follows:

[0582] sensor:

[0583] The sensors used are temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, each connected to a data collection device such as a Raspberry Pi.

[0584] Database:

[0585] The database uses MySQL and stores collected environmental data, optimized cultivation conditions, and yield predictions.

[0586] Analysis and optimization software:

[0587] The analysis and optimization software is implemented using Python, and the data science library scikit-learn is used for the optimization algorithms.

[0588] Emotion Engine:

[0589] The Emotion API from Microsoft Azure is used to analyze user emotions, and emotional states are analyzed based on voice and facial expression data.

[0590] User device:

[0591] User terminals are electronic devices such as smartphones, tablets, and personal computers. These terminals receive data from the server and display it to the user.

[0592] Specific processing flow

[0593] First, the server periodically collects environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture) from sensors, and stores the collected data in a database.

[0594] The server then uses Python and scikit-learn to calculate optimal growing conditions based on the collected data. For example, if the temperature is outside the set range (20°C to 28°C), the temperature control system will automatically adjust. Similarly, humidity, light intensity, nutrient solution concentration, and soil moisture will also be optimized.

[0595] Using the optimized conditions, the server predicts crop yields by building a predictive model based on past data and current environmental conditions, and then calculating yields. The results are then stored in a database.

[0596] The saved optimization conditions and yield information are sent to the user's device and displayed to the user. Specific display information could be "Temperature: 22.5°C, Humidity: 49%, Light Intensity: 70%, Predicted Yield: 36.8".

[0597] Furthermore, the server collects voice and facial expression data from the user's smartphone or computer and sends it to the Emotion API to analyze the user's emotions. Based on the analysis results, the system provides positive feedback, such as "You're doing well today! You're almost there!"

[0598] Specific examples

[0599] Example prompt sentence:

[0600] 1. Collect from the sensors the current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%.

[0601] 2. The server checks the temperature and determines that it is within the normal range. At the same time, it determines that the humidity is below 50% and issues a command to use a humidifier to increase the humidity by 1%.

[0602] 3. Based on the collected data, the yield is calculated using the optimized environmental conditions. The yield is predicted using a linear regression model in scikit-learn.

[0603] 4. The server stores the predicted yield of 36.8 and the optimized environmental conditions in a database and displays them on the user's terminal.

[0604] 5. Send the user's facial expressions and voice data to the Emotion API to analyze their emotional state. If the user is feeling stressed, provide positive feedback.

[0605] In this way, by combining detailed collection and management of environmental data, optimization of cultivation conditions, calculation of predicted yields, and feedback based on user emotions, the system of the present invention can achieve efficient and sustainable food production in urban agriculture while also improving the user experience.

[0606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0607] Step 1: Collect data

[0608] Description: The server periodically collects environmental data from various sensors (temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor) installed in the vertical farming system.

[0609] How it works: The server obtains the current temperature data (e.g., 22.5°C) from the temperature sensor connected to the Raspberry Pi. Similarly, it obtains the humidity (e.g., 48%), light intensity (e.g., 75%), nutrient solution concentration (e.g., 1.4 mL / L), and soil moisture (e.g., 30%) from other sensors.

[0610] Input: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil humidity sensor

[0611] Output: Temperature, humidity, light intensity, nutrient solution concentration, soil humidity data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[0612] Step 2: Save your data

[0613] Description: The server stores the collected environmental data in a database.

[0614] Specific operation: The server saves the collected data of temperature (22.5°C), humidity (48%), light intensity (75%), nutrient solution concentration (1.4 mL / L), and soil moisture (30%) in a MySQL database using INSERT statements.

[0615] Input: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%)

[0616] Output: Environmental data stored in a database

[0617] Step 3: Optimizing cultivation conditions

[0618] Description: The server optimizes the cultivation conditions based on the collected environmental data. It uses an optimization algorithm written in Python.

[0619] What happens: The server passes the collected data to a Python script, which verifies that the temperature is within the normal range at 22.5°C. The humidity is out of range at 48%, so it issues a command to use a humidifier to increase the humidity by 1%. It then adjusts other parameters in the same way.

[0620] Input: Environmental data stored in the database (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[0621] Output: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0622] Step 4: Calculate yield

[0623] Description: The server calculates crop yields based on optimized cultivation conditions. It uses the data science library scikit-learn.

[0624] How it works: The server inputs the optimized environmental conditions into a linear regression model in scikit-learn to calculate the crop yield. For example, based on the optimized temperature of 22.5°C, humidity of 49%, light intensity of 70%, nutrient solution concentration of 1.3 mL / L, and soil moisture of 28%, the predicted yield is calculated as 36.8.

[0625] Input: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0626] Output: Expected yield (e.g. 36.8)

[0627] Step 5: Save the results

[0628] Description: The server stores the calculated crop yields and optimized cultivation conditions in a database.

[0629] Specific operation: The server uses an INSERT statement to save the predicted yield of 36.8 and the optimized environmental conditions data into the MySQL database.

[0630] Input: Predicted yield and optimized cultivation conditions (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0631] Output: Predicted yield and optimized cultivation conditions stored in a database

[0632] Step 6: Output the results

[0633] Description: The server displays the optimized environmental conditions and crop yields on the user's device.

[0634] Specific operation: The server uses HTML and JavaScript to generate a web page containing the optimized data (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, predicted yield 36.8), sends it to the user's device, and displays it in the browser.

[0635] Input: Predicted yield and optimized cultivation conditions stored in the database (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0636] Output: Environmental conditions and predicted yield displayed on the user's terminal

[0637] Step 7: Run the Emotion Engine

[0638] Description: The server passes the voice and facial expression data collected from the user's device to the emotion engine and analyzes the user's emotional state.

[0639] How it works: When a user is checking the displayed information on their smartphone, the camera and microphone collect the user's facial expressions and voice, and send this data to the server. The server then passes this data to the Emotion API for analysis.

[0640] Input: Voice data, facial expression data (e.g., user's facial expressions and voice)

[0641] Output: Parsed emotional state (e.g., stressed, satisfied, dissatisfied)

[0642] Step 8: Provide emotional feedback

[0643] Description: The server customizes results and provides feedback based on the user's emotional state as recognized by the emotion engine.

[0644] Specific operation: If the server recognizes that the user is feeling stressed, it generates a positive message such as "Today's work is going well! You're almost there!" and displays it on the user's device.

[0645] Input: Parsed emotional state (e.g., stress)

[0646] Output: A customized feedback message (e.g., "You're doing great today! You're almost there!")

[0647] (Application example 2)

[0648] 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."

[0649] Conventional vertical farming systems achieve efficient cultivation through the collection and optimization of environmental data, but no systems have taken user emotions and feedback into consideration. This has resulted in a lack of improvement in the user experience and limitations in sustainable agricultural operations. There is a need for more personalized support that reflects the emotions users feel about system operation and results.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0651] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for collecting user emotion data, and means for analyzing the collected emotion data and providing feedback, thereby realizing efficient and sustainable vertical farming and providing feedback based on the user's emotions.

[0652] A "sensor" is a device for measuring environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0653] "Environmental data" refers to information related to the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0654] "Cultivation conditions" refer to the settings of temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which are environmental parameters optimal for crop growth.

[0655] "Yield" refers to the amount of crop produced within a specific period of time.

[0656] "Emotion data" is information about the emotional state obtained by analyzing the user's facial expressions and voice.

[0657] An "optimization means" is a process or device that adjusts cultivation conditions within set ranges based on collected environmental data.

[0658] The "collection means" is a process or device that acquires environmental data and user emotion data using sensors or the like.

[0659] An "analysis means" is a process or device for processing acquired data and extracting useful information.

[0660] A "feedback means" is a process or device that provides appropriate messages or advice based on the user's emotional data.

[0661] The "system" is a comprehensive device that combines the above sensors, collection means, optimization means, analysis means and feedback means to manage vertical farming and provide user support.

[0662] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable urban agriculture. The invention improves the user experience by incorporating a function to collect user emotion data and provide feedback based on that data.

[0663] First, the system is equipped with multiple sensors. These sensors periodically collect environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. This allows for a detailed understanding of the current cultivation environment. The collected data is sent to a server and stored in a database or temporary memory.

[0664] The server uses AI to optimize cultivation conditions based on the collected environmental data. The AI ​​optimization engine adjusts parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain the optimal cultivation environment. For example, if the temperature deviates from the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity are also controlled to stay within their respective optimal ranges.

[0665] The server then predicts crop yields based on the optimized cultivation conditions. Using a prediction model, it calculates yields based on the average and standard values ​​of each environmental parameter. The calculated yields and optimized cultivation conditions are stored in a database or temporary memory, allowing for future reference and recalculation.

[0666] Furthermore, the system has the ability to collect and analyze the user's emotional data. The server collects voice and facial expression data from the user's device and analyzes it using an emotion engine. This emotion engine recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.) and customizes the information output based on the results. For example, if the user is feeling stressed, the system will provide encouraging messages and helpful advice.

[0667] Specific examples

[0668] The following is an example of a day's processing. Sensors collect the following information: current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%. The server uses this data to determine whether the temperature is appropriate and adjusts the humidity to approach 50%. Light intensity and nutrient solution concentration are also adjusted to their optimal ranges. Crop yield is predicted based on this data; for example, a yield of 36.8% is calculated. The user's emotional data is then collected and analyzed by the emotion engine. If the system recognizes that the user is satisfied with the system's results, no special feedback is provided. However, if the user is feeling stressed, a positive message such as "Today's work is going well! You're almost there!" is displayed.

[0669] Prompt Sentence Examples

[0670] For example, you could use the prompt "If the temperature is 30°C, what can you do to keep it within the optimal range?"

[0671] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0672] Step 1:

[0673] The server collects environmental data from sensors installed in the vertical farming system. The sensors include temperature, humidity, light, nutrient solution concentration, and soil moisture sensors. The data measured by these sensors (input) is sent to the server and stored in a database or temporary memory in real time (output).

[0674] Step 2:

[0675] The server analyzes the collected environmental data and determines the current state of the cultivation environment. Input data such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are compared with a set optimal range (e.g., temperature: 20°C to 28°C). The server outputs the comparison result (e.g., temperature outside the optimal range) and generates instructions to adjust the situation.

