Indoor urban farming support system, indoor urban farming support method, and indoor urban farming support program

Abstract

By effectively utilizing limited urban space and achieving sustainable and efficient plant cultivation, we aim to improve the environment, enhance energy efficiency, reduce operational burdens, promote resource recycling, and enable maintenance that does not rely on specialized knowledge, thereby creating new agricultural value in urban environments. [Solution] A mobile device M is equipped with an indoor 3D point cloud data acquisition unit (LiDAR1) that 3D scans the indoor space to acquire indoor three-dimensional point cloud data, an indoor image acquisition unit (camera2) that captures images of the indoor space to acquire image data, and an indoor positioning data acquisition unit (indoor positioning sensor3) that acquires indoor positioning data. In addition, some or all of the environmental sensors that measure indoor environmental data are provided. While the mobile device M is moving or stationary, indoor three-dimensional point cloud data, image data, indoor positioning data and environmental data are acquired and integrated, and the arrangement of the indoor cultivation system is determined based on the integrated data.

Description

Technical Field

[0001] The present invention belongs to the technical field related to efficient and sustainable food production indoors in urban areas. In particular, it relates to a technology that utilizes hydroponics and aquaponics and combines sensor and AI technologies to support urban farming optimized for the urban environment.

Background Art

[0002] In urban areas, due to space constraints, agriculture has mainly been carried out in the suburbs and rural areas. Examples of attempts at food production in cities include rooftop gardens and community gardens, but these mainly remain at the level of local activities and small-scale production. On the other hand, in office buildings and commercial facilities, green walls and indoor plantings have been introduced, but they are introduced for the purpose of air purification and aesthetic improvement and do not serve as a means of food production (for example, see Patent Documents 1 and 2).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] As described above, in urban areas, it is required to effectively utilize limited space. However, conventional agricultural technologies have not established an efficient cultivation method suitable for buildings or urban spaces, and many problems remain in terms of energy efficiency and maintenance burden. In an urban environment where it is difficult to secure farmland, conventional efforts such as rooftop gardens and community gardens are insufficient from the viewpoints of food supply scale and efficiency. A means to overcome such space constraints and realize cultivation inside buildings has been faced (the problem of insufficient space in urban areas). Furthermore, indoor plant cultivation requires essential equipment such as artificial lighting, temperature control, and humidity control, which consume large amounts of energy. Conventional systems are energy-inefficient, so attempts to improve environmental performance often lead to increased total costs, making sustainability a challenge (the problem of increased energy consumption). Furthermore, with conventional technology, many tasks such as supplying water and nutrients, managing temperature and humidity, and adjusting lighting are performed manually, which places a heavy burden of time and effort on operators (the problem of maintenance burden).

[0005] Furthermore, conventional indoor cultivation systems often suffer from insufficient resource recycling, leading to inefficient use of water and nutrients. Waste disposal also poses a challenge, resulting in unresolved environmental impact. In today's urban areas, where sustainability is paramount, the development of circular systems is essential (the lack of circular agriculture). Furthermore, properly managing plant growth conditions often requires advanced expertise. If operators lack sufficient knowledge, they may make mistakes in setting up or adjusting equipment, which reduces the reliability and efficiency of operations (the problem of insufficient expertise).

[0006] This invention was made in view of these circumstances, and its main objective is to create new agricultural value in urban environments by effectively utilizing the limited space in urban areas and realizing sustainable and efficient plant cultivation, thereby improving the environment, increasing energy efficiency, reducing operational burden, promoting the recycling of resources, and enabling maintenance and management that does not rely on specialized knowledge. [Means for solving the problem]

[0007] To achieve the above objectives, the indoor urban farming support system according to the present invention includes an indoor 3D point cloud data acquisition unit that 3D scans an indoor space to acquire indoor three-dimensional point cloud data, An indoor image acquisition unit that captures images of the indoor space and acquires indoor image data, An indoor positioning data acquisition unit that acquires indoor positioning data, An environmental sensor that measures the aforementioned indoor environmental data, A mobile device is provided with the indoor 3D point cloud data acquisition unit, the indoor image acquisition unit, and the indoor positioning data acquisition unit, and is also provided with some or all of the environmental sensors. A data integration unit that acquires and integrates the indoor three-dimensional point cloud data, indoor image data, indoor positioning data, and environmental data acquired while moving or stationary the mobile device, A data analysis unit analyzes indoor environmental conditions based on the data integrated by the aforementioned data integration unit, Based on the analysis results analyzed by the aforementioned data analysis unit, the layout determination unit determines the layout of the indoor cultivation system, It is characterized by possessing the following features.

[0008] Here, the mobile device may be a self-propelled mobile body such as a mobile robot capable of autonomously moving indoors or a drone that flies indoors, and the portable device may be a shoulder-worn device or a backpack-type device equipped with portable sensors for collecting data necessary for optimizing the arrangement of the cultivation system.

[0009] Therefore, according to the indoor urban farming support system of the present invention, 1. By integrating and analyzing indoor 3D point cloud data, image data, positioning data, and environmental sensor data, limited indoor space in urban areas can be utilized efficiently. This overcomes the space constraints that could not be addressed by conventional small-scale initiatives such as rooftop gardens and community gardens, enabling larger-scale and more efficient plant cultivation (solving the problem of space shortage in urban areas). 2. By precisely managing indoor temperature, humidity, and lighting conditions using environmental sensors and data analysis units, an optimal cultivation environment can be provided with minimal energy consumption. This enhances environmental improvement while reducing total costs (improving energy efficiency). 3. By utilizing mobile devices to automate data collection and environmental surveys, the burden of traditional manual work is significantly reduced. This enables the efficient use of time and effort, making it easier to survey and manage large areas (reducing the burden of maintenance). 4. The data integration unit efficiently manages water and nutrient usage, minimizing waste and realizing circular agriculture. It also streamlines waste disposal, contributing to a reduction in environmental impact (promoting the circular use of resources). 5. By determining plant placement based on scientific data and conducting detailed analyses of environmental conditions, even operators without specialized knowledge can efficiently manage the system. This ensures operational reliability while increasing the efficiency and sustainability of cultivation (achieving reliable operation even with insufficient expertise). 6. The placement determination unit automatically generates an optimal placement plan based on data analysis results, taking into account the physical constraints and environmental conditions of the indoor space. This provides a more effective placement plan that does not rely on subjective judgment, thereby improving plant productivity and quality (scientifically-based placement optimization). As described above, the present invention provides a rational and scientific approach to all processes, from data collection and integration to analysis of environmental conditions and determination of cultivation system placement, and establishes a comprehensive system to support plant cultivation in indoor spaces.

[0010] Furthermore, the system is characterized by further comprising a cultivation environment determination unit that determines the cultivation environment for the plants to be cultivated based on the analysis results analyzed by the data analysis unit and taking into account the arrangement determined by the arrangement determination unit. This allows the cultivation environment for the plants to be grown to be determined based on the arrangement decided by the arrangement determination unit based on the analysis results. This makes it possible to precisely create the optimal environmental conditions for plant growth, resulting in improved productivity and uniform quality. Furthermore, because it can flexibly respond to changes in the environment and the growth stage of the plants, efficient and sustainable cultivation management becomes possible.

