Interior mapping system, interior mapping method, and interior mapping program

Abstract

By optimizing the placement of green walls and indoor plantings based on scientific data, we maximize CO2 absorption and air purification effects while improving the building's insulation performance. This enhances indoor comfort, improves energy efficiency, and realizes sustainable architectural design. [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 indoor greening arrangement is determined based on the integrated data.

Description

Technical Field

[0001] The present invention relates to an interior mapping technology capable of appropriately arranging green walls and plantings provided indoors, improving the comfort of the indoor environment, and enhancing energy efficiency.

Background Art

[0002] Introduction of green walls and indoor plantings: In many commercial facilities and office buildings, green walls and indoor plantings are introduced as part of the interior design. This is expected to improve the aesthetics of the space and bring a relaxation effect to employees and visitors. Furthermore, due to the CO2 absorption and air purification effects of plants, it is expected to reduce the environmental load and realize a healthy indoor environment. As technologies related to green walls and plantings, the following patent documents and the like are known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in many cases of introducing green walls and plantings, the arrangement of green walls and plantings is carried out with emphasis on the designer's sense and aesthetics, and optimization of the arrangement based on scientific data has not been performed. In addition, the effects of CO2 absorption and air purification by plants are rarely specifically measured, and it is unclear how high the effects are. Furthermore, although there is a possibility of enhancing the heat insulation effect of the building by the arrangement of plantings, the effect has not been sufficiently studied, so improvement of energy efficiency has not been realized. Therefore, solving these challenges requires new technologies based on scientific data and analysis. This invention has been made in view of the above circumstances, and its main objective is to provide an interior mapping system, interior mapping method, and interior mapping program that maximizes CO2 absorption and air purification effects, improves the thermal insulation performance of buildings, and thereby enhances the comfort of the indoor environment, improves energy efficiency, and realizes sustainable architectural design by optimizing the arrangement of greenings such as green walls and indoor plantings based on scientific data. [Means for solving the problem]

[0005] To achieve the above objectives, the interior mapping system according to the present invention is 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, The aforementioned indoor environmental data is measured by environmental sensors (temperature sensor, humidity sensor, CO2 sensor, anemometer), A mobile device (self-propelled mobile body, portable device, etc.) 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, A greening arrangement determination unit determines the indoor greening arrangement based on the analysis results analyzed by the aforementioned data analysis unit, It is characterized by possessing the following features.

[0006] Here, the mobile device may be a self-propelled mobile device 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 greening arrangement. Therefore, according to the greening arrangement optimization system of the present invention, 1. By integrating and analyzing three-dimensional point cloud data, image data, positioning data, and environmental sensor data of indoor spaces, it becomes possible to determine the optimal greening layout based on scientific evidence. This allows for the provision of more effective layout proposals that do not rely on conventional, subjective design (optimization of greening layout based on scientific data). 2. It is possible to maximize the effects of the placed green walls and plants, such as CO2 absorption, air purification, and temperature and humidity regulation. This improves the comfort of the indoor environment and provides psychological and physiological benefits to users (maximizing the environmental improvement effect). 3. The placement of plants enhances the insulation effect within the building, reducing the consumption of heating and cooling energy and improving energy efficiency. This enables a reduction in environmental impact and the promotion of sustainable building design (improved energy efficiency of the building). 4. By using mobile devices, it is possible to collect data on dynamically changing indoor environments, enabling optimization of green space placement under diverse conditions. Furthermore, these devices can be flexibly adapted to specific uses and spaces (flexibility through the use of dynamic data). 5. Through scientific analysis of environmental data and optimization of green space placement, environmentally conscious and sustainable spatial design can be realized. This invention is applicable to a variety of indoor spaces, including commercial facilities, office buildings, and public facilities, and is expected to be used in a wide range of fields (realization of sustainable spatial design).

[0007] Here, the greening placement information may include placement information for ceiling gardens, waterfront gardens, container gardens, and floor gardens, but it is preferable to include placement information for at least one of the central elements of interior mapping, namely green walls and indoor plantings.

[0008] Furthermore, it is preferable to include a simulation unit that simulates the greening arrangement determined by the greening arrangement determination unit and predicts its effects. Such a system not only provides optimal greening placement information, but also allows for simulations based on that information to predict the effects after placement, thus facilitating consensus building and adjustments during the design phase (convenience provided by greening placement information).