[0676] Step 3:

[0677] The server uses an AI optimization engine to automatically optimize cultivation conditions based on collected environmental data. Based on the input data, temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are adjusted to fall within optimal ranges (for example, if the temperature is 30°C, the cooling system is activated and set to 22°C). The optimized environmental conditions (output) after adjustment are stored in a database.

[0678] Step 4:

[0679] The server predicts crop yields based on the optimized cultivation conditions. The yield prediction model uses average or standard values ​​for the optimized temperature, humidity, light intensity, nutrient solution concentration, and soil moisture as input data. The prediction algorithm processes these parameters and outputs a predicted yield (e.g., a yield of 36.8).

[0680] Step 5:

[0681] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory. The stored data (input) is prepared so that it can be accessed from the user's device. This allows the user to plan cultivation and adjustment work based on the stored data (output).

[0682] Step 6:

[0683] The server collects user voice and facial expression data from the user's device. The device's built-in camera and microphone capture the user's facial expressions and voice and send them to the server as input data. The emotion analysis engine analyzes this data and detects the user's emotional state (e.g., satisfaction, dissatisfaction, stress) (output).

[0684] Step 7:

[0685] The server provides feedback based on the user's emotions recognized by the emotion analysis engine. The result of the emotion analysis is used as input to generate a feedback message (e.g., "You're doing well today! You're almost there!") that corresponds to the user's emotional state. This message is then sent to the user's device and displayed (output).

[0686] Step 8:

[0687] The user checks the feedback messages and the latest cultivation environment information provided by the server. For example, based on information such as whether the temperature is within the appropriate range or the predicted yield is 36.8, the user can plan the next cultivation step or adjustment work (based on the confirmed data as input, a specific work plan is created as output).

[0688] 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.

[0689] 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.

[0690] 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.

[0691] [Third embodiment]

[0692] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0693] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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).

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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."

[0704] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. Specific embodiments of this system are described below.

[0705] Program Description

[0706] 1. Data collection

[0707] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, thereby accurately determining the current state of the growing environment.

[0708] 2. Running the optimization algorithm

[0709] The server optimizes growing conditions based on the collected environmental data. For example, if the temperature deviates from the specified range (20°C to 28°C), it adjusts it accordingly. The same goes for humidity, light intensity, nutrient solution concentration, and soil moisture, adjusting each parameter to stay within the optimal range.

[0710] 3. Yield Calculation

[0711] The server calculates the crop yield based on the optimized cultivation conditions by building a yield model using the average values ​​of each parameter and predicting the yield.

[0712] 4. Outputting the results

[0713] The server calculates and outputs the final environmental conditions and crop yield, allowing users to check the current cultivation conditions and predicted crop yield.

[0714] Specific examples

[0715] Example of processing time per day

[0716] Data collection

[0717] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[0718] Optimization process

[0719] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[0720] Yield calculation

[0721] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[0722] Result output

[0723] The server outputs and displays the adjusted environmental conditions and yield to the user, who can then check them and plan their next cultivation.

[0724] Thus, the system of the present invention can realize efficient and sustainable vertical farming, and has wide applicability as it can be adapted to different urban environments and cultivation conditions.

[0725] The processing flow will be explained below.

[0726] Step 1:

[0727] Environmental data is collected from sensors. The server collects data in real time from various sensors installed in the vertical farming system (temperature sensors, humidity sensors, light sensors, nutrient solution sensors, soil moisture sensors), allowing the current environmental conditions to be understood.

[0728] Step 2:

[0729] Temporarily store collected data. The server stores the collected environmental data in a database or temporary memory. This storage process makes it easier to process and analyze the data later.

[0730] Step 3:

[0731] Various conditions are checked and optimized. Based on the collected data, the server checks whether temperature, humidity, light intensity, nutrient solution concentration, soil humidity, etc. are within the set ranges. If a condition is outside the range, adjustments are made to optimize each condition. For example, if the temperature is below 20°C, adjustments such as turning on the heater are made.

[0732] Step 4:

[0733] Calculates crop yield based on optimized conditions. The server calculates predicted yield based on adjusted environmental conditions. Yield is predicted using a simple model using the average and standard values ​​of each environmental parameter.

[0734] Step 5:

[0735] Temporarily save the calculation results. The server saves the calculated crop yields and optimized environmental conditions in a database or temporary memory, which makes it easier to refer to the results or recalculate later.

[0736] Step 6:

[0737] The results are output and displayed to the user. The server outputs the optimized environmental conditions and predicted crop yields, which are displayed on the user's device. The user can use the displayed information to plan the next cultivation steps and adjustments.

[0738] Step 7:

[0739] Gathering user feedback and tuning the system. The user checks the results and sends feedback to the server as needed. The server then readjusts the system parameters based on this feedback and applies it to the next data collection and optimization process.

[0740] Example 1

[0741] 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."

[0742] To achieve efficient and sustainable food production in urban agriculture, detailed adjustment and management of the cultivation environment is necessary. With conventional methods, collecting environmental data and optimizing cultivation conditions based on that data is time-consuming and labor-intensive, and the results are often unstable. To solve these problems, a new integrated system is needed.

[0743] 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.

[0744] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for adjusting each parameter using the collected environmental data to keep it within an appropriate range, and means for constructing and predicting a yield model based on the adjusted environmental data, thereby enabling detailed adjustment and management of the cultivation environment to be performed efficiently and effectively.

[0745] A "sensor" is a measuring device for collecting environmental data.

[0746] "Environmental data" is information that indicates the state of the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0747] A "server" is a device that processes environmental data collected from sensors and performs calculations to optimize cultivation conditions.

[0748] "Growth conditions" refers to the optimal range of environmental data required to support proper plant growth.

[0749] "Optimization" refers to the process of adjusting growing conditions to fall within ideal ranges based on collected environmental data.

[0750] "Yield" is a value that indicates the expected production of a crop under specific cultivation conditions.

[0751] A "yield model" is a mathematical or statistical model for calculating predicted crop yields based on environmental data.

[0752] "Output" refers to the processing to provide the final environmental conditions and predicted yields to the user.

[0753] "Adjustment" refers to the operation and control of various devices to keep each parameter of environmental data within an appropriate range.

[0754] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system collects environmental data from sensors, automatically adjusts optimal cultivation conditions, and predicts crop yields. This section describes a specific embodiment of the system.

[0755] First, the system is equipped with various sensors to measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. These sensors collect data in real time and send it to a server. The server receives this environmental data and performs the necessary calculations and data processing.

[0756] The server first analyzes the collected environmental data and checks whether each parameter is within the specified range. For example, assume the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this data, the server checks whether the temperature is within the appropriate range and makes adjustments as necessary. Because the humidity is below 50%, it turns on the humidifier and sets it to increase the humidity by 1%. The light intensity and nutrient solution concentration are also adjusted to fall within their respective optimal ranges.

[0757] The server then calculates the yield based on the adjusted environmental conditions. For example, it uses average values ​​for temperature, humidity, light intensity, nutrient solution concentration, and soil moisture to build a yield model and calculate a predicted yield. This yield model can be refined using collected data. In a specific example, after all parameters are adjusted, the yield is calculated as 36.8.

[0758] Finally, the server outputs the calculated environmental conditions and predicted yield to the user. The user can use this information to plan their next cultivation. For example, the following prompt sentence can be input into the generative AI model and used.

[0759] Example prompts

[0760] In an AI-integrated vertical farming system, current environmental data collected from sensors shows the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this, set the optimal cultivation conditions and predict the yield that will result from increasing humidity by 1%. Also, output the environmental conditions and predicted yield after setting the settings.

[0761] In this way, this system realizes efficient and sustainable vertical farming using various sensors connected to a server and automated optimization algorithms. This system can be adapted to different urban environments and cultivation conditions, making it versatile.

[0762] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0763] Step 1:

[0764] Environmental data collection

[0765] The server collects environmental data from sensors within the vertical farming system.

[0766] Input: Measurement data from each sensor (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0767] Data processing: The server receives data from various sensors in real time, organizes it, and records it.

[0768] Output: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%).

[0769] Specific operation: The server receives data of 22.5°C from the temperature sensor, data of 48% from the humidity sensor, and collects data from other sensors in the same way.

[0770] Step 2:

[0771] Optimizing cultivation conditions

[0772] Cultivation conditions are optimized based on the environmental data collected by the server.

[0773] Input: Collected environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0774] Data calculation: The server checks each parameter and calculates the adjustments to keep it within the specified range (e.g., temperature 20°C to 28°C, humidity 50% to 60%, etc.).

[0775] Output: Optimized cultivation environment conditions (e.g., temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%).

[0776] Specific operation: The server checks that the temperature is within the appropriate range at 22.5°C, and the humidity is below the lower limit of the appropriate range at 48%, so it activates the humidifier to adjust the humidity to 50%. It also optimizes other parameters in the same way.

[0777] Step 3:

[0778] Yield calculation

[0779] The server calculates the crop yield based on the optimized cultivation conditions.

[0780] Input: Optimized environmental condition data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[0781] Data calculation: The server applies the yield model and predicts the yield based on the average value of each parameter.

[0782] Output: Predicted yield (e.g. 36.8).

[0783] Specific operation: The server calculates the yield model using data for temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, and soil moisture 30%, and predicts a yield of 36.8.

[0784] Step 4:

[0785] Output of results

[0786] The server outputs the final environmental conditions and crop yields to the user.

[0787] Input: Optimized environmental condition data and predicted yield.

[0788] Data processing: The server organizes the adjusted environmental conditions and predicted yields and converts them into a format that is easy for users to understand.

[0789] Output: Displayed on the user interface (optimized environmental conditions and predicted yield).

[0790] Specific operation: The server formats the data to display the adjusted temperature of 22.5°C, humidity of 50%, light intensity of 75%, nutrient solution concentration of 1.4 mL / L, soil humidity of 30%, and predicted yield of 36.8, and outputs it to the user interface.

[0791] In this way, efficient and sustainable vertical farming is achieved through data collection, processing, and output at each step.

[0792] (Application example 1)

[0793] 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."