[0011] Here, the arrangement of cultivation systems may include aeroponics, vertical farming systems, fogponics, LED grow rooms, indoor soil-based farming, nutrient film technique (NFT), closed-loop greenhouses, bioponics, microgreen farming, etc., but it is preferable to include information on the arrangement of at least one of hydroponics or aquaponics, which are central elements of indoor urban farming.

[0012] Furthermore, it is preferable to include a simulation unit that simulates the arrangement of the cultivation system determined by the arrangement determination unit and the cultivation environment determined by the cultivation environment determination unit, and predicts their effects. In such a system, the simulation unit plays a role in predicting the performance of the cultivation system (yield, quality, energy consumption, etc.) in detail, based on the layout and environmental conditions determined by the layout determination unit and the cultivation environment determination unit. This makes it possible to visualize the system's effects before installation and make optimal adjustments during the design phase. Furthermore, because it is possible to compare and evaluate cultivation effects under different conditions, it becomes possible to respond flexibly to various environmental factors, thereby reducing operational risks and costs.

[0013] Furthermore, it would be preferable to further include visualization means for visualizing the arrangement of the cultivation system determined by the arrangement determination unit. Visualizing the layout of the cultivation system allows operators to intuitively understand the overall system configuration after installation. This enables them to check the appropriateness and efficiency of the layout plan in advance and identify areas for improvement as needed. Furthermore, the visualized information facilitates communication among stakeholders and accelerates decision-making in the installation process.

[0014] Furthermore, an implementation information generation unit may be further provided that automatically generates guidelines and procedures necessary for implementing the arrangement of the cultivation system determined by the arrangement determination unit. By providing such an implementation information generation unit, the installation process of the cultivation system is streamlined, and it becomes possible for the operator to accurately construct the system without getting lost. Specifically, the location of installation, the order of installation, a list of necessary equipment and parts, the connection method, etc. are automatically presented, making it possible to significantly reduce the labor and human errors during installation (it becomes possible to achieve unified understanding among designers and constructors involved in the implementation of the arrangement of the cultivation system).

[0015] Note that the arrangement of the cultivation system determined by the arrangement determination unit may be determined by inputting the input data including the environmental conditions analyzed by the data analysis unit into a learning model that has been pre-trained on the correlation with the arrangement of the cultivation system indoors. With such a configuration, based on the input data including the environmental conditions obtained by the data analysis unit, it becomes possible to quickly and accurately determine the optimal arrangement of the cultivation system based on scientific grounds. Also, by utilizing the model that has learned the correlation between past data and environmental conditions and the arrangement, a flexible and highly accurate arrangement proposal can be realized even under complex conditions.

[0016] Also, the cultivation environment determined by the cultivation environment determination unit may be determined by inputting the input data including the environmental conditions analyzed by the data analysis unit into a learning model that has been pre-trained on the correlation with the cultivation environment of the plant to be cultivated. With such a configuration, based on the input data including the environmental conditions obtained by the data analysis unit, it becomes possible to accurately and efficiently determine the optimal cultivation environment for the plant to be cultivated based on scientific grounds. Also, by utilizing the learning model, environmental adjustment that appropriately considers the correlation with past data and the characteristics of the plant is realized, and the growth and quality of the plant are maximally improved.

Effects of the Invention

[0017] As described above, according to the indoor urban farming support system, indoor urban farming support method, and indoor urban farming support program according to the present invention, the following effects can be obtained.

[0018] According to the present invention, by means of an indoor urban farming support system, such as an "indoor 3D point cloud data acquisition unit that performs 3D scanning of an indoor space to acquire indoor three-dimensional point cloud data", an "indoor image acquisition unit that acquires indoor image data", and an "indoor positioning data acquisition unit that acquires indoor positioning data", detailed spatial data can be efficiently acquired and integrated. Based on this integrated data, an optimal cultivation system layout based on scientific grounds is determined, so that "a more effective layout plan that does not depend on a sensory design" can be provided. In this way, it becomes possible to make the most of the limited space in urban areas.

[0019] Furthermore, in the present invention, an environmental sensor for measuring indoor environmental data is used to monitor environmental conditions such as temperature, humidity, light intensity, and CO2 concentration in real time and automatically adjust them as necessary. Therefore, it becomes possible to provide an environment optimal for plant growth while minimizing energy consumption in artificial lighting and temperature control. This effect realizes an improvement in energy efficiency and the ensuring of sustainability.

[0020] Also, through a data integration unit that integrates indoor three-dimensional point cloud data, indoor image data, indoor positioning data, and environmental data acquired while moving or stopping a mobile device, and a data analysis unit that analyzes indoor environmental conditions based on the integrated data, an automated data collection and analysis process is realized, significantly reducing operations such as water and nutrient supply, temperature and humidity management, and lighting adjustment that the operator conventionally performed manually. As a result, the burden on the operator is reduced, and efficient operation is possible without the need for specialized knowledge.

[0021] Furthermore, by designing resource recycling systems that utilize hydroponics and aquaponics, it is possible to efficiently use water and nutrients while reducing waste generation. Therefore, it is possible to promote the recycling of resources and construct a sustainable cultivation system that minimizes environmental impact.

[0022] As described above, the present invention solves problems such as space shortages in urban areas, increased energy consumption, maintenance burdens, lack of circular agriculture, and lack of expertise, and realizes a system that provides new value by achieving both an improved quality of urban life and a reduction in environmental impact. [Brief explanation of the drawing]

[0023] [Figure 1] This is a schematic diagram showing an example configuration of the indoor urban farming support system according to the present invention. [Figure 2] This flowchart illustrates a series of processes for determining the optimal placement of cultivation systems and the environmental conditions for those systems. [Figure 3] This diagram shows examples of indoor layouts for cultivation systems; (a) shows an example of an aquaponics system, and (b) shows an example of a hydroponic cultivation system. [Figure 4] This figure shows an example of the configuration of a machine learning device used in the present invention. [Figure 5] This is a schematic diagram showing another configuration example of the indoor urban farming support system according to the present invention. [Modes for carrying out the invention]

[0024] Hereinafter, embodiments of the indoor urban farming support system according to the present invention will be described with reference to the attached drawings. Figure 1 shows the overall configuration of the indoor urban farming support system S. This indoor urban farming support system S has the function of moving a mobile device M (a self-propelled mobile body such as a self-propelled robot or drone, or a portable device consisting of a shoulder-strap type device or a backpack-type device) to measure each room and other spaces indoors (in the case of a private house, corridors, entrances, stairs, storage spaces, open spaces, etc.; in the case of an office building, spaces for movement and connection, common spaces, functional spaces, decorative or comfort-enhancing spaces, etc.) and collecting environmental data (temperature, humidity, CO2 concentration, light intensity, etc.) and structural data (indoor three-dimensional point cloud data, indoor image data, indoor positioning data, etc.).