[0009] Furthermore, it is preferable to further include a visualization means for visualizing the greening arrangement determined by the greening arrangement determination unit. Visualizing the green space layout makes it easier to understand the proposed layout and facilitates consensus building among designers and stakeholders. Furthermore, visualizing simulation results allows for a concrete understanding of the effects after placement (e.g., CO2 reduction, temperature and humidity improvement, improved insulation performance, etc.), enabling an objective evaluation of the effectiveness of the green space layout.

[0010] Furthermore, the system may also include an implementation information generation unit that automatically generates guidelines and procedures necessary for implementing the greening layout determined by the greening layout determination unit. This configuration provides specific guidelines and procedures for implementing green spaces, thereby streamlining the implementation process and ensuring a unified understanding among designers and contractors involved in the implementation of green spaces.

[0011] The greening arrangement determined by the greening arrangement determination unit is preferably determined by inputting input data, including the indoor three-dimensional point cloud data, the indoor image data, the indoor positioning data, and the environmental data, or input data, including indoor environmental conditions analyzed by the data analysis unit, into a learning model that has been pre-trained to correlate with the indoor greening arrangement. This configuration improves the accuracy of determining green space placement based on scientific evidence, enabling efficient and flexible optimization of placement plans. Furthermore, by utilizing learning models, it is expected that placement plans that accurately reflect real-world environmental conditions will be derived, maximizing improvements in indoor environments and energy efficiency. [Effects of the Invention]

[0012] As described above, the interior mapping system, interior mapping method, and interior mapping program according to the present invention make it possible to obtain the following effects. (Achieving optimal placement based on scientific evidence) By integrating and analyzing three-dimensional point cloud data, image data, positioning data, and environmental sensor data (CO2 concentration, temperature, humidity, wind speed, etc.) of indoor spaces, it becomes possible to accurately grasp the environmental conditions of indoor spaces and provide scientifically-based greening layouts. This enables highly accurate greening layouts that could not be achieved with conventional intuitive and empirical methods, resulting in reliable layout proposals. (Maximizing the environmental improvement effect) By appropriately proposing indoor greening arrangements, it becomes possible to maximize the effects of green walls and plants, such as CO2 absorption, air purification, and temperature and humidity regulation. This is expected to improve the comfort of the indoor environment and provide users with a healthy and comfortable space. Furthermore, by improving air circulation and humidity balance, a highly livable environment can be created. (Improving the energy efficiency of buildings) By utilizing the insulating effect of appropriately placed green spaces, indoor heating and cooling energy consumption can be reduced. This improves the overall energy efficiency of the building, leading to reduced operating costs and a lighter environmental impact. (Flexible adaptability and application to diverse spaces) By utilizing mobile devices, it becomes possible to collect dynamic data throughout the entire interior. Therefore, it is easy to adapt to various space conditions and environmental changes, and it can be applied in various spaces such as commercial facilities, office buildings, and public facilities. Also, in order to respond to layout changes based on dynamic data, it becomes possible to flexibly adapt to long-term operation. (Improving the Efficiency of the Design Process) By conducting a preliminary confirmation of the effects through simulation, the design and construction processes are made more efficient. Also, by visualizing the greening layout information, it becomes easier to materialize the design plan and achieve unity of understanding among the relevant parties. Additionally, by providing guidelines and procedures necessary for the implementation of the greening layout, the process from design to construction is made more efficient. As a result, it becomes possible to realize an effective greening layout while reducing the overall project cost and time. (Realizing Sustainable Space Design) Through greening layout based on scientific data, it is possible to improve the comfort of the indoor space while reducing the environmental load. Such a design supports long-term sustainability and is effective in various indoor environments including commercial facilities and public spaces. As a result, it becomes possible to achieve a space design that integrally realizes energy efficiency, environmental improvement, and aesthetic enhancement.