[0794] Modern urban agriculture requires efficient and sustainable food production, but conventional systems have difficulty optimizing environmental conditions in real time and planning harvests and shipments. Current technology only optimizes conditions using data from individual sensors, and does not automate yield predictions or shipment plans. This leads to a lack of efficiency and accuracy in production and waste of resources.

[0795] 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.

[0796] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for monitoring the collected environmental data in real time, and means for automatically creating harvest and shipping plans based on the optimized cultivation conditions, thereby enabling efficient and sustainable food production in urban agriculture, reducing resource waste, and automatically optimizing harvest and shipping plans.

[0797] A "sensor" is a device that measures and collects environmental data.

[0798] "Environmental data" includes data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0799] "Cultivation conditions" refer to the environmental conditions necessary for crops to grow.

[0800] "Optimization" refers to adjusting to the most desirable state under given conditions and resources.

[0801] "Crop yield" is the total production of a crop grown for a particular period and under particular conditions.

[0802] "Collecting" refers to the act of obtaining data using sensors or other devices.

[0803] "Monitoring" "collected environmental data" means constantly observing it and, if necessary, recording and analyzing it.

[0804] "Real-time" refers to reacting immediately to the current time in progress.

[0805] "Automatically creating a harvest and shipping plan based on optimized cultivation conditions" means automatically setting a harvest and shipping schedule in accordance with optimized cultivation conditions.

[0806] To implement this invention, a vertical farming system installed in a distribution center, a server for managing the system, and a mobile device are required. The system collects environmental data from sensors, optimizes cultivation conditions based on the data, predicts yield, and automatically creates harvest and shipping plans.

[0807] Hardware and software used

[0808] Hardware:

[0809] Sensors: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor

[0810] Smart devices: smartphones, head-mounted displays (e.g. HoloLens)

[0811] Robot: Autonomous Guided Vehicle (AGV)

[0812] software:

[0813] Data analysis libraries: NumPy, scikit-learn

[0814] Program execution environment: Python

[0815] Smartphone app: Kotlin (for Android), Swift (for iOS)

[0816] System Operation

[0817] 1. Data Collection:

[0818] The server collects real-time data from various sensors on temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which serves as the basis for optimizing the growing environment for crops.

[0819] 2. Data optimization:

[0820] The server analyzes the collected environmental data and adjusts each parameter to suit the optimal cultivation conditions, thereby providing the optimal environment for crop growth.

[0821] 3. Yield prediction:

[0822] The server predicts crop yields based on the optimized data, using a yield model.

[0823] 4. Automated shipping planning:

[0824] Based on the optimized data and yield forecasts, the server automatically creates harvest and shipping plans that are dynamically updated based on data collected in real time.

[0825] Specific examples

[0826] For example, suppose the data collected by the server in the morning of a certain day is a temperature of 24°C, humidity of 58%, light intensity of 76%, nutrient solution concentration of 1.4 mL / L, and soil moisture of 36%. Based on this data, the server optimizes the environmental conditions and calculates a predicted yield of 36.8 kg. It then automatically creates a shipping plan based on the expected harvest date and prepares for delivery.

[0827] Prompt Sentence Examples

[0828] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[0829] In this way, this invention enables efficient and sustainable food production in urban agriculture within logistics centers, minimizing resource waste and optimizing cultivation conditions by automating harvesting and shipping planning.

[0830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0831] Step 1:

[0832] The server collects environmental data from various sensors. Specifically, it is equipped with temperature, humidity, light intensity, nutrient solution concentration, and soil humidity sensors, and these sensors collect data in real time and send it to the server. The input is the real-time data (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) obtained from each sensor, and the output is environmental data that is a unified version of this data.

[0833] Step 2:

[0834] The server optimizes cultivation conditions based on the collected environmental data. Specifically, it runs an algorithm that adjusts each parameter (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) to fall within a specific range. The input is the collected environmental data, and the output is the adjusted environmental data. If the temperature is 24°C and the humidity is 58%, it checks whether they are within the optimal range and adjusts the environmental conditions as necessary.

[0835] Step 3:

[0836] The server reconfirms the cultivation conditions using the optimized environmental data. Specifically, it evaluates whether the optimized environmental data meets the ideal cultivation conditions and makes further fine adjustments if necessary. The input is the optimized environmental data, and the output is the environmental data after reconfirmation and fine adjustment.

[0837] Step 4:

[0838] The server predicts crop yields based on the optimized environmental data. Specifically, it uses a yield prediction model to calculate yields using each optimized parameter as input data. This yield prediction model uses, for example, a machine learning algorithm (such as Linear Regression). The input is the optimized environmental data, and the output is the predicted yield.

[0839] Step 5:

[0840] The server automatically creates harvest and shipping plans based on the yield prediction results. Specifically, it determines the harvest date and time based on the predicted yield and even sets the subsequent shipping plan. It also includes a process that uses autonomous vehicles (AGVs) to automatically prepare for harvest and shipping. The input is the predicted yield and the current schedule, and the output is the harvest and shipping plan.

[0841] Step 6:

[0842] The server or terminal displays the final environmental conditions, predicted yield, and harvest and shipping plans on the user's device. Specifically, data is sent to a smartphone or head-mounted display, allowing the user to check the current situation in real time. The input is the final environmental data, predicted yield, and harvest and shipping plans, and the output is the terminal screen displaying this information.

[0843] Prompt Sentence Examples

[0844] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[0845] 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.

[0846] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. It also incorporates an emotion engine to provide feedback based on the user's emotions, improving the user experience. A specific embodiment of this system is described below.

[0847] Program Description

[0848] 1. Data collection

[0849] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, providing a detailed understanding of the current state of the cultivation environment.

[0850] 2. Data storage

[0851] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[0852] 3. Optimizing cultivation conditions

[0853] The server optimizes settings such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity based on the collected environmental data. For example, if the temperature is outside the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity will also be adjusted to stay within the optimal range.

[0854] 4. Yield Calculation

[0855] The server calculates the crop yield based on the optimized cultivation conditions by predicting the yield using a simple model that uses the average and standard values ​​of each environmental parameter.

[0856] 5. Saving the results

[0857] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory, which makes it easy to refer to the results or perform recalculations later.

[0858] 6. Outputting the results

[0859] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. The user can then use the displayed information to plan their next cultivation or adjustment work.

[0860] 7. Running the Emotion Engine

[0861] The server collects the user's voice and facial expression data from the user's device and analyzes it using an emotion engine, which then recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[0862] 8. Providing emotional feedback

[0863] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the system displays encouraging messages or helpful advice for farming operations.

[0864] Specific examples

[0865] Example of processing time per day

[0866] Data collection

[0867] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[0868] Optimization process

[0869] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[0870] Yield calculation

[0871] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[0872] Result output

[0873] The server outputs the adjusted environmental conditions and yields and displays them on the user's terminal, allowing the user to check them and plan the next cultivation steps or adjustment work.

[0874] emotion recognition

[0875] The server analyzes the user's voice and facial expressions, and uses an emotion engine to recognize whether the user is satisfied or dissatisfied with the system's results.

[0876] Emotional Feedback

[0877] If the user is feeling stressed, the server will display positive feedback such as "You're doing well today! You're almost there!"

[0878] In this way, the system of the present invention can realize efficient and sustainable vertical farming, and can also improve the user experience by providing a feedback function based on the user's emotions.

[0879] The processing flow will be explained below.

[0880] Step 1:

[0881] The server collects environmental data from sensors installed in the vertical farming system, which measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, and sends the data to the server.

[0882] Step 2:

[0883] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[0884] Step 3:

[0885] The server uses the collected data to check whether each cultivation condition (temperature, humidity, light intensity, nutrient solution concentration, soil humidity) is within the set range. For example, if the temperature is below 20°C, it will make adjustments such as turning on the heater.

[0886] Step 4:

[0887] The server adjusts environmental conditions as needed to set optimal growing conditions, such as increasing humidity or adjusting light intensity, to ensure all conditions are within the optimum range.

[0888] Step 5:

[0889] The server calculates the crop yield based on the optimized cultivation conditions by using a simple model to predict the yield using the average and standard values ​​of each environmental parameter.

[0890] Step 6:

[0891] The server stores the calculated crop yields and optimized environmental conditions in a database or temporary memory, which facilitates future reference and recalculation of the results.

[0892] Step 7:

[0893] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. Based on the displayed information, the user can plan their next cultivation and adjustment work.

[0894] Step 8:

[0895] The server collects the user's voice and facial expression data from the user's device, and uses an emotion engine to recognize the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[0896] Step 9:

[0897] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the server displays encouraging messages or helpful advice for farming operations.

[0898] Step 10:

[0899] Users can review the results and feedback, and then take the next steps or make adjustments, improving their cultivation efficiency and satisfaction.

[0900] Example 2

[0901] 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."

[0902] To achieve efficient and sustainable food production in urban agriculture, it is necessary to collect and manage detailed environmental data and maintain appropriate cultivation conditions. However, conventional systems require a complicated process for collecting environmental data, and adjustments to maintain optimal cultivation conditions are labor-intensive. Another issue is the lack of feedback functions to improve the user's farming experience.

[0903] 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.

[0904] In this invention, the server includes means for collecting environmental data from sensors, means for storing the collected environmental data in a database, means for optimizing cultivation conditions based on the collected environmental data, means for calculating a crop yield based on the optimized cultivation conditions, means for storing the optimized cultivation conditions and the calculated crop yield in a database, means for outputting the calculated crop yield and displaying it on a user terminal, means for collecting emotion data to analyze user emotions, and means for providing feedback based on the emotion data, thereby enabling efficient and sustainable cultivation and providing a feedback function that improves the user experience.

[0905] A "sensor" is a device for collecting environmental data, and is capable of measuring parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0906] "Environmental data" refers to various data that indicate the plant cultivation environment, and specifically includes information such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0907] A "database" is a system for systematically storing and managing large amounts of data, and is capable of efficiently storing, searching, and updating data.

[0908] "Optimization means" refers to algorithms and control systems that analyze collected environmental data and maintain optimal cultivation conditions.

[0909] "Yield" is an indicator of the amount of harvested crops grown and is predicted based on optimized environmental conditions.

[0910] "Emotion data" is data collected from the user's voice, facial expressions, etc., and is basic data for analyzing the user's emotional state.