[0025] This indoor urban farming support system S may use only sensors installed on a mobile device (self-propelled mobile body or portable device), but in this example, various data is collected by sensors installed on the mobile device and other fixed sensors fixed in appropriate locations indoors. The collected data is then integrated and stored in a data management server 40, such as a cloud server or on-premise server, and analyzed.

[0026] 1. Regarding the sensors provided on the mobile device M In Figure 1, the main sensors installed on the mobile device M are the following: LiDAR1, camera2, indoor positioning sensor3 (Wi-Fi sensor and inertial measurement unit (IMU) for collecting positioning data), temperature sensor, humidity sensor, CO2 sensor, and light sensor. (1)LiDAR (Light Detection and Ranging) LiDAR1 uses laser light to 3D scan indoor spaces, acquires indoor three-dimensional point cloud data, and creates a detailed indoor 3D spatial model by performing 3D mapping based on distance and position information. The data that can be collected by this LiDAR1 primarily includes the following: (a) Distance data (b) Shape of walls and objects (c) Indoor spatial structure

[0027] (2) Camera Camera 2 captures high-resolution images and videos and integrates them with LiDAR data to create indoor 3D maps, thereby supplementing visual data and improving mapping accuracy. The data that can be collected by this camera 2 mainly includes the following: (a) Visual data (images) (b) Visual data (video) (c) Color information.

[0028] (3) Wi-Fi sensor The Wi-Fi sensor is a device that accurately determines the indoor location of a mobile device M. It is used to correlate data collected by LiDAR1 and camera2 to the device's precise location within the indoor space. This ensures accurate location information for the collected data. In other words, it is used to accurately map the collected data to the indoor space. By measuring the latitude, longitude, and altitude of the mobile device M, or by measuring its position in a coordinate system uniquely set up indoors, the location information of the indoor space and the data are accurately linked. The data that can be collected by Wi-Fi sensors mainly includes the following: (a) Indoor location information of mobile device M (b) Precise positional coordinates assigned to data collected by LiDAR or cameras

[0029] (4)IMU(Inertial Measurement Unit) The IMU is a device that measures the movement of a mobile device M. It is installed inside the mobile device M and is used for position correction of data acquired by LiDAR1 and camera2. In other words, by measuring the acceleration and angular velocity of the mobile device M, it plays a role in assisting data alignment, thereby improving the accuracy of the collected data. The data that can be collected by the IMU mainly includes the following: (a) Posture information of mobile device M (b) Motion of mobile device M (acceleration, angular velocity)

[0030] (5) Temperature sensor The temperature sensor 5 measures the temperature in each area of ​​the room in real time, providing basic data for analyzing heat flow and environmental conditions. The data that can be collected by the temperature sensor 5 mainly consists of the following: (a) Ambient temperature (Celsius) (b) Temperature distribution

[0031] (6) Humidity sensor The humidity sensor 6 measures indoor humidity, contributing to the evaluation of comfort levels and the optimization of plant growth conditions. The data that can be collected by the humidity sensor 6 mainly consists of the following: (a) Relative humidity (%)

[0032] (7) CO2 sensor The CO2 sensor 7 measures indoor CO2 concentration in real time, which is useful for collecting data to optimize cultivation system layout and environmental improvements. The data that can be collected by the CO2 sensor 7 mainly consists of the following: (a) CO2 concentration (ppm)

[0033] (8) Light sensor The light sensor 8 is used to measure the intensity and distribution of light in indoor environments and to identify the optimal growing environment. The light sensor monitors the light conditions inside the building in real time and measures the distribution and intensity of lighting to evaluate the optimal placement and environment for plant growth. It also plays a role in supporting the optimization of the growing environment by integrating data from other environmental sensors (temperature, humidity, CO2 concentration, etc.). The data that can be collected by the light sensor 8 mainly consists of the following: (a) Light intensity (Lux): Measure the intensity of light in each area. (b) Light distribution: Understand the illuminance distribution of the entire space and use it in the layout design.

[0034] 2. Fixed sensors installed in addition to the mobile device M The fixed sensors 20, which are not part of the mobile device M and are installed in appropriate locations indoors (each room, corridor, toilet, etc.) to obtain environmental data for those areas, are, as shown in Figure 1, a temperature sensor 21, a humidity sensor 22, a CO2 sensor 23, a light sensor 24, and a water sensor 25.

[0035] (1) Temperature sensor The temperature sensor 21 is fixedly installed in a suitable location indoors to continuously monitor indoor temperature fluctuations and provide basic data for analyzing heat flow. The data that can be collected by the temperature sensor 21 mainly consists of the following: (a) Ambient temperature (Celsius) (b) Temperature distribution

[0036] (2) Humidity sensor The humidity sensor 22 is fixedly installed in a suitable location indoors and continuously monitors humidity conditions in each area to support the evaluation of comfort and environmental conditions. The data that can be collected by the humidity sensor 22 mainly consists of the following: (a) Relative humidity (%)

[0037] (3) CO2 sensor The CO2 sensor 23 is permanently installed in a suitable location indoors to monitor the CO2 concentration in the indoor environment and is used for planning environmental improvements and plant placement. The data that can be collected by the CO2 sensor 23 mainly consists of the following: (a) CO2 concentration (ppm)

[0038] (4) Light sensor The fixed light sensor 24 monitors the light conditions of the indoor environment and supports the efficient operation of the cultivation system. The fixed light sensor continuously measures the intensity and distribution of light at the installation location, and the plant placement and cultivation environment are adjusted based on this data. The data acquired by the light sensor 24 is incorporated into a placement optimization algorithm to scientifically evaluate the optimal light environment for plant growth. Based on this data, the brightness and duration of artificial lighting are automatically adjusted, making it possible to reduce energy consumption while supporting plant growth. Furthermore, the data from the fixed light sensor is integrated with data from other sensors (temperature, humidity, CO2 sensors, etc.) to help analyze the overall indoor environment. The data that can be collected by the light sensor 24 mainly consists of the following: (a) Light intensity (Lux): The intensity of light in each area is measured. (b) Light distribution: Understand the illuminance distribution of the entire space and use it to design plant placement.

[0039] (5) Water sensor The water sensor 25 accurately measures the amount and quality of water necessary for plant growth and is used to maintain an optimal cultivation environment. This water sensor 25 monitors the water content of the cultivation system in real time and provides appropriate water supply to prevent water shortages or oversupply. It also measures water quality data such as pH, dissolved oxygen, and electrical conductivity (EC), managing a suitable water environment for plants. Furthermore, this data is integrated with other environmental data (light, temperature, humidity) and used in AI and optimization algorithms. The water sensor 25 also contributes to a circular system, supporting a sustainable cultivation system by efficiently reusing water resources. In this way, the water sensor 25 enables efficient resource management and automation, playing a role in building the foundation for sustainable agriculture in urban areas. The data that can be collected by the water sensor 25 mainly consists of the following: (a) Moisture content: Monitor fluctuations in the moisture content of the soil and the water tank. (b) pH value: Measures the acidity or alkalinity of water. (c) Dissolved oxygen (DO value): Measurement of the oxygen concentration in water. (d) Electrical conductivity (EC value): Used to evaluate the concentration of nutrients and dissolved substances in water. (e) Water temperature: Measure the water temperature and maintain it within a range suitable for plant growth.