Brief Explanation of Drawings

[0013] [Figure 1] It is a schematic configuration diagram showing a configuration example of the interior mapping system according to the present invention. [Figure 2] [[ID=)19]]It is a flowchart explaining a series of processes for determining the optimal arrangement of green walls and plantings. [Figure 3] It is a diagram showing an indoor arrangement example of a green wall and plantings. [Figure 4] It is a diagram showing a configuration example of the machine learning device used in the present invention. [Figure 5] It is a schematic configuration diagram showing another configuration example of the interior mapping system according to the present invention.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, embodiments of the interior mapping system according to the present invention will be described with reference to the attached drawings. Figure 1 shows the overall configuration of the interior mapping system S. This interior mapping 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 inside a building (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, wind speed, etc.) and structural data (indoor three-dimensional point cloud data, indoor image data, indoor positioning data, etc.).

[0015] This interior mapping system S may use only sensors installed on a mobile device (a self-propelled mobile body or a portable device), but in this example, various data are collected by sensors installed on the mobile device and other fixed sensors fixed indoors, and the collected data is integrated and stored in a data management server 40 such as a cloud server or on-premise server for analysis.

[0016] [Regarding the sensors installed 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, anemometer, and thermographer (thermal imaging camera).

[0017] (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 mainly includes the following: (a) distance data (b) Shape of walls and objects (c) Indoor spatial structure

[0018] (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

[0019] (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

[0020] (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) Movement of the mobile device M (acceleration, rotation rate)

[0021] (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

[0022] (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 (%)

[0023] (7) CO2 sensor The CO2 sensor 7 measures indoor CO2 concentration in real time, which is useful for collecting data for plant placement and environmental improvement. The data that can be collected by the CO2 sensor 7 mainly consists of the following: (a) CO2 concentration (ppm)

[0024] (8) Anemometer Anemometer 8 is used to measure airflow and identify optimal circulation paths and evaluate air quality. The data that can be collected by the anemometer 8 mainly consists of the following: (a) Wind speed (m / s) (b) Wind direction (°)

[0025] (9) Thermography (thermal imaging camera) Thermography 9 is a camera that uses infrared light to visualize the heat distribution indoors, identifying areas of heat flow, insulation defects, and heat leaks. In other words, it is used to visualize the temperature of an indoor space in real time, understand temperature deviations in specific areas, analyze the direction and intensity of heat transfer indoors, and identify areas where insulation is insufficient. The data that can be collected with thermography 9 primarily includes the following: (a) Surface temperature (in degrees Celsius) at various locations indoors (b) Indoor thermal distribution image (c) Thermal flow pattern

[0026] [Regarding 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, and an anemometer 24.

[0027] (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

[0028] (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 (%)

[0029] (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)

[0030] (4) Anemometer The anemometer 24 is permanently installed and continuously measures airflow to support efficient ventilation design and maintenance of air quality. The data that can be collected by the anemometer 24 mainly consists of the following: (a) Wind speed (m / s) (b) Wind direction (°)

[0031] 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 separately from the mobile device is also sent to the data management server 40 via a transmission control unit (not shown) and the network 11.

[0032] 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, and based on the analysis results, the greening placement determination unit 44 determines the placement of green walls and plants to optimize environmental conditions (maximize the environmental improvement effect). Here, "optimization of environmental conditions" and "optimal placement of green walls and indoor plantings" are interrelated and complementary. "Optimization of environmental conditions" aims to collect and analyze environmental data such as indoor temperature, humidity, CO2 concentration, and airflow, and to design an optimal indoor environment (for example, an environment where indoor environmental data are within a predetermined range of standard values). This process collects scientific data that forms the basis for improving comfort and productivity.

[0033] On the other hand, "optimal placement of green walls and indoor plantings" aims to maximize environmental improvement effects by optimizing plant placement using this scientific data. Specifically, data obtained from the analysis of environmental conditions (for example, areas with high CO2 concentration, areas with good airflow, areas requiring insulation, etc.) is directly used in plant placement planning. For example, placing plants in areas with high CO2 concentration can maximize air purification effects and improve the quality of the living environment. Also, placing plants near windows with strong sunlight or on walls that require insulation can improve the energy efficiency of the building. Furthermore, improving airflow is important not only for increasing indoor comfort but also for optimizing plant growth conditions. Since plants themselves have an impact on the environment, appropriately placed plant communities balance temperature and humidity and contribute to improved air quality.

[0034] Therefore, "optimizing environmental conditions" provides a data base for plant placement, and "optimizing the placement of green walls and indoor plantings" 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.