[0911] A "means for providing feedback" is a system or function for providing a corresponding message or advice to a user based on the analyzed emotional data of the user.

[0912] A "user terminal" is a device used by a user to receive information, and includes electronic devices such as smartphones, tablets, and personal computers.

[0913] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system integrates means for collecting, storing, and analyzing environmental data, and means for providing feedback based on user emotions.

[0914] Hardware and Software Configuration

[0915] server:

[0916] The server plays a central role in data collection, data storage, data analysis, environmental control, and feedback provision. Specific functions are as follows:

[0917] sensor:

[0918] The sensors used are temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, each connected to a data collection device such as a Raspberry Pi.

[0919] Database:

[0920] The database uses MySQL and stores collected environmental data, optimized cultivation conditions, and yield predictions.

[0921] Analysis and optimization software:

[0922] The analysis and optimization software is implemented using Python, and the data science library scikit-learn is used for the optimization algorithms.

[0923] Emotion Engine:

[0924] The Emotion API from Microsoft Azure is used to analyze user emotions, and emotional states are analyzed based on voice and facial expression data.

[0925] User device:

[0926] User terminals are electronic devices such as smartphones, tablets, and personal computers. These terminals receive data from the server and display it to the user.

[0927] Specific processing flow

[0928] First, the server periodically collects environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture) from sensors, and stores the collected data in a database.

[0929] The server then uses Python and scikit-learn to calculate optimal growing conditions based on the collected data. For example, if the temperature is outside the set range (20°C to 28°C), the temperature control system will automatically adjust. Similarly, humidity, light intensity, nutrient solution concentration, and soil moisture will also be optimized.

[0930] Using the optimized conditions, the server predicts crop yields by building a predictive model based on past data and current environmental conditions, and then calculating yields. The results are then stored in a database.

[0931] The saved optimization conditions and yield information are sent to the user's device and displayed to the user. Specific display information could be "Temperature: 22.5°C, Humidity: 49%, Light Intensity: 70%, Predicted Yield: 36.8".

[0932] Furthermore, the server collects voice and facial expression data from the user's smartphone or computer and sends it to the Emotion API to analyze the user's emotions. Based on the analysis results, the system provides positive feedback, such as "You're doing well today! You're almost there!"

[0933] Specific examples

[0934] Example prompt sentence:

[0935] 1. Collect from the sensors the current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%.

[0936] 2. The server checks the temperature and determines that it is within the normal range. At the same time, it determines that the humidity is below 50% and issues a command to use a humidifier to increase the humidity by 1%.

[0937] 3. Based on the collected data, the yield is calculated using the optimized environmental conditions. The yield is predicted using a linear regression model in scikit-learn.

[0938] 4. The server stores the predicted yield of 36.8 and the optimized environmental conditions in a database and displays them on the user's terminal.

[0939] 5. Send the user's facial expressions and voice data to the Emotion API to analyze their emotional state. If the user is feeling stressed, provide positive feedback.

[0940] In this way, by combining detailed collection and management of environmental data, optimization of cultivation conditions, calculation of predicted yields, and feedback based on user emotions, the system of the present invention can achieve efficient and sustainable food production in urban agriculture while also improving the user experience.

[0941] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0942] Step 1: Collect data

[0943] Description: The server periodically collects environmental data from various sensors (temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor) installed in the vertical farming system.

[0944] How it works: The server obtains the current temperature data (e.g., 22.5°C) from the temperature sensor connected to the Raspberry Pi. Similarly, it obtains the humidity (e.g., 48%), light intensity (e.g., 75%), nutrient solution concentration (e.g., 1.4 mL / L), and soil moisture (e.g., 30%) from other sensors.

[0945] Input: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil humidity sensor

[0946] Output: Temperature, humidity, light intensity, nutrient solution concentration, soil humidity data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[0947] Step 2: Save your data

[0948] Description: The server stores the collected environmental data in a database.

[0949] Specific operation: The server saves the collected data of temperature (22.5°C), humidity (48%), light intensity (75%), nutrient solution concentration (1.4 mL / L), and soil moisture (30%) in a MySQL database using INSERT statements.

[0950] Input: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%)

[0951] Output: Environmental data stored in a database

[0952] Step 3: Optimizing cultivation conditions

[0953] Description: The server optimizes the cultivation conditions based on the collected environmental data. It uses an optimization algorithm written in Python.

[0954] What happens: The server passes the collected data to a Python script, which verifies that the temperature is within the normal range at 22.5°C. The humidity is out of range at 48%, so it issues a command to use a humidifier to increase the humidity by 1%. It then adjusts other parameters in the same way.

[0955] Input: Environmental data stored in the database (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[0956] Output: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0957] Step 4: Calculate yield

[0958] Description: The server calculates crop yields based on optimized cultivation conditions. It uses the data science library scikit-learn.

[0959] How it works: The server inputs the optimized environmental conditions into a linear regression model in scikit-learn to calculate the crop yield. For example, based on the optimized temperature of 22.5°C, humidity of 49%, light intensity of 70%, nutrient solution concentration of 1.3 mL / L, and soil moisture of 28%, the predicted yield is calculated as 36.8.

[0960] Input: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0961] Output: Expected yield (e.g. 36.8)

[0962] Step 5: Save the results

[0963] Description: The server stores the calculated crop yields and optimized cultivation conditions in a database.

[0964] Specific operation: The server uses an INSERT statement to save the predicted yield of 36.8 and the optimized environmental conditions data into the MySQL database.

[0965] Input: Predicted yield and optimized cultivation conditions (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0966] Output: Predicted yield and optimized cultivation conditions stored in a database

[0967] Step 6: Output the results

[0968] Description: The server displays the optimized environmental conditions and crop yields on the user's device.

[0969] Specific operation: The server uses HTML and JavaScript to generate a web page containing the optimized data (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, predicted yield 36.8), sends it to the user's device, and displays it in the browser.

[0970] Input: Predicted yield and optimized cultivation conditions stored in the database (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[0971] Output: Environmental conditions and predicted yield displayed on the user's terminal

[0972] Step 7: Run the Emotion Engine

[0973] Description: The server passes the voice and facial expression data collected from the user's device to the emotion engine and analyzes the user's emotional state.

[0974] How it works: When a user is checking the displayed information on their smartphone, the camera and microphone collect the user's facial expressions and voice, and send this data to the server. The server then passes this data to the Emotion API for analysis.

[0975] Input: Voice data, facial expression data (e.g., user's facial expressions and voice)

[0976] Output: Parsed emotional state (e.g., stressed, satisfied, dissatisfied)

[0977] Step 8: Provide emotional feedback

[0978] Description: The server customizes results and provides feedback based on the user's emotional state as recognized by the emotion engine.

[0979] Specific operation: If the server recognizes that the user is feeling stressed, it generates a positive message such as "Today's work is going well! You're almost there!" and displays it on the user's device.

[0980] Input: Parsed emotional state (e.g., stress)

[0981] Output: A customized feedback message (e.g., "You're doing great today! You're almost there!")

[0982] (Application example 2)

[0983] 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."

[0984] Conventional vertical farming systems achieve efficient cultivation through the collection and optimization of environmental data, but no systems have taken user emotions and feedback into consideration. This has resulted in a lack of improvement in the user experience and limitations in sustainable agricultural operations. There is a need for more personalized support that reflects the emotions users feel about system operation and results.

[0985] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0986] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for collecting user emotion data, and means for analyzing the collected emotion data and providing feedback, thereby realizing efficient and sustainable vertical farming and providing feedback based on the user's emotions.

[0987] A "sensor" is a device for measuring environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[0988] "Environmental data" refers to information related to the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[0989] "Cultivation conditions" refer to the settings of temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which are environmental parameters optimal for crop growth.

[0990] "Yield" refers to the amount of crop produced within a specific period of time.

[0991] "Emotion data" is information about the emotional state obtained by analyzing the user's facial expressions and voice.

[0992] An "optimization means" is a process or device that adjusts cultivation conditions within set ranges based on collected environmental data.

[0993] The "collection means" is a process or device that acquires environmental data and user emotion data using sensors or the like.

[0994] An "analysis means" is a process or device for processing acquired data and extracting useful information.

[0995] A "feedback means" is a process or device that provides appropriate messages or advice based on the user's emotional data.

[0996] The "system" is a comprehensive device that combines the above sensors, collection means, optimization means, analysis means and feedback means to manage vertical farming and provide user support.

[0997] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable urban agriculture. The invention improves the user experience by incorporating a function to collect user emotion data and provide feedback based on that data.

[0998] First, the system is equipped with multiple sensors. These sensors periodically collect environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. This allows for a detailed understanding of the current cultivation environment. The collected data is sent to a server and stored in a database or temporary memory.

[0999] The server uses AI to optimize cultivation conditions based on the collected environmental data. The AI ​​optimization engine adjusts parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain the optimal cultivation environment. For example, if the temperature deviates from the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity are also controlled to stay within their respective optimal ranges.

[1000] The server then predicts crop yields based on the optimized cultivation conditions. Using a prediction model, it calculates yields based on the average and standard values ​​of each environmental parameter. The calculated yields and optimized cultivation conditions are stored in a database or temporary memory, allowing for future reference and recalculation.

[1001] Furthermore, the system has the ability to collect and analyze the user's emotional data. The server collects voice and facial expression data from the user's device and analyzes it using an emotion engine. This emotion engine recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.) and customizes the information output based on the results. For example, if the user is feeling stressed, the system will provide encouraging messages and helpful advice.

[1002] Specific examples

[1003] The following is an example of a day's processing. Sensors collect the following information: current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%. The server uses this data to determine whether the temperature is appropriate and adjusts the humidity to approach 50%. Light intensity and nutrient solution concentration are also adjusted to their optimal ranges. Crop yield is predicted based on this data; for example, a yield of 36.8% is calculated. The user's emotional data is then collected and analyzed by the emotion engine. If the system recognizes that the user is satisfied with the system's results, no special feedback is provided. However, if the user is feeling stressed, a positive message such as "Today's work is going well! You're almost there!" is displayed.

[1004] Prompt Sentence Examples

[1005] For example, you could use the prompt "If the temperature is 30°C, what can you do to keep it within the optimal range?"