[0040] Data collected by each sensor mounted on the mobile device M is transmitted in real time from the data transmission unit 10 to the data management server 40 via the network 11. Data acquired from fixed sensors 20 installed in addition to the mobile device M is also sent to the data management server 40 via a transmission control unit (not shown) and the network 11.

[0041] The data management server 40 receives various data transmitted from the mobile device M and the fixed sensor 20 in the data receiving unit 41, integrates the acquired data received by the data receiving unit 41 in relation to the measurement time in the data integration unit 42, and stores it. Then, the data management server 40 performs data analysis in the data analysis unit 43 based on the data stored in the data integration unit 42.

[0042] In this section, the data analysis unit 43 evaluates and analyzes the indoor environmental conditions based on the collected environmental data, and supports the design and operation of the optimal cultivation environment. Specifically, the following items are subject to analysis. (1) Analysis of lighting conditions By analyzing light intensity and distribution data acquired from light sensors, the optimal light environment for plant growth is identified. (2) Analysis of temperature and humidity Based on data obtained from temperature and humidity sensors, indoor climate conditions are evaluated to determine a temperature and humidity environment suitable for plant growth. (3) Analysis of CO2 concentration Data from CO2 sensors is analyzed to maintain the optimal CO2 concentration for promoting plant photosynthesis. (4) Analysis of the aquatic environment By analyzing water content and water quality data (pH value, dissolved oxygen level, etc.) obtained from water sensors, it supports appropriate water supply and water quality management. (5) Analysis of spatial conditions We analyze 3D scan data and indoor positioning data to evaluate spatial requirements for plant placement and system design.

[0043] These analysis results are input into AI and placement optimization algorithms, which are used to determine the placement of cultivation systems and to design them. Then, based on the environmental conditions (light, temperature, humidity, CO2 concentration, etc.) obtained by the data analysis unit 43 and the characteristics of the plants to be cultivated (required conditions and growth stage), the arrangement determination unit 44 determines the optimal arrangement of the cultivation system to optimize the environmental conditions. Subsequently, based on the decided layout plan, the environmental condition determination unit 45 determines the environmental conditions (light intensity, temperature and humidity, CO2 concentration, water supply, etc.) for the cultivation system (plants and equipment) to be installed.

[0044] Here, "optimization of environmental conditions" and "optimal placement of the cultivation system" are interrelated and complementary. "Optimization of environmental conditions through the placement of the cultivation system" aims to collect and analyze environmental data such as indoor temperature, humidity, CO2 concentration, and light intensity, and to design an optimal indoor environment (for example, an environment where indoor environmental data are within a predetermined standard range). In this process, scientific data that forms the basis for improving comfort and productivity is collected.

[0045] On the other hand, "optimal placement of cultivation systems" aims to maximize cultivation efficiency and environmental improvement effects by optimizing the placement of cultivation systems using this scientific data. Specifically, data obtained from the analysis of environmental conditions (for example, areas with high light intensity, areas with high CO2 concentration, and areas where temperature and humidity are appropriately maintained) is directly used in planning the placement of cultivation systems. For example, placing a cultivation system in an area with high light intensity can increase the photosynthetic efficiency of plants and improve their growth rate. However, if the light intensity is too high, appropriate shading must be considered to prevent light stress. Furthermore, placing the system in an area with high CO2 concentration can further enhance photosynthetic effects and create a more efficient cultivation environment. Furthermore, not only light intensity but also the overall balance of environmental conditions is crucial. For example, placing plants in areas where temperature and humidity are appropriately maintained optimizes plant growth conditions and enables stable harvests. Since the cultivation system itself influences the environment, a properly positioned group of systems harmonizes the indoor environment while optimizing conditions such as light intensity, CO2 concentration, temperature, and humidity, thereby improving cultivation productivity.

[0046] Therefore, "optimizing environmental conditions" provides a data base for the placement of cultivation systems, and "optimizing the placement of cultivation systems" utilizes that data to concretely realize environmental improvement effects. In this way, the two are closely correlated in a series of processes for enhancing indoor comfort and sustainability. Therefore, the following placement optimization algorithm is used. The procedure for forming the placement optimization algorithm is realized through a series of processes that involve collecting indoor environmental data, analyzing it, and determining the optimal placement based on the results. First, the purpose of the placement optimization algorithm will be explained.

[0047] 1. Objective of the placement optimization algorithm The placement optimization algorithm analyzes environmental data acquired from various sensors and structural information of the building to determine the optimal placement of the cultivation system to achieve the following objectives. (Maximize plant growth) The goal is to identify the most suitable locations for plant growth based on environmental data (light, temperature, humidity, CO2 concentration, etc.) and to efficiently arrange plants in those locations. (Effective use of urban space) Maximize the use of limited urban space and improve spatial efficiency based on the placement of plants. (Efficient use of resources) By optimizing the placement of each plant, resources such as energy, nutrients, and water are distributed efficiently. (Improved system efficiency) Based on the placement results, the supply of light and nutrients is optimized, improving the overall operational efficiency of the cultivation system.

[0048] 2. A series of processes for determining the optimal placement Next, Figure 2 shows a series of processes for determining the optimal setup and environmental conditions for the cultivation system (hydroponic or aquaponics system). This process will be explained in detail below.

[0049] 2.1 Data Collection (Step S11) Data collection involves gathering information necessary to understand the indoor environment and determine the optimal placement of the cultivation system. This stage utilizes sensors mounted on mobile devices M and fixed sensors 20 installed indoors.

[0050] 2.1.1 Utilization of sensors mounted on mobile devices The mobile device M, equipped with a LiDAR (Light Detection and Ranging), camera, temperature sensor, humidity sensor, CO2 sensor, and light sensor, collects 3D data and environmental data of the indoor space (each room and areas outside the rooms) while the mobile device M is moving or stationary. For example, in the house shown in Figure 3, the mobile device M is moved through each room and other areas as indicated by the dashed lines to collect the following data in all directions. (1) LIDAR1: 3D maps the indoor structure and room shape. (2) Camera 2: Acquires high-resolution images and complements the LIDAR data. (3) Environmental sensors (temperature sensor 5, humidity sensor 6, CO2 sensor 7, light sensor 8): These sensors measure indoor temperature, humidity, CO2 concentration, and light intensity in real time. They also measure indoor heat distribution. These data are used to analyze indoor environmental conditions in detail, determine the optimal placement of the cultivation system, and determine the optimal environmental conditions (light intensity, temperature and humidity, CO2 concentration, water supply, etc.) for the determined cultivation system placement.