[0035] 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 green walls and plantings to achieve the following objectives. (1) Maximizing CO2 absorption: Maximizing the amount of CO2 absorbed: Place plants in areas with high CO2 concentration in the air to improve air purification effect. (2) Improved insulation: Plants are placed in areas with high heat loss to improve energy efficiency. (3) Improving airflow: Plant plants in areas where air stagnates to promote air circulation. (4) Improved comfort: Create a comfortable indoor environment by adjusting temperature and humidity conditions.

[0036] 2. A series of processes for determining the optimal placement Next, we will explain the process of determining the optimal placement of green walls and plants (see Figure 2). 2.1 Data Collection (Step S11) Data collection involves gathering information necessary to understand the indoor environment and determine the optimal placement of green walls and plants. This stage utilizes sensors mounted on mobile devices and fixed sensors installed indoors. 2.1.1 Utilization of sensors mounted on mobile devices The mobile device M is equipped with a LiDAR (Light Detection and Ranging), camera, temperature sensor, humidity sensor, CO2 sensor, and anemometer. While the mobile device M is moving or stationary, it collects 3D data and environmental data of the indoor space (each room and areas outside the rooms). 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, anemometer 8, thermography 9): These sensors measure indoor temperature, humidity, and CO2 concentration in real time. They also measure airflow (velocity and direction) to understand the air circulation pattern within the building. Furthermore, they measure the indoor heat distribution. This data is used to analyze indoor environmental conditions in detail and determine the optimal placement of green walls and plants. 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, anemometer 24): Continuously monitor environmental data (temperature, humidity, CO2 concentration, airflow) in each area of ​​the indoors.

[0037] 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 builds a foundation for creating deployment plans. 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 is integrated based on their installation location to ensure consistency between spatial and environmental data. The integrated data is provided as a base dataset for use in the subsequent data analysis unit 43.

[0038] 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) Airflow analysis: Based on anemometer data, the airflow is visualized to identify efficient circulation paths and stagnant areas (areas where air circulation is insufficient). (2) CO2 concentration analysis: By analyzing the data from the CO2 sensor and identifying areas with high concentrations, the placement locations that have a high CO2 absorption effect will be evaluated. (3) Thermal fluid analysis: Using thermography 9 and temperature sensors 5, 21, the heat distribution is analyzed to identify areas where heat is concentrated and areas with insufficient insulation performance (places with low insulation effect). Particular attention is paid to analyzing areas near windows where sunlight is strong and walls with high heat loss. These analysis results are passed to the greening placement determination unit 44 as indicators for optimizing the indoor greening arrangement.

[0039] 3. Decision on the layout plan (Step S14) In the greening placement determination unit 44, the optimal placement of green walls and plantings is determined based on the analysis results. This process considers three criteria: CO2 absorption efficiency, thermal insulation effect, and airflow. Based on the data analysis results, a placement plan is created using a learning model. The placement plan may include not only the location of green walls and plantings, but also the type and density of the plants. 3.1 Prioritization of Placement Criteria The placement is determined based on the following criteria: CO2 absorption efficiency as the top priority, followed by thermal insulation, and finally, improving airflow. 3.2 Optimal placement of green walls Green walls are placed in areas with high CO2 concentrations, near windows that receive strong sunlight, and on walls where heat easily escapes. This maximizes CO2 absorption and insulation effects. Furthermore, placing them in locations with good airflow improves air purification. CO2 absorption: Green walls are installed in areas with high CO2 concentrations (such as conference rooms and crowded spaces) to maximize their CO2 absorption effect. Insulation effect: By placing it on walls where heat easily escapes or near windows where sunlight is strong, it improves the overall energy efficiency of the building. Airflow: By installing the system in areas where airflow is efficient, the air purification effect is enhanced. 3.3 Optimal planting arrangement Plants are placed in areas with high CO2 concentrations to enhance CO2 absorption efficiency. They are also installed in dry areas susceptible to the effects of air conditioning, and in relaxation spaces where visual appeal is important, to promote air circulation and humidity control. CO2 absorption: Plants should be placed in areas with insufficient ventilation and high CO2 concentrations. Humidity control: Place it in areas prone to dryness (such as near air conditioners) to balance humidity levels. Visual effect: Placed around desks, in break areas, etc., with the aim of promoting relaxation and stress reduction.