[1006] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1007] Step 1:

[1008] The server collects environmental data from sensors installed in the vertical farming system. The sensors include temperature, humidity, light, nutrient solution concentration, and soil moisture sensors. The data measured by these sensors (input) is sent to the server and stored in a database or temporary memory in real time (output).

[1009] Step 2:

[1010] The server analyzes the collected environmental data and determines the current state of the cultivation environment. Input data such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are compared with a set optimal range (e.g., temperature: 20°C to 28°C). The server outputs the comparison result (e.g., temperature outside the optimal range) and generates instructions to adjust the situation.

[1011] Step 3:

[1012] The server uses an AI optimization engine to automatically optimize cultivation conditions based on collected environmental data. Based on the input data, temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are adjusted to fall within optimal ranges (for example, if the temperature is 30°C, the cooling system is activated and set to 22°C). The optimized environmental conditions (output) after adjustment are stored in a database.

[1013] Step 4:

[1014] The server predicts crop yields based on the optimized cultivation conditions. The yield prediction model uses average or standard values ​​for the optimized temperature, humidity, light intensity, nutrient solution concentration, and soil moisture as input data. The prediction algorithm processes these parameters and outputs a predicted yield (e.g., a yield of 36.8).

[1015] Step 5:

[1016] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory. The stored data (input) is prepared so that it can be accessed from the user's device. This allows the user to plan cultivation and adjustment work based on the stored data (output).

[1017] Step 6:

[1018] The server collects user voice and facial expression data from the user's device. The device's built-in camera and microphone capture the user's facial expressions and voice and send them to the server as input data. The emotion analysis engine analyzes this data and detects the user's emotional state (e.g., satisfaction, dissatisfaction, stress) (output).

[1019] Step 7:

[1020] The server provides feedback based on the user's emotions recognized by the emotion analysis engine. The result of the emotion analysis is used as input to generate a feedback message (e.g., "You're doing well today! You're almost there!") that corresponds to the user's emotional state. This message is then sent to the user's device and displayed (output).

[1021] Step 8:

[1022] The user checks the feedback messages and the latest cultivation environment information provided by the server. For example, based on information such as whether the temperature is within the appropriate range or the predicted yield is 36.8, the user can plan the next cultivation step or adjustment work (based on the confirmed data as input, a specific work plan is created as output).

[1023] 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.

[1024] 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.

[1025] 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.

[1026] [Fourth embodiment]

[1027] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1028] 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.

[1029] 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).

[1030] 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.

[1031] 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.

[1032] 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).

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] 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."

[1040] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. Specific embodiments of this system are described below.

[1041] Program Description

[1042] 1. Data collection

[1043] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, thereby accurately determining the current state of the growing environment.

[1044] 2. Running the optimization algorithm

[1045] The server optimizes growing conditions based on the collected environmental data. For example, if the temperature deviates from the specified range (20°C to 28°C), it adjusts it accordingly. The same goes for humidity, light intensity, nutrient solution concentration, and soil moisture, adjusting each parameter to stay within the optimal range.

[1046] 3. Yield Calculation

[1047] The server calculates the crop yield based on the optimized cultivation conditions by building a yield model using the average values ​​of each parameter and predicting the yield.

[1048] 4. Outputting the results

[1049] The server calculates and outputs the final environmental conditions and crop yield, allowing users to check the current cultivation conditions and predicted crop yield.

[1050] Specific examples

[1051] Example of processing time per day

[1052] Data collection

[1053] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[1054] Optimization process

[1055] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[1056] Yield calculation

[1057] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[1058] Result output

[1059] The server outputs and displays the adjusted environmental conditions and yield to the user, who can then check them and plan their next cultivation.

[1060] Thus, the system of the present invention can realize efficient and sustainable vertical farming, and has wide applicability as it can be adapted to different urban environments and cultivation conditions.

[1061] The processing flow will be explained below.

[1062] Step 1:

[1063] Environmental data is collected from sensors. The server collects data in real time from various sensors installed in the vertical farming system (temperature sensors, humidity sensors, light sensors, nutrient solution sensors, soil moisture sensors), allowing the current environmental conditions to be understood.

[1064] Step 2:

[1065] Temporarily store collected data. The server stores the collected environmental data in a database or temporary memory. This storage process makes it easier to process and analyze the data later.

[1066] Step 3:

[1067] Various conditions are checked and optimized. Based on the collected data, the server checks whether temperature, humidity, light intensity, nutrient solution concentration, soil humidity, etc. are within the set ranges. If a condition is outside the range, adjustments are made to optimize each condition. For example, if the temperature is below 20°C, adjustments such as turning on the heater are made.

[1068] Step 4:

[1069] Calculates crop yield based on optimized conditions. The server calculates predicted yield based on adjusted environmental conditions. Yield is predicted using a simple model using the average and standard values ​​of each environmental parameter.

[1070] Step 5:

[1071] Temporarily save the calculation results. The server saves the calculated crop yields and optimized environmental conditions in a database or temporary memory, which makes it easier to refer to the results or recalculate later.

[1072] Step 6:

[1073] The results are output and displayed to the user. The server outputs the optimized environmental conditions and predicted crop yields, which are displayed on the user's device. The user can use the displayed information to plan the next cultivation steps and adjustments.

[1074] Step 7:

[1075] Gathering user feedback and tuning the system. The user checks the results and sends feedback to the server as needed. The server then readjusts the system parameters based on this feedback and applies it to the next data collection and optimization process.

[1076] Example 1

[1077] 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."

[1078] To achieve efficient and sustainable food production in urban agriculture, detailed adjustment and management of the cultivation environment is necessary. With conventional methods, collecting environmental data and optimizing cultivation conditions based on that data is time-consuming and labor-intensive, and the results are often unstable. To solve these problems, a new integrated system is needed.

[1079] 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.

[1080] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for adjusting each parameter using the collected environmental data to keep it within an appropriate range, and means for constructing and predicting a yield model based on the adjusted environmental data, thereby enabling detailed adjustment and management of the cultivation environment to be performed efficiently and effectively.

[1081] A "sensor" is a measuring device for collecting environmental data.

[1082] "Environmental data" is information that indicates the state of the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[1083] A "server" is a device that processes environmental data collected from sensors and performs calculations to optimize cultivation conditions.

[1084] "Growth conditions" refers to the optimal range of environmental data required to support proper plant growth.

[1085] "Optimization" refers to the process of adjusting growing conditions to fall within ideal ranges based on collected environmental data.

[1086] "Yield" is a value that indicates the expected production of a crop under specific cultivation conditions.

[1087] A "yield model" is a mathematical or statistical model for calculating predicted crop yields based on environmental data.

[1088] "Output" refers to the processing to provide the final environmental conditions and predicted yields to the user.

[1089] "Adjustment" refers to the operation and control of various devices to keep each parameter of environmental data within an appropriate range.

[1090] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system collects environmental data from sensors, automatically adjusts optimal cultivation conditions, and predicts crop yields. This section describes a specific embodiment of the system.

[1091] First, the system is equipped with various sensors to measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. These sensors collect data in real time and send it to a server. The server receives this environmental data and performs the necessary calculations and data processing.

[1092] The server first analyzes the collected environmental data and checks whether each parameter is within the specified range. For example, assume the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this data, the server checks whether the temperature is within the appropriate range and makes adjustments as necessary. Because the humidity is below 50%, it turns on the humidifier and sets it to increase the humidity by 1%. The light intensity and nutrient solution concentration are also adjusted to fall within their respective optimal ranges.

[1093] The server then calculates the yield based on the adjusted environmental conditions. For example, it uses average values ​​for temperature, humidity, light intensity, nutrient solution concentration, and soil moisture to build a yield model and calculate a predicted yield. This yield model can be refined using collected data. In a specific example, after all parameters are adjusted, the yield is calculated as 36.8.

[1094] Finally, the server outputs the calculated environmental conditions and predicted yield to the user. The user can use this information to plan their next cultivation. For example, the following prompt sentence can be input into the generative AI model and used.

[1095] Example prompts

[1096] In an AI-integrated vertical farming system, current environmental data collected from sensors shows the temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil humidity is 30%. Based on this, set the optimal cultivation conditions and predict the yield that will result from increasing humidity by 1%. Also, output the environmental conditions and predicted yield after setting the settings.

[1097] In this way, this system realizes efficient and sustainable vertical farming using various sensors connected to a server and automated optimization algorithms. This system can be adapted to different urban environments and cultivation conditions, making it versatile.

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] Environmental data collection

[1101] The server collects environmental data from sensors within the vertical farming system.

[1102] Input: Measurement data from each sensor (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[1103] Data processing: The server receives data from various sensors in real time, organizes it, and records it.

[1104] Output: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%).

[1105] Specific operation: The server receives data of 22.5°C from the temperature sensor, data of 48% from the humidity sensor, and collects data from other sensors in the same way.

[1106] Step 2:

[1107] Optimizing cultivation conditions

[1108] Cultivation conditions are optimized based on the environmental data collected by the server.

[1109] Input: Collected environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[1110] Data calculation: The server checks each parameter and calculates the adjustments to keep it within the specified range (e.g., temperature 20°C to 28°C, humidity 50% to 60%, etc.).

[1111] Output: Optimized cultivation environment conditions (e.g., temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%).

[1112] Specific operation: The server checks that the temperature is within the appropriate range at 22.5°C, and the humidity is below the lower limit of the appropriate range at 48%, so it activates the humidifier to adjust the humidity to 50%. It also optimizes other parameters in the same way.

[1113] Step 3:

[1114] Yield calculation

[1115] The server calculates the crop yield based on the optimized cultivation conditions.

[1116] Input: Optimized environmental condition data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture).

[1117] Data calculation: The server applies the yield model and predicts the yield based on the average value of each parameter.

[1118] Output: Predicted yield (e.g. 36.8).

[1119] Specific operation: The server calculates the yield model using data for temperature 22.5°C, humidity 50%, light intensity 75%, nutrient solution concentration 1.4 mL / L, and soil moisture 30%, and predicts a yield of 36.8.

[1120] Step 4:

[1121] Output of results

[1122] The server outputs the final environmental conditions and crop yields to the user.