[0051] 2.1.2 Use of Fixed Sensors Fixed sensors installed in each room and other areas of the building continuously collect indoor environmental data. For example, fixed sensors 20 are arranged as shown in Figure 3 to measure the following items. Fixed sensors 20 (temperature sensor 21, humidity sensor 22, CO2 sensor 23, light sensor 24, water sensor 25): Continuously monitor environmental data (temperature, humidity, CO2 concentration, light intensity, water quality data, etc.) in each area of ​​the indoors.

[0052] 2.2 Data Integration (Step S12) The data integration unit 42 has the function of integrating the data collected from each sensor and organizing it into an analyzable format. By integrating the collected data in association with the measurement time, positioning data of mobile devices, and installation location of fixed sensors, it constructs a foundation for creating a deployment plan. Specifically, it integrates the following data: Correlation with measurement time: Data collected from each sensor is integrated based on the measurement time to create a time-series dataset. Mobile device positioning data: Correlate indoor positioning data from mobile devices to determine the precise spatial location at the time of data collection. Fixed sensor installation location: Data from indoor fixed sensors 20 is integrated based on their installation location to unify spatial information and environmental data. The integrated data is provided as a base dataset for use in the subsequent data analysis unit 43.

[0053] 2.3 Data Analysis (Step S13) The data analysis unit 43 has the function of analyzing indoor environmental conditions based on the data integrated by the data integration unit 42. Specifically, the following analysis will be performed: (1) Analysis of lighting conditions By analyzing light intensity and distribution data acquired from light sensors, the optimal light environment for plant growth is identified. (2) Analysis of temperature and humidity Based on data obtained from temperature and humidity sensors, indoor climate conditions are evaluated to determine a temperature and humidity environment suitable for plant growth. (3) Analysis of CO2 concentration Data from CO2 sensors is analyzed to maintain the optimal CO2 concentration for promoting plant photosynthesis. (4) Analysis of the aquatic environment By analyzing water content and water quality data (pH value, dissolved oxygen level, etc.) obtained from water sensors, it supports appropriate water supply and water quality management. (5) Analysis of spatial conditions We analyze 3D scan data and indoor positioning data to evaluate spatial requirements for plant placement and system design. (6) Integrated analysis of data It integrates data from various sensors to provide fundamental information for optimizing the layout of cultivation systems and plant arrangements. These analysis results are passed to the arrangement determination unit 44 and the environmental condition determination unit 45 as indicators for optimizing the arrangement of the cultivation system and the environmental conditions for cultivation.

[0054] 2.4 Determining the layout of the cultivation system (Step S14) The placement determination unit 44 determines the optimal placement of the cultivation system. In this example, a learning model may be used to determine the optimal placement of the cultivation system (optimal placement for hydroponics, optimal placement for aquaponics system). Specifically, a machine learning device 50 that estimates the optimal placement of the cultivation system based on the analysis results analyzed by the data analysis unit 43 is installed on the data management server 40 (see Figure 1). The machine learning device 50, which estimates the optimal arrangement of the cultivation system, uses the following analysis results (1) to (5) from the analysis results analyzed by the data analysis unit 43, as shown in Figure 4, (1) Analysis results of light conditions: Light intensity and suitability score for each area (2) Analysis results of temperature and humidity: Appropriate temperature and humidity range and suitability evaluation for each area (3) Analysis results of CO2 concentration: Optimal area and concentration distribution for photosynthesis (4) Analysis results of spatial conditions: available space and location of obstacles (5) Data integration analysis results: Overall suitability score for each area (integrated evaluation of light, temperature and humidity, and CO2 concentration) It has an input data acquisition unit 51 that acquires plant data (growth conditions for each plant species (required light amount, temperature and humidity, CO2 concentration), required space (width, depth, height)) as input data.

[0055] Furthermore, the machine learning device 50 includes a label acquisition unit 52 that acquires the optimal placement coordinates (e.g., [x, y, z]) of each cultivation system and equipment, and a zoning map after placement (an overall view of the area where plants and equipment are placed) as labels, and a learning model construction unit 53 that constructs a learning model 55 by performing supervised learning using the input data and label pairs as training data.

[0056] 2.5 Determination of environmental conditions in the determined configuration (Step S15) The environmental condition determination unit 45 determines the optimal environmental conditions for the cultivation system layout determined by the layout determination unit 44. In this example, a learning model may be used to determine environmental conditions for the determined cultivation system layout. Specifically, the machine learning device 50 is provided with a function to estimate the environmental conditions required for the determined cultivation system layout based on the analysis results analyzed by the data analysis unit 43 (see Figure 1).

[0057] The machine learning device 50, which estimates the environmental conditions necessary for the determined cultivation system layout, has an input data acquisition unit 56 that acquires the following analysis results from the data analysis unit 43, along with plant growth conditions, cultivation system layout information, and resource constraints, as input data, as shown in Figure 4. (1) Analysis results (a) Analysis results of light conditions: Light intensity and suitability score for each area (b) Analysis results of temperature and humidity: Appropriate temperature and humidity range and suitability evaluation for each area (c) Analysis results of CO2 concentration: Optimal area and concentration distribution for photosynthesis (d) Analysis results of the aquatic environment: Water supply amount and water quality data (pH value, dissolved oxygen level, etc.) necessary for growth. (2) Plant growth conditions and (a) Environmental conditions required by each plant (e.g., light intensity, temperature and humidity, CO2 concentration) (b) Growth stages of each plant (e.g., germination stage, growth stage, harvest stage) (3) Layout information of the cultivation system (a) Coordinates of the cultivation system (e.g., [x, y, z]) (b) Information on equipment installed in each area (e.g., lighting, humidifiers, CO2 supply devices) (c) Placement coordinates and characteristics of the equipment (e.g., maximum light intensity, irrigation capacity) (4) Resource constraints (a) Maximum capacity of the equipment (e.g., the range that one CO2 supply unit can cover) (b) Reach of piping and power supply

[0058] Furthermore, the machine learning device 50 includes a label acquisition unit 57 that acquires the following environmental setting values ​​as labels, and a learning model construction unit 58 that constructs a learning model 59 by performing supervised learning using the input data and label pair as training data. Environment settings (1) Lighting Light intensity adjustment value (e.g., set to 800 lx for each area) Lighting schedule (Example: 12 hours of lighting per day) (2) Temperature and humidity Temperature setting (Example: Area A is 25°C, Area B is 20°C) Humidity setting values ​​(e.g., Area A is 60%, Area B is 70%) (3)CO2 supply CO2 concentration replenishment amount (e.g., add +50 ppm to area B) Supply schedule (e.g., 3 times a day, morning, noon, and night) (4) Irrigation Water supply amount (e.g., 2 liters / day in area C) Irrigation schedule (Example: Twice a day, at 8:00 AM and 2:00 PM)