[0040] 3.4 Utilization of Learning Models In this system, a learning model may be used to optimize the indoor greening arrangement (optimal placement of green walls, optimal placement of plants). Specifically, a machine learning device 50 may be provided on the data management server 40 that estimates the greening arrangement (optimal placement of green walls, optimal placement of plants) based on the data integrated by the data integration unit 42 or based on the analysis results analyzed by the data analysis unit 43 (see Figure 1). As shown in Figure 4, such a machine learning device 50 includes an input data acquisition unit 51 that acquires data sets collected from various sensors as input data, or analysis results analyzed by a data analysis unit as input data, a label acquisition unit 52 that acquires indicators of greening arrangement (positions of green walls and plantings, and effect indicators obtained by the arrangement) 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 combinations as training data.

[0041] (1) Data collection Specifically, the following data will be used as input data. (When using the data from each sensor as is) When using the data from each sensor directly, the input data consists of environmental information and indoor space data collected from each sensor. Building structure data and characteristic data of plants used for green walls and landscaping may also be included. Specifically, the data includes the following: (a) LIDAR (Light Detection and Ranging) Indoor 3D structural data: Shape of the space where it can be placed Location of walls, ceiling, and floor Area and height of each area (b) Camera Visual data: texture of walls and floors Harmony with windows and existing interior design (c) Temperature sensor Temperature data: Ambient temperature (Celsius) in each area temperature distribution (d) Humidity sensor Humidity data: Relative humidity (%) in each area humidity distribution (e)CO2 sensor CO2 concentration data: CO2 concentration (ppm) in each area Fluctuation data by time period (f) Anemometer Airflow data: Air velocity (m / s) Direction of airflow (°) (g) Thermography (thermal imaging camera) Heat distribution data: Surface temperature distribution Identifying areas with high heat loss (h) Building structure data Placeable areas: Location of windows, walls, and ceilings Areas where sunlight enters (estimated light intensity conditions) Installation restrictions: Limitations on the weight and size that can be placed. (i) Plant characteristic data CO2 absorption capacity Humidity adjustment ability Resistance to light, temperature, and humidity Growth rate, physical size (When using analysis results) It is also possible to use the results of the analysis performed by the data analysis unit 43 as input data. Specifically, the following data can be used as input data. (a) Results of airflow analysis: circulation paths and stagnation areas, etc. (b) Results of CO2 concentration analysis: Locations with high CO2 absorption effect, etc. (c) Results of thermal fluid analysis: areas where heat is concentrated, areas with insufficient thermal insulation, etc.

[0042] (2) Data preprocessing The collected data cannot be directly applied to the learning model, so the following preprocessing is performed. (a) Noise reduction: Eliminate outliers and sensor errors to extract reliable data. (b) Imputation of missing values: Interpolates missing measurements and maintains the consistency of the dataset. (c) Normalization and standardization: Unify the scale of the data to improve the training efficiency of the model.

[0043] (3) Feature extraction From the preprocessed data, extract the features necessary for training the model. For each sensor, generate the following features: Temperature and humidity: average value, maximum value, range of variation CO2 concentration: Maximum value, percentage change in concentration Wind speed: average wind speed, retention rate Thermography: Heat distribution patterns and temperature differences These features serve as important indicators for quantitatively evaluating the impact of green space arrangements on the environment.

[0044] (4) Creating labels The following information will be set as the model's output data (labels). The output data (labels) are specific green wall and planting placements and evaluation indicators generated by the placement optimization algorithm. Labels are assigned based on past performance and simulation results. The following data are suitable for use as labels. (a) Location: Specific locations for green walls and plantings (location and area names) (b) Effectiveness indicators based on placement: (i) CO2 reduction effect: Change in CO2 concentration after deployment (reduction in ppm) (ii) Changes in temperature and humidity: Improvement values ​​of temperature (Celsius) and humidity (%) after placement. (iii) Improvement of airflow: Changes in airflow velocity and direction (m / s, °) (iv) Thermal insulation effect: Changes in heat distribution after placement (analysis using thermography) (v) Evaluation score of the layout plan: Overall score for environmental improvement (e.g., rated on a scale of 0 to 100) Energy efficiency improvement rate (%) (vi) Plant selection information: Which plants should be planted? (Names and characteristics of plant species) Using the measured data collected after the layout is implemented, labels are generated that reflect the actual improvement effects brought about by the layout plan.