[1123] Input: Optimized environmental condition data and predicted yield.

[1124] Data processing: The server organizes the adjusted environmental conditions and predicted yields and converts them into a format that is easy for users to understand.

[1125] Output: Displayed on the user interface (optimized environmental conditions and predicted yield).

[1126] Specific operation: The server formats the data to display the adjusted temperature of 22.5°C, humidity of 50%, light intensity of 75%, nutrient solution concentration of 1.4 mL / L, soil humidity of 30%, and predicted yield of 36.8, and outputs it to the user interface.

[1127] In this way, efficient and sustainable vertical farming is achieved through data collection, processing, and output at each step.

[1128] (Application example 1)

[1129] 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."

[1130] Modern urban agriculture requires efficient and sustainable food production, but conventional systems have difficulty optimizing environmental conditions in real time and planning harvests and shipments. Current technology only optimizes conditions using data from individual sensors, and does not automate yield predictions or shipment plans. This leads to a lack of efficiency and accuracy in production and waste of resources.

[1131] 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.

[1132] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for monitoring the collected environmental data in real time, and means for automatically creating harvest and shipping plans based on the optimized cultivation conditions, thereby enabling efficient and sustainable food production in urban agriculture, reducing resource waste, and automatically optimizing harvest and shipping plans.

[1133] A "sensor" is a device that measures and collects environmental data.

[1134] "Environmental data" includes data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1135] "Cultivation conditions" refer to the environmental conditions necessary for crops to grow.

[1136] "Optimization" refers to adjusting to the most desirable state under given conditions and resources.

[1137] "Crop yield" is the total production of a crop grown for a particular period and under particular conditions.

[1138] "Collecting" refers to the act of obtaining data using sensors or other devices.

[1139] "Monitoring" "collected environmental data" means constantly observing it and, if necessary, recording and analyzing it.

[1140] "Real-time" refers to reacting immediately to the current time in progress.

[1141] "Automatically creating a harvest and shipping plan based on optimized cultivation conditions" means automatically setting a harvest and shipping schedule in accordance with optimized cultivation conditions.

[1142] To implement this invention, a vertical farming system installed in a distribution center, a server for managing the system, and a mobile device are required. The system collects environmental data from sensors, optimizes cultivation conditions based on the data, predicts yield, and automatically creates harvest and shipping plans.

[1143] Hardware and software used

[1144] Hardware:

[1145] Sensors: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor

[1146] Smart devices: smartphones, head-mounted displays (e.g. HoloLens)

[1147] Robot: Autonomous Guided Vehicle (AGV)

[1148] software:

[1149] Data analysis libraries: NumPy, scikit-learn

[1150] Program execution environment: Python

[1151] Smartphone app: Kotlin (for Android), Swift (for iOS)

[1152] System Operation

[1153] 1. Data Collection:

[1154] The server collects real-time data from various sensors on temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which serves as the basis for optimizing the growing environment for crops.

[1155] 2. Data optimization:

[1156] The server analyzes the collected environmental data and adjusts each parameter to suit the optimal cultivation conditions, thereby providing the optimal environment for crop growth.

[1157] 3. Yield prediction:

[1158] The server predicts crop yields based on the optimized data, using a yield model.

[1159] 4. Automated shipping planning:

[1160] Based on the optimized data and yield forecasts, the server automatically creates harvest and shipping plans that are dynamically updated based on data collected in real time.

[1161] Specific examples

[1162] For example, suppose the data collected by the server in the morning of a certain day is a temperature of 24°C, humidity of 58%, light intensity of 76%, nutrient solution concentration of 1.4 mL / L, and soil moisture of 36%. Based on this data, the server optimizes the environmental conditions and calculates a predicted yield of 36.8 kg. It then automatically creates a shipping plan based on the expected harvest date and prepares for delivery.

[1163] Prompt Sentence Examples

[1164] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[1165] In this way, this invention enables efficient and sustainable food production in urban agriculture within logistics centers, minimizing resource waste and optimizing cultivation conditions by automating harvesting and shipping planning.

[1166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1167] Step 1:

[1168] The server collects environmental data from various sensors. Specifically, it is equipped with temperature, humidity, light intensity, nutrient solution concentration, and soil humidity sensors, and these sensors collect data in real time and send it to the server. The input is the real-time data (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) obtained from each sensor, and the output is environmental data that is a unified version of this data.

[1169] Step 2:

[1170] The server optimizes cultivation conditions based on the collected environmental data. Specifically, it runs an algorithm that adjusts each parameter (temperature, humidity, light intensity, nutrient solution concentration, and soil humidity) to fall within a specific range. The input is the collected environmental data, and the output is the adjusted environmental data. If the temperature is 24°C and the humidity is 58%, it checks whether they are within the optimal range and adjusts the environmental conditions as necessary.

[1171] Step 3:

[1172] The server reconfirms the cultivation conditions using the optimized environmental data. Specifically, it evaluates whether the optimized environmental data meets the ideal cultivation conditions and makes further fine adjustments if necessary. The input is the optimized environmental data, and the output is the environmental data after reconfirmation and fine adjustment.

[1173] Step 4:

[1174] The server predicts crop yields based on the optimized environmental data. Specifically, it uses a yield prediction model to calculate yields using each optimized parameter as input data. This yield prediction model uses, for example, a machine learning algorithm (such as Linear Regression). The input is the optimized environmental data, and the output is the predicted yield.

[1175] Step 5:

[1176] The server automatically creates harvest and shipping plans based on the yield prediction results. Specifically, it determines the harvest date and time based on the predicted yield and even sets the subsequent shipping plan. It also includes a process that uses autonomous vehicles (AGVs) to automatically prepare for harvest and shipping. The input is the predicted yield and the current schedule, and the output is the harvest and shipping plan.

[1177] Step 6:

[1178] The server or terminal displays the final environmental conditions, predicted yield, and harvest and shipping plans on the user's device. Specifically, data is sent to a smartphone or head-mounted display, allowing the user to check the current situation in real time. The input is the final environmental data, predicted yield, and harvest and shipping plans, and the output is the terminal screen displaying this information.

[1179] Prompt Sentence Examples

[1180] "The current temperature is 24°C, humidity is 58%, light intensity is 76%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 36%. Please predict the yield based on this."

[1181] 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.

[1182] The present invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. It also incorporates an emotion engine to provide feedback based on the user's emotions, improving the user experience. A specific embodiment of this system is described below.

[1183] Program Description

[1184] 1. Data collection

[1185] The server periodically collects environmental data from sensors installed in the vertical farming system, which measure multiple parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, providing a detailed understanding of the current state of the cultivation environment.

[1186] 2. Data storage

[1187] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[1188] 3. Optimizing cultivation conditions

[1189] The server optimizes settings such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity based on the collected environmental data. For example, if the temperature is outside the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity will also be adjusted to stay within the optimal range.

[1190] 4. Yield Calculation

[1191] The server calculates the crop yield based on the optimized cultivation conditions by predicting the yield using a simple model that uses the average and standard values ​​of each environmental parameter.

[1192] 5. Saving the results

[1193] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory, which makes it easy to refer to the results or perform recalculations later.

[1194] 6. Outputting the results

[1195] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. The user can then use the displayed information to plan their next cultivation or adjustment work.

[1196] 7. Running the Emotion Engine

[1197] The server collects the user's voice and facial expression data from the user's device and analyzes it using an emotion engine, which then recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[1198] 8. Providing emotional feedback

[1199] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the system displays encouraging messages or helpful advice for farming operations.

[1200] Specific examples

[1201] Example of processing time per day

[1202] Data collection

[1203] The server collects from the sensors that the current temperature is 22.5°C, humidity is 48%, light intensity is 75%, nutrient solution concentration is 1.4 mL / L, and soil moisture is 30%.

[1204] Optimization process

[1205] The server checks to see if the temperature is appropriate and adjusts it as needed. If the humidity is below 50%, it will increase it by 1%. The server also adjusts the light intensity and nutrient solution concentration to ensure they are within the optimal range.

[1206] Yield calculation

[1207] The server calculates the yield based on the average values ​​of temperature, humidity, light intensity, nutrient solution concentration and soil moisture. For example, after adjusting all parameters, the yield is 36.8.

[1208] Result output

[1209] The server outputs the adjusted environmental conditions and yields and displays them on the user's terminal, allowing the user to check them and plan the next cultivation steps or adjustment work.

[1210] emotion recognition

[1211] The server analyzes the user's voice and facial expressions, and uses an emotion engine to recognize whether the user is satisfied or dissatisfied with the system's results.

[1212] Emotional Feedback

[1213] If the user is feeling stressed, the server will display positive feedback such as "You're doing well today! You're almost there!"

[1214] In this way, the system of the present invention can realize efficient and sustainable vertical farming, and can also improve the user experience by providing a feedback function based on the user's emotions.

[1215] The processing flow will be explained below.

[1216] Step 1:

[1217] The server collects environmental data from sensors installed in the vertical farming system, which measure temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, and sends the data to the server.

[1218] Step 2:

[1219] The server stores the collected environmental data in a database or temporary memory, which facilitates later data processing and analysis.

[1220] Step 3:

[1221] The server uses the collected data to check whether each cultivation condition (temperature, humidity, light intensity, nutrient solution concentration, soil humidity) is within the set range. For example, if the temperature is below 20°C, it will make adjustments such as turning on the heater.

[1222] Step 4:

[1223] The server adjusts environmental conditions as needed to set optimal growing conditions, such as increasing humidity or adjusting light intensity, to ensure all conditions are within the optimum range.

[1224] Step 5:

[1225] The server calculates the crop yield based on the optimized cultivation conditions by using a simple model to predict the yield using the average and standard values ​​of each environmental parameter.

[1226] Step 6:

[1227] The server stores the calculated crop yields and optimized environmental conditions in a database or temporary memory, which facilitates future reference and recalculation of the results.

[1228] Step 7:

[1229] The server outputs the optimized environmental conditions and crop yields and displays them on the user's device. Based on the displayed information, the user can plan their next cultivation and adjustment work.

[1230] Step 8:

[1231] The server collects the user's voice and facial expression data from the user's device, and uses an emotion engine to recognize the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.).