[0059] The procedure for creating each learning model is as follows: A. Procedure for creating a learning model to determine the layout plan the purpose It generates layout plans that optimize the placement of plants and cultivation systems, maximizing efficient use of space and optimal plant growth conditions. procedure A-1 Setting the Objective Maximize plant growth and improve space efficiency. Determine the arrangement that meets the necessary conditions (light, temperature, humidity, CO2 concentration). A-2 Data Collection Spatial data: Indoor 3D scan data, positioning data Environmental data: Light intensity, temperature and humidity, CO2 concentration, etc. Plant data: Growth requirements for each plant species (e.g., required light and humidity) A3 Data Preprocessing Implanting missing values ​​in the data Convert spatial data into a format that the model can understand (e.g., grid format). Integration of environmental and spatial data A-4 Model Design Deep Reinforcement Learning: The state (spatial data + environmental data), action (plant placement), and reward (growth efficiency and space utilization efficiency) are defined. Optimization algorithms (genetic algorithms, simulated annealing): These generate candidate placements, evaluate each candidate, and select the optimal placement. A-5 Model Training A simulation environment is built, and the model learns placement options through trial and error. The reward function is adjusted to optimize the balance between placement efficiency and plant growth efficiency. Evaluation and tuning of the A-6 model Evaluate the accuracy of the layout plan (e.g., plant growth rate, space utilization rate) Improve the model as needed (adjust the reward function, add data). A-7 operation Generate a layout plan and implement it in the actual system. Collect new data and continuously update the model.

[0060] B. Procedure for creating a learning model that adjusts environmental conditions the purpose The system dynamically adjusts environmental conditions such as light, temperature, humidity, CO2 concentration, and water supply in the cultivation system to promote plant growth. procedure B-1 Setting the Objective The goal is to maximize energy consumption and water resource efficiency while meeting the growth requirements of each plant. B-2 Data Collection Sensor data: Real-time light intensity, temperature and humidity, CO2 concentration, and water quality data. Plant growth data: growth rate, yield, health status B-3 Data Preprocessing Organizing time-series data (e.g., smoothing sensor data) Analyzing the correlation between growth conditions and environmental conditions. Design of the B-4 model Regression models (random forest, linear regression): Learn the relationship between environmental conditions and plant growth. Time series forecasting models (LSTM, GRU): Predict fluctuations in environmental conditions and determine the optimal timing for adjustments. Control models (PID control, model predictive control): Dynamically adjust environmental conditions. B-5 Model Training Training using past sensor data and plant growth data Set an objective function (e.g., maximize growth efficiency, minimize energy consumption). Evaluation and tuning of the B-6 model Evaluate the results of adjusting environmental conditions through simulation. The model is improved based on growth conditions. B-7 operation Adjust environmental conditions in real time based on sensor data. We provide feedback on the adjustment results and continuously improve the model.

[0061] Through these procedures, the learning model 55 that generates the layout plan and the learning model 59 that adjusts the environmental conditions are built independently. However, in the operational phase, the two work together, complementing each other's data to provide an efficient and sustainable cultivation environment. These two learning models 55 and 59 work together to create an efficient cultivation environment. First, learning model 55, which seeks placement options, proposes the optimal initial placement. Based on this placement, learning model 59 adjusts environmental conditions in real time, maximizing plant growth and resource efficiency. This combination realizes a sustainable cultivation system that overcomes the space constraints and environmental burdens of urban areas.

[0062] The trained model takes data from indoor environment sensors as input and generates a proposed layout for the cultivation system in real time. This layout is visualized on a display unit 101, such as a data management server, a PC monitor owned by a relevant party, an AR / VR device, or a portable device, as shown in Figure 3. This display unit 101 corresponds to the visualization means that visualizes the layout of the cultivation system. Furthermore, the model will be periodically updated using newly collected data during operation to adapt to changes in environmental conditions.

[0063] The example shown in Figure 3 illustrates an indoor layout of an aquaponics system. In this example, the layout determination unit 44 determines that the aquaponics system should be placed in the upper left room of the indoor area as shown in the figure, and this is visualized and displayed on the display unit 101. The aquaponics system has four main components: a fish tank 31, a filter 32, a plant bed 33, and a pump 34. These components are connected by a loop-shaped piping system 35, and the circulation of water promotes interaction between fish and plants.

[0064] Description of each component (1) Fish Tank Fish tank 31 is the central element of the system and is where the fish are kept. Fish waste is released into the water, which serves as a nutrient source for the plants. To maintain the health of the fish, it is necessary to manage the water temperature and oxygen levels to ensure they are appropriate. (2) Filter The filter 32 is a device that processes the water supplied from the fish tank 31. This filter 32 not only removes solid matter (fish waste and leftover food), but also plays a role in converting ammonia into nitrates using nitrifying bacteria. This generates nutrients in a form that is easily absorbed by plants. (3) Plant bed (Grow Bed) The plant bed 33 is where hydroponics takes place. Nutrient-rich water treated by the filter 32 is supplied here, and plants grow. The plant roots absorb nutrients from the water while simultaneously purifying it. Gravel and clay balls are laid in the bed to distribute the water flow. (4) Pump Pump 34 plays the role of circulating water and maintaining the overall function of the system. It continuously sends water from the fish tank 31 to the filter 32, the plant bed 33, and back to the fish tank 31. The smooth operation of pump 34 ensures that the entire system operates stably.

[0065] This aquaponics system significantly reduces waste because the waste generated in the fish tank 31 is used efficiently as nutrients for the plants. Furthermore, the plants absorb nutrients from the water and grow efficiently, allowing for earlier harvests than traditional soil cultivation. Additionally, the water is purified in the plant bed 33, maintaining a healthy environment for the fish. Thus, because the aquaponics system forms a closed, self-sustaining cycle, the supply of fertilizer and water from external sources is minimized.

[0066] Alternatively, a hydroponic system may be selected instead of the aquaponics system described above. In the case of a simple circulating hydroponic system (reservoir circulation type) that uses a plant bed 33 and a nutrient solution tank 36, if the placement determination unit 44 determines the placement in the same position, the part of the cultivation system shown in Figure 3(a) can be replaced with, for example, a configuration in which the nutrient solution tank 36, filter 32, pump 34, and plant bed 33 are connected in a loop by a piping system 35 (Figure 3(b)).

[0067] 2.6 Simulation (Step S16) After the arrangement of the cultivation system and the environmental conditions for that arrangement have been determined as described above, a simulation is performed in the simulation unit 46 using this determined arrangement and environmental conditions. This simulation predicts the effect of environmental improvement after the arrangement is completed. For example, the following items are evaluated quantitatively: (1) CO2 absorption The amount of CO2 absorbed by the placed plants is calculated, and the air purification effect in the room is evaluated. (2) Temperature and humidity control effect The system predicts the impact of placed plants on indoor temperature and humidity and measures the degree of improvement in comfort. (3) Degree of improvement in air circulation We will analyze how airflow improves with each layout proposal, and evaluate improvements in ventilation and reduction of dead space, in particular. (4) Uniformity of illuminance distribution We will analyze how the placement of plants and lighting equipment affects the light distribution and confirm whether the plants receive adequate illumination. (5) Water resource utilization efficiency Through simulations, the system calculates the water supply and usage efficiency of irrigation systems, providing data to prevent oversupply and shortages. (6) Energy consumption We will calculate the energy consumption from the operation of lighting and air conditioning equipment and verify whether efficient operation is possible. Furthermore, these results are also visualized on the display unit 101, allowing stakeholders to intuitively understand the effectiveness of the layout plan. At this stage, the appropriateness of the proposed layout is confirmed, and modifications are made as necessary.