[1232] Step 9:

[1233] The server customizes the output based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the server displays encouraging messages or helpful advice for farming operations.

[1234] Step 10:

[1235] Users can review the results and feedback, and then take the next steps or make adjustments, improving their cultivation efficiency and satisfaction.

[1236] Example 2

[1237] 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."

[1238] To achieve efficient and sustainable food production in urban agriculture, it is necessary to collect and manage detailed environmental data and maintain appropriate cultivation conditions. However, conventional systems require a complicated process for collecting environmental data, and adjustments to maintain optimal cultivation conditions are labor-intensive. Another issue is the lack of feedback functions to improve the user's farming experience.

[1239] 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.

[1240] In this invention, the server includes means for collecting environmental data from sensors, means for storing the collected environmental data in a database, means for optimizing cultivation conditions based on the collected environmental data, means for calculating a crop yield based on the optimized cultivation conditions, means for storing the optimized cultivation conditions and the calculated crop yield in a database, means for outputting the calculated crop yield and displaying it on a user terminal, means for collecting emotion data to analyze user emotions, and means for providing feedback based on the emotion data, thereby enabling efficient and sustainable cultivation and providing a feedback function that improves the user experience.

[1241] A "sensor" is a device for collecting environmental data, and is capable of measuring parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1242] "Environmental data" refers to various data that indicate the plant cultivation environment, and specifically includes information such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[1243] A "database" is a system for systematically storing and managing large amounts of data, and is capable of efficiently storing, searching, and updating data.

[1244] "Optimization means" refers to algorithms and control systems that analyze collected environmental data and maintain optimal cultivation conditions.

[1245] "Yield" is an indicator of the amount of harvested crops grown and is predicted based on optimized environmental conditions.

[1246] "Emotion data" is data collected from the user's voice, facial expressions, etc., and is basic data for analyzing the user's emotional state.

[1247] A "means for providing feedback" is a system or function for providing a corresponding message or advice to a user based on the analyzed emotional data of the user.

[1248] A "user terminal" is a device used by a user to receive information, and includes electronic devices such as smartphones, tablets, and personal computers.

[1249] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable food production in urban agriculture. The system integrates means for collecting, storing, and analyzing environmental data, and means for providing feedback based on user emotions.

[1250] Hardware and Software Configuration

[1251] server:

[1252] The server plays a central role in data collection, data storage, data analysis, environmental control, and feedback provision. Specific functions are as follows:

[1253] sensor:

[1254] The sensors used are temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, each connected to a data collection device such as a Raspberry Pi.

[1255] Database:

[1256] The database uses MySQL and stores collected environmental data, optimized cultivation conditions, and yield predictions.

[1257] Analysis and optimization software:

[1258] The analysis and optimization software is implemented using Python, and the data science library scikit-learn is used for the optimization algorithms.

[1259] Emotion Engine:

[1260] The Emotion API from Microsoft Azure is used to analyze user emotions, and emotional states are analyzed based on voice and facial expression data.

[1261] User device:

[1262] User terminals are electronic devices such as smartphones, tablets, and personal computers. These terminals receive data from the server and display it to the user.

[1263] Specific processing flow

[1264] First, the server periodically collects environmental data (temperature, humidity, light intensity, nutrient solution concentration, soil moisture) from sensors, and stores the collected data in a database.

[1265] The server then uses Python and scikit-learn to calculate optimal growing conditions based on the collected data. For example, if the temperature is outside the set range (20°C to 28°C), the temperature control system will automatically adjust. Similarly, humidity, light intensity, nutrient solution concentration, and soil moisture will also be optimized.

[1266] Using the optimized conditions, the server predicts crop yields by building a predictive model based on past data and current environmental conditions, and then calculating yields. The results are then stored in a database.

[1267] The saved optimization conditions and yield information are sent to the user's device and displayed to the user. Specific display information could be "Temperature: 22.5°C, Humidity: 49%, Light Intensity: 70%, Predicted Yield: 36.8".

[1268] Furthermore, the server collects voice and facial expression data from the user's smartphone or computer and sends it to the Emotion API to analyze the user's emotions. Based on the analysis results, the system provides positive feedback, such as "You're doing well today! You're almost there!"

[1269] Specific examples

[1270] Example prompt sentence:

[1271] 1. Collect from the sensors the current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%.

[1272] 2. The server checks the temperature and determines that it is within the normal range. At the same time, it determines that the humidity is below 50% and issues a command to use a humidifier to increase the humidity by 1%.

[1273] 3. Based on the collected data, the yield is calculated using the optimized environmental conditions. The yield is predicted using a linear regression model in scikit-learn.

[1274] 4. The server stores the predicted yield of 36.8 and the optimized environmental conditions in a database and displays them on the user's terminal.

[1275] 5. Send the user's facial expressions and voice data to the Emotion API to analyze their emotional state. If the user is feeling stressed, provide positive feedback.

[1276] In this way, by combining detailed collection and management of environmental data, optimization of cultivation conditions, calculation of predicted yields, and feedback based on user emotions, the system of the present invention can achieve efficient and sustainable food production in urban agriculture while also improving the user experience.

[1277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1278] Step 1: Collect data

[1279] Description: The server periodically collects environmental data from various sensors (temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil moisture sensor) installed in the vertical farming system.

[1280] How it works: The server obtains the current temperature data (e.g., 22.5°C) from the temperature sensor connected to the Raspberry Pi. Similarly, it obtains the humidity (e.g., 48%), light intensity (e.g., 75%), nutrient solution concentration (e.g., 1.4 mL / L), and soil moisture (e.g., 30%) from other sensors.

[1281] Input: Temperature sensor, humidity sensor, light intensity sensor, nutrient solution concentration sensor, soil humidity sensor

[1282] Output: Temperature, humidity, light intensity, nutrient solution concentration, soil humidity data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[1283] Step 2: Save your data

[1284] Description: The server stores the collected environmental data in a database.

[1285] Specific operation: The server saves the collected data of temperature (22.5°C), humidity (48%), light intensity (75%), nutrient solution concentration (1.4 mL / L), and soil moisture (30%) in a MySQL database using INSERT statements.

[1286] Input: Collected environmental data (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil moisture 30%)

[1287] Output: Environmental data stored in a database

[1288] Step 3: Optimizing cultivation conditions

[1289] Description: The server optimizes the cultivation conditions based on the collected environmental data. It uses an optimization algorithm written in Python.

[1290] What happens: The server passes the collected data to a Python script, which verifies that the temperature is within the normal range at 22.5°C. The humidity is out of range at 48%, so it issues a command to use a humidifier to increase the humidity by 1%. It then adjusts other parameters in the same way.

[1291] Input: Environmental data stored in the database (e.g., temperature 22.5°C, humidity 48%, light intensity 75%, nutrient solution concentration 1.4 mL / L, soil humidity 30%)

[1292] Output: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[1293] Step 4: Calculate yield

[1294] Description: The server calculates crop yields based on optimized cultivation conditions. It uses the data science library scikit-learn.

[1295] How it works: The server inputs the optimized environmental conditions into a linear regression model in scikit-learn to calculate the crop yield. For example, based on the optimized temperature of 22.5°C, humidity of 49%, light intensity of 70%, nutrient solution concentration of 1.3 mL / L, and soil moisture of 28%, the predicted yield is calculated as 36.8.

[1296] Input: Optimized cultivation conditions (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[1297] Output: Expected yield (e.g. 36.8)

[1298] Step 5: Save the results

[1299] Description: The server stores the calculated crop yields and optimized cultivation conditions in a database.

[1300] Specific operation: The server uses an INSERT statement to save the predicted yield of 36.8 and the optimized environmental conditions data into the MySQL database.

[1301] Input: Predicted yield and optimized cultivation conditions (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[1302] Output: Predicted yield and optimized cultivation conditions stored in a database

[1303] Step 6: Output the results

[1304] Description: The server displays the optimized environmental conditions and crop yields on the user's device.

[1305] Specific operation: The server uses HTML and JavaScript to generate a web page containing the optimized data (e.g., temperature 22.5°C, humidity 49%, light intensity 70%, predicted yield 36.8), sends it to the user's device, and displays it in the browser.

[1306] Input: Predicted yield and optimized cultivation conditions stored in the database (e.g., predicted yield 36.8, temperature 22.5°C, humidity 49%, light intensity 70%, nutrient solution concentration 1.3 mL / L, soil humidity 28%)

[1307] Output: Environmental conditions and predicted yield displayed on the user's terminal

[1308] Step 7: Run the Emotion Engine

[1309] Description: The server passes the voice and facial expression data collected from the user's device to the emotion engine and analyzes the user's emotional state.

[1310] How it works: When a user is checking the displayed information on their smartphone, the camera and microphone collect the user's facial expressions and voice, and send this data to the server. The server then passes this data to the Emotion API for analysis.

[1311] Input: Voice data, facial expression data (e.g., user's facial expressions and voice)

[1312] Output: Parsed emotional state (e.g., stressed, satisfied, dissatisfied)

[1313] Step 8: Provide emotional feedback

[1314] Description: The server customizes results and provides feedback based on the user's emotional state as recognized by the emotion engine.

[1315] Specific operation: If the server recognizes that the user is feeling stressed, it generates a positive message such as "Today's work is going well! You're almost there!" and displays it on the user's device.

[1316] Input: Parsed emotional state (e.g., stress)

[1317] Output: A customized feedback message (e.g., "You're doing great today! You're almost there!")

[1318] (Application example 2)

[1319] 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."

[1320] Conventional vertical farming systems achieve efficient cultivation through the collection and optimization of environmental data, but no systems have taken user emotions and feedback into consideration. This has resulted in a lack of improvement in the user experience and limitations in sustainable agricultural operations. There is a need for more personalized support that reflects the emotions users feel about system operation and results.

[1321] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1322] In this invention, the server includes means for collecting environmental data from sensors, means for optimizing cultivation conditions based on the collected environmental data, means for calculating crop yields based on the optimized cultivation conditions, means for outputting the calculated crop yields, means for collecting user emotion data, and means for analyzing the collected emotion data and providing feedback, thereby realizing efficient and sustainable vertical farming and providing feedback based on the user's emotions.

[1323] A "sensor" is a device for measuring environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1324] "Environmental data" refers to information related to the cultivation environment, such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity.

[1325] "Cultivation conditions" refer to the settings of temperature, humidity, light intensity, nutrient solution concentration, and soil moisture, which are environmental parameters optimal for crop growth.

[1326] "Yield" refers to the amount of crop produced within a specific period of time.

[1327] "Emotion data" is information about the emotional state obtained by analyzing the user's facial expressions and voice.

[1328] An "optimization means" is a process or device that adjusts cultivation conditions within set ranges based on collected environmental data.

[1329] The "collection means" is a process or device that acquires environmental data and user emotion data using sensors or the like.

[1330] An "analysis means" is a process or device for processing acquired data and extracting useful information.

[1331] A "feedback means" is a process or device that provides appropriate messages or advice based on the user's emotional data.

[1332] The "system" is a comprehensive device that combines the above sensors, collection means, optimization means, analysis means and feedback means to manage vertical farming and provide user support.

[1333] This invention relates to an AI-integrated vertical farming system for achieving efficient and sustainable urban agriculture. The invention improves the user experience by incorporating a function to collect user emotion data and provide feedback based on that data.

[1334] First, the system is equipped with multiple sensors. These sensors periodically collect environmental data such as temperature, humidity, light intensity, nutrient solution concentration, and soil moisture. This allows for a detailed understanding of the current cultivation environment. The collected data is sent to a server and stored in a database or temporary memory.

[1335] The server uses AI to optimize cultivation conditions based on the collected environmental data. The AI ​​optimization engine adjusts parameters such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain the optimal cultivation environment. For example, if the temperature deviates from the set range (20°C to 28°C), it will be automatically adjusted. Similarly, humidity, light intensity, nutrient solution concentration, and soil humidity are also controlled to stay within their respective optimal ranges.

[1336] The server then predicts crop yields based on the optimized cultivation conditions. Using a prediction model, it calculates yields based on the average and standard values ​​of each environmental parameter. The calculated yields and optimized cultivation conditions are stored in a database or temporary memory, allowing for future reference and recalculation.

[1337] Furthermore, the system has the ability to collect and analyze the user's emotional data. The server collects voice and facial expression data from the user's device and analyzes it using an emotion engine. This emotion engine recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, stress, etc.) and customizes the information output based on the results. For example, if the user is feeling stressed, the system will provide encouraging messages and helpful advice.

[1338] Specific examples

[1339] The following is an example of a day's processing. Sensors collect the following information: current temperature: 22.5°C, humidity: 48%, light intensity: 75%, nutrient solution concentration: 1.4 mL / L, and soil moisture: 30%. The server uses this data to determine whether the temperature is appropriate and adjusts the humidity to approach 50%. Light intensity and nutrient solution concentration are also adjusted to their optimal ranges. Crop yield is predicted based on this data; for example, a yield of 36.8% is calculated. The user's emotional data is then collected and analyzed by the emotion engine. If the system recognizes that the user is satisfied with the system's results, no special feedback is provided. However, if the user is feeling stressed, a positive message such as "Today's work is going well! You're almost there!" is displayed.

[1340] Prompt Sentence Examples

[1341] For example, you could use the prompt "If the temperature is 30°C, what can you do to keep it within the optimal range?"

[1342] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1343] Step 1:

[1344] The server collects environmental data from sensors installed in the vertical farming system. The sensors include temperature, humidity, light, nutrient solution concentration, and soil moisture sensors. The data measured by these sensors (input) is sent to the server and stored in a database or temporary memory in real time (output).

[1345] Step 2:

[1346] The server analyzes the collected environmental data and determines the current state of the cultivation environment. Input data such as temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are compared with a set optimal range (e.g., temperature: 20°C to 28°C). The server outputs the comparison result (e.g., temperature outside the optimal range) and generates instructions to adjust the situation.

[1347] Step 3:

[1348] The server uses an AI optimization engine to automatically optimize cultivation conditions based on collected environmental data. Based on the input data, temperature, humidity, light intensity, nutrient solution concentration, and soil humidity are adjusted to fall within optimal ranges (for example, if the temperature is 30°C, the cooling system is activated and set to 22°C). The optimized environmental conditions (output) after adjustment are stored in a database.

[1349] Step 4:

[1350] The server predicts crop yields based on the optimized cultivation conditions. The yield prediction model uses average or standard values ​​for the optimized temperature, humidity, light intensity, nutrient solution concentration, and soil moisture as input data. The prediction algorithm processes these parameters and outputs a predicted yield (e.g., a yield of 36.8).

[1351] Step 5:

[1352] The server stores the calculated crop yields and optimized cultivation conditions in a database or temporary memory. The stored data (input) is prepared so that it can be accessed from the user's device. This allows the user to plan cultivation and adjustment work based on the stored data (output).

[1353] Step 6:

[1354] The server collects user voice and facial expression data from the user's device. The device's built-in camera and microphone capture the user's facial expressions and voice and send them to the server as input data. The emotion analysis engine analyzes this data and detects the user's emotional state (e.g., satisfaction, dissatisfaction, stress) (output).

[1355] Step 7:

[1356] The server provides feedback based on the user's emotions recognized by the emotion analysis engine. The result of the emotion analysis is used as input to generate a feedback message (e.g., "You're doing well today! You're almost there!") that corresponds to the user's emotional state. This message is then sent to the user's device and displayed (output).

[1357] Step 8:

[1358] The user checks the feedback messages and the latest cultivation environment information provided by the server. For example, based on information such as whether the temperature is within the appropriate range or the predicted yield is 36.8, the user can plan the next cultivation step or adjustment work (based on the confirmed data as input, a specific work plan is created as output).

[1359] 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.

[1360] 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.

[1361] 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 robot 414.

[1362] 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.

[1363] FIG. 9 is a diagram illustrating 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 actions 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.

[1364] 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.

[1365] 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).

[1366] 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.

[1367] 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."

[1368] 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.

[1369] 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).

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] The following is further disclosed regarding the above embodiment.

[1381] (Claim 1)

[1382] means for collecting environmental data from sensors;

[1383] means for optimizing cultivation conditions based on the collected environmental data; and

[1384] a means for calculating crop yield based on the optimized cultivation conditions;

[1385] a means for outputting the calculated crop yield;

[1386] A system including:

[1387] (Claim 2)

[1388] 10. The system of claim 1, wherein the collected environmental data includes temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1389] (Claim 3)

[1390] 2. The system according to claim 1, wherein the means for optimizing the cultivation conditions adjusts the temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain them within specific ranges.

[1391] "Example 1"

[1392] (Claim 1)

[1393] means for collecting environmental data from sensors;

[1394] means for optimizing cultivation conditions based on the collected environmental data; and

[1395] a means for calculating crop yield based on the optimized cultivation conditions;

[1396] a means for outputting the calculated crop yield;

[1397] a means for adjusting each parameter using the collected environmental data to keep it within an appropriate range;

[1398] A system that includes a means for constructing and predicting yield models based on adjusted environmental data.

[1399] (Claim 2)

[1400] 10. The system of claim 1, wherein the collected environmental data includes temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1401] (Claim 3)

[1402] 2. The system according to claim 1, wherein the means for optimizing the cultivation conditions adjusts the temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain them within specific ranges.

[1403] "Application Example 1"

[1404] (Claim 1)

[1405] means for collecting environmental data from sensors;

[1406] means for optimizing cultivation conditions based on the collected environmental data; and

[1407] a means for calculating crop yield based on the optimized cultivation conditions;

[1408] a means for outputting the calculated crop yield;

[1409] a means for monitoring the collected environmental data in real time;

[1410] A means for automatically creating a harvest and shipping plan based on optimized cultivation conditions;

[1411] A system including:

[1412] (Claim 2)

[1413] 10. The system of claim 1, wherein the collected environmental data includes temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1414] (Claim 3)

[1415] 2. The system according to claim 1, wherein the means for optimizing the cultivation conditions adjusts the temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain them within specific ranges.

[1416] "Example 2: Combining Emotion Engines"

[1417] (Claim 1)

[1418] means for collecting environmental data from sensors;

[1419] a means for storing the collected environmental data in a database;

[1420] means for optimizing cultivation conditions based on the collected environmental data; and

[1421] a means for calculating crop yield based on the optimized cultivation conditions;

[1422] means for storing the optimized cultivation conditions and the calculated crop yield in a database;

[1423] means for outputting the calculated crop yield and displaying it on a user terminal;

[1424] means for collecting emotion data for analyzing user emotions;

[1425] a means for providing feedback based on the emotion data;

[1426] A system including:

[1427] (Claim 2)

[1428] 10. The system of claim 1, wherein the collected environmental data includes temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1429] (Claim 3)

[1430] 2. The system according to claim 1, wherein the means for optimizing the cultivation conditions adjusts the temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain them within specific ranges.

[1431] "Application example 2 when combining emotion engines"

[1432] (Claim 1)

[1433] means for collecting environmental data from sensors;

[1434] means for optimizing cultivation conditions based on the collected environmental data; and

[1435] a means for calculating crop yield based on the optimized cultivation conditions;

[1436] a means for outputting the calculated crop yield;

[1437] means for collecting user emotion data;

[1438] A means of analyzing the collected emotional data and providing feedback;

[1439] A system including:

[1440] (Claim 2)

[1441] 10. The system of claim 1, wherein the collected environmental data includes temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

[1442] (Claim 3)

[1443] 2. The system according to claim 1, wherein the means for optimizing the cultivation conditions adjusts the temperature, humidity, light intensity, nutrient solution concentration, and soil humidity to maintain them within specific ranges. [Explanation of symbols]

[1444] 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. means for collecting environmental data from sensors; means for optimizing cultivation conditions based on the collected environmental data; and a means for calculating crop yield based on the optimized cultivation conditions; a means for outputting the calculated crop yield; A system including:

2. 10. The system of claim 1, wherein the collected environmental data includes temperature, humidity, light intensity, nutrient solution concentration, and soil moisture.

3. 2. The system according to claim 1, wherein the means for optimizing the cultivation conditions adjusts the temperature, humidity, light intensity, nutrient solution concentration, and soil moisture to maintain them within specific ranges.

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