[0068] 2.7 Creation of implementation guidelines for the deployment plan (Step S17) Based on the cultivation system layout plan confirmed by simulation, guidelines and procedures necessary for implementing the cultivation system layout are automatically generated. These guidelines include the following: (1) Types and components of the cultivation system to be used Detailed configuration and installation plan for hydroponic systems (e.g., NFT systems, drip systems) and aquaponics systems (e.g., fish tanks, plant beds, filters, pumps). (2) Installation procedure The installation sequence for each component (plant bed, pump, piping, sensors, etc.), the necessary tools, and points to note (e.g., preventing leaks in the fish tank, ensuring secure pipe connections). (3) Necessary materials and equipment Specify the exact materials (piping, culture medium, support materials, etc.) and equipment (pumps, sensors, lighting devices, irrigation equipment) and their quantities, as well as the necessary conditions for their installation (space, power supply points, etc.). (4) Operation method The operational procedures after the system is operational will include the circulation schedule within the cultivation system, the procedure for replenishing nutrient solution, the calibration of sensors, and the method for monitoring the equipment. (5) Maintenance plan This document should include regular maintenance items (e.g., cleaning of piping, filter replacement, pump operation check) and their frequency, as well as procedures for responding to malfunctions (e.g., emergency procedures for water leaks). (6) Methods for adjusting environmental conditions The documentation should specify adjustment procedures and recommended ranges for maintaining environmental conditions (e.g., water temperature, light intensity, CO2 concentration, humidity) suitable for the system. Specifically, it should outline settings such as lighting adjustments, CO2 supply schedules, and water quality management (pH, DO, EC values). Furthermore, these guidelines should be digitized and provided in a format accessible to contractors and operators (e.g., mobile devices and cloud-based platforms). Visualization of layout plans and operational plans should also be implemented to improve usability and understanding at construction sites. It is also recommended to incorporate a mechanism that allows for dynamic updates of the guidelines in response to changes and environmental conditions. By creating detailed and practical guidelines in this way, the feasibility of the deployment plan improves, and the overall efficiency and accuracy of the process increase.

[0069] 2.8 Implementation, Monitoring, and Adjustment (Step S18) 2.8.1 Implementation Based on the guidelines, we will install hydroponic or aquaponics systems. The installation work will proceed according to the following procedure. (1) Arrangement of main components For hydroponic systems, install the plant beds, nutrient solution tanks, piping, and pumps in the designated locations. For aquaponics systems, connect the fish tanks, filters, plant beds, and pumps in the correct order to construct the piping system. (2) Securing electricity and water sources Verify that each system has a power supply (pumps, sensors, lighting) and water source (nutrient solution tank or water connection) to ensure proper operation, and perform the necessary wiring and piping. (3) Sensor placement and calibration Light sensors, temperature sensors, humidity sensors, CO2 sensors, and water quality sensors are installed in each system, and their operation is verified and initial calibration is performed. 2.8.2 Monitoring After installation, the monitoring system is activated to monitor the system's operating status and environmental conditions in real time. (1) Acquisition and analysis of sensor data Data collected from each sensor (light intensity, temperature and humidity, CO2 concentration, water quality, etc.) is sent to a cloud server for analysis. This allows for evaluation of whether environmental conditions are being maintained appropriately. (2) Operation check of the cultivation system Monitor the pump's operation, the circulation of the nutrient solution, and the water quality in the fish tank (in the case of aquaponics) to ensure there are no abnormalities. 2.8.3 Adjustment Based on monitoring results, adjust the deployment and operating methods as needed. (1) Optimization of environmental conditions The settings for light intensity, temperature and humidity, CO2 concentration, and water quality (pH, DO, EC values) are adjusted and optimized to match the plant's growth conditions. (2) Improvement of system operation Improvements will be made to enhance the overall efficiency of the system, such as adjusting the piping, changing the pump operating speed, and adjusting the nutrient solution concentration. (3) Periodic re-evaluation By regularly re-evaluating the system's operational status and updating settings based on new data, a stable cultivation environment can be maintained in the long term.

[0070] As described above, this system allows for the integrated analysis of three-dimensional point cloud data, image data, positioning data, and environmental data of indoor spaces. This enables the scientific evaluation of the indoor environment and the optimization of the placement of hydroponic and aquaponics systems based on scientific evidence, thus providing highly accurate placement proposals that could not be obtained with conventional intuitive design (scientifically-based placement optimization).

[0071] Properly positioned hydroponic and aquaponics systems can maximize the effects of CO2 absorption, air purification, and temperature and humidity regulation, thereby improving indoor comfort and providing a healthy space for residents and users. Furthermore, improved water circulation and humidity balance can create a more comfortable environment (maximizing environmental improvement effects).

[0072] By improving insulation through the proper placement of hydroponic or aquaponics systems, it is possible to reduce indoor heating and cooling energy consumption. As a result, it is possible to improve the overall energy efficiency of the building, which is expected to reduce operating costs and the environmental impact (improved energy efficiency).

[0073] The simulation function for layout plans allows for prior verification of the effectiveness of hydroponic and aquaponics systems. This streamlines the design and construction processes. Furthermore, visualizing the layout information makes it easier to understand the design plan and ensures a unified understanding among stakeholders, thereby improving the overall efficiency of the project (streamlining the design process).

[0074] By deploying hydroponic and aquaponics systems based on scientific data, it becomes possible to provide sustainable spatial designs that enhance comfort while reducing environmental impact. This system is applicable to a variety of indoor spaces, including commercial facilities, office buildings, and public facilities, and can create long-term value (realizing sustainable spatial design).

[0075] By utilizing mobile devices (self-propelled devices and portable equipment), it is possible to collect data on dynamically changing indoor environments in real time. Based on this data, it has the flexibility to propose the optimal placement of hydroponic and aquaponics systems under various conditions. As a result, it is possible to respond quickly to changes in environmental conditions (flexibility through the use of dynamic data).

[0076] Through airflow analysis, CO2 concentration analysis, and thermal fluid analysis, indoor environmental conditions can be analyzed in detail, providing indicators for optimizing the placement of hydroponic and aquaponics systems and enabling the creation of scientifically-based placement plans (advanced environmental data analysis).

[0077] It features a function that automatically generates guidelines and procedures necessary for the placement of hydroponic and aquaponics systems, enabling designers and contractors to accurately and efficiently implement their layout plans. Furthermore, long-term effectiveness can be maintained through monitoring and adjustments (streamlining the implementation process).

[0078] Hydroponic and aquaponics systems can promote the psychological and physiological health of users through their relaxation and stress-reducing effects. Furthermore, the improved visual aesthetics these systems offer can enhance the satisfaction of residents and visitors (improving health and psychological well-being).

[0079] In addition to hydroponic and aquaponics systems, this system supports a variety of cultivation methods, including vertical farming, wall designs incorporating plants, and green floors. This system is applicable not only to commercial facilities and office buildings, but also to public facilities and residences, and is expected to have a wide range of applications (diverse range of applications).

[0080] While the above-mentioned indoor urban farming support system primarily focused on hydroponic and aquaponics systems, other cultivation systems that contribute to environmental improvement (CO2 absorption, air purification, temperature and humidity control), enhanced aesthetics and psychological effects, and sustainable spatial design can also be considered, including aeroponics, vertical farming systems, fogponics, LED grow rooms, indoor soil-based farming, nutrient film technique (NFT), closed-loop greenhouses, bioponics, and microgreen farming.

[0081] Furthermore, in the indoor urban farming support system S described above, data collected by sensors on a mobile device and fixed sensors installed in appropriate locations indoors are integrated and analyzed to determine the arrangement of the cultivation system. However, as shown in Figure 5, all sensors may be concentrated on a mobile device M, and the same processing may be performed using only the sensors installed on the mobile device M.

[0082] Furthermore, the indoor urban farming support system S described above can also be provided in the form of a program (indoor urban farming support program) that causes a computer to execute each step of the indoor urban farming support method described above. [Explanation of Symbols]

[0083] 1 LIDAR 2 cameras 3. Indoor positioning sensor 5.21 Temperature sensor 6.22 Humidity Sensor 7.23 CO2 sensor 8.24 Light Sensor 9.25 Water Sensor 42 Data Integration Department 43 Data Analysis Department 44 Placement determination section 45 Environmental Condition Determination Section 46 Simulation Implementation Department 50 Machine Learning Devices 55,59 Learning Models S Indoor Urban Farming Support System M Mobile device

Claims

1. An indoor 3D point cloud data acquisition unit that 3D scans the indoor space to acquire indoor three-dimensional point cloud data, An indoor image acquisition unit that captures images of the indoor space and acquires indoor image data, An indoor positioning data acquisition unit that acquires indoor positioning data, An environmental sensor that measures the aforementioned indoor environmental data, A mobile device is provided with the indoor 3D point cloud data acquisition unit, the indoor image acquisition unit, and the indoor positioning data acquisition unit, and is also provided with some or all of the environmental sensors. A data integration unit that acquires and integrates the indoor three-dimensional point cloud data, indoor image data, indoor positioning data, and environmental data acquired while moving or stationary the mobile device, A data analysis unit analyzes indoor environmental conditions based on the data integrated by the aforementioned data integration unit, Based on the analysis results analyzed by the aforementioned data analysis unit, the layout determination unit determines the layout of the indoor cultivation system, An indoor urban farming support system characterized by comprising the following:

2. The indoor urban farming support system according to claim 1, further comprising: a cultivation environment determination unit that determines the cultivation environment for plants to be cultivated based on the analysis results analyzed by the data analysis unit and taking into account the arrangement determined by the arrangement determination unit.

3. The indoor urban farming support system according to claim 1 or 2, wherein the determination of the arrangement of the cultivation system includes determining the arrangement of at least one of a hydroponic cultivation system and an aquaponics system.

4. The indoor urban farming support system according to claim 2, further comprising a simulation implementation unit that simulates the arrangement of the cultivation system determined by the arrangement determination unit and the cultivation environment determined by the cultivation environment determination unit and predicts its effects.

5. The indoor urban farming support system according to claim 1, further comprising a visualization means for visualizing the arrangement of the cultivation system determined by the arrangement determination unit.

6. The indoor urban farming support system according to claim 1, further comprising: an implementation information generation unit that automatically generates guidelines and procedures necessary for implementing the arrangement of the cultivation system determined by the arrangement determination unit.

7. The arrangement of the cultivation system determined by the arrangement determination unit is: The indoor urban farming support system according to claim 1, which is determined by inputting input data, including environmental conditions analyzed by the data analysis unit, into a learning model that has been pre-trained to correlate with the arrangement of an indoor cultivation system.

8. The cultivation environment determined by the cultivation environment determination unit is: The indoor urban farming support system according to claim 2, which is determined by inputting input data, including environmental conditions analyzed by the data analysis unit, into a learning model that has been pre-trained to recognize the correlation between the planting environment and the planting environment.

9. An indoor 3D point cloud data acquisition unit that 3D scans the indoor space to acquire indoor three-dimensional point cloud data, An indoor image acquisition unit that captures images of the indoor space and acquires indoor image data, An indoor positioning data acquisition unit that acquires indoor positioning data, An environmental sensor that measures the aforementioned indoor environmental data, A mobile device is provided with the indoor 3D point cloud data acquisition unit, the indoor image acquisition unit, and the indoor positioning data acquisition unit, and is also provided with some or all of the environmental sensors. An urban farming support method comprising determining the arrangement of a cultivation system, A data integration step of acquiring and integrating the indoor three-dimensional point cloud data, indoor image data, indoor positioning data, and environmental data acquired while moving or stationary the mobile device, A data analysis step which analyzes indoor environmental conditions based on the data integrated in the aforementioned data integration step, Based on the analysis results obtained in the data analysis step, a placement determination step is performed to determine the arrangement of the indoor cultivation system. An indoor urban farming support method characterized by comprising the following:

10. Based on the analysis results obtained in the data analysis step, a cultivation environment determination step is performed to determine the cultivation environment for the plants to be cultivated, taking into account the arrangement determined in the arrangement determination step. The indoor urban farming support method according to claim 9, further comprising the above.

11. The indoor urban farming support method according to claim 10, further comprising a simulation implementation step of simulating the arrangement of the cultivation system determined by the arrangement determination step and the cultivation environment determined by the cultivation environment determination step to predict its effects.

12. The indoor urban farming support method according to claim 9, further comprising a visualization step of visualizing the arrangement of the cultivation system determined by the arrangement determination step.

13. The indoor urban farming support method according to claim 9, further comprising an implementation information generation step for automatically generating guidelines and procedures necessary for implementing the arrangement of the cultivation system determined by the arrangement determination step.

14. The arrangement of the cultivation system determined in the arrangement determination step is: The method for supporting indoor urban farming according to claim 9, which is determined by inputting the input data, including the environmental conditions analyzed in the data analysis step, into a learning model that has been pre-trained to correlate with the arrangement of an indoor cultivation system.

15. The cultivation environment determined in the cultivation environment determination step is: The method for supporting indoor urban farming according to claim 10, which is determined by inputting the input data, including the environmental conditions analyzed in the data analysis step, into a learning model that has been pre-trained to recognize the correlation between the plant to be cultivated and the cultivation environment.

16. An indoor urban farming support program for causing a computer to perform each step of the indoor urban farming support method according to any one of claims 9 to 15.

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