[0045] (5) Model training The processed input data and labels are used to build a learning model. The algorithm is selected according to the purpose, for example, as follows: Regression models: Predicting CO2 reduction and temperature control effects. Time series models: Predicting environmental data while considering changes over time. Deep learning models: Image analysis using thermography and 3D spatial data The model is trained using the training data, then its parameters are tuned on the validation data, and its generalization performance is evaluated on the test data.

[0046] (6) Operation and updating of the model The trained model takes sensor data from the indoor environment as input and generates a greening layout plan in real time. This layout plan 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 for visualizing the greening layout. Furthermore, the model will be periodically updated using newly collected data during operation to adapt to changes in environmental conditions.

[0047] 4. Simulation (Step S15) The simulation execution unit 45 performs a simulation using the greening arrangement determined in the greening arrangement determination unit 44. This simulation predicts the environmental improvement effect after the arrangement is made. For example, it quantitatively evaluates CO2 reduction, temperature and humidity control effects, and changes in airflow. Furthermore, these results are visualized on the display unit 101 so that stakeholders can intuitively understand the effects of the arrangement plan. At this stage, the appropriateness of the proposed layout is confirmed, and modifications are made as necessary. 4.1 Verification of the Layout Plan We will model the proposed layout and evaluate the amount of CO2 absorbed, the degree of improvement in air circulation, and the thermal insulation effect. The validity of the proposed layout will be confirmed using virtual simulations. 4.2 Adjustment of the layout plan Based on the simulation results, the layout plan will be adjusted as needed.

[0048] 5. Creation of implementation guidelines for the deployment plan (Step S16) Based on the layout plan confirmed by simulation, the system automatically generates guidelines and procedures necessary for implementing the green space layout. These guidelines include the types of plants to be used, installation procedures, necessary materials and equipment, operation methods, and even a maintenance plan. This creates an environment where designers and contractors can accurately and efficiently realize the green space layout.

[0049] 6. Implementation, monitoring, and adjustment (Step S17) 6.1 Implementation Based on the guidelines, the green wall G and planting P will be installed as shown in the example in Figure 3. After installation, the monitoring system will be activated to monitor the environmental improvement effect in real time. 6.2 Monitoring After installing the green wall and plantings, the effects of the proposed layout will be evaluated using various sensors (temperature sensors 5, 21, humidity sensors 6, 22, CO2 sensors 7, 23, anemometer 8, 24, and thermography camera 9). In particular, the CO2 reduction and temperature / humidity control effects will be continuously measured to evaluate the effectiveness of the layout. 6.3 Adjustment Based on the monitoring results, the deployment and operating methods will be reviewed as needed. We will conduct regular reassessments to maintain a sustainable green environment in the long term. Through the above series of processes, the entire process from data collection to layout plan creation, simulation, implementation, and monitoring is carried out efficiently and scientifically, resulting in the optimal placement of green walls and plantings. This provides a comfortable and sustainable indoor environment that achieves environmental improvement and energy efficiency through CO2 absorption, improved air circulation, and enhanced insulation.

[0050] As described above, this system enables the integrated analysis of three-dimensional point cloud data, image data, positioning data, and environmental data of indoor spaces, allowing for a scientific evaluation of the indoor environment and the optimization of green space placement based on scientific evidence. This makes it possible to provide highly accurate placement proposals that could not be obtained with conventional intuitive design (optimization of green space placement based on scientific evidence).

[0051] By maximizing the CO2 absorption, air purification, and temperature / humidity regulation effects of appropriately placed green walls and vegetation, the comfort of the indoor environment is improved, providing a healthy space for residents and users. Furthermore, improved air circulation and humidity balance can lead to a more comfortable environment (maximizing environmental improvement effects).

[0052] By enhancing insulation through the appropriate placement of green walls and landscaping, 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).

[0053] The simulation function for layout plans allows for prior verification of the effects of green space placement. This streamlines the design and construction processes. Furthermore, visualizing green space placement 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).

[0054] Through greening arrangements 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).

[0055] 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 optimal greening arrangements under various conditions. As a result, it is possible to respond quickly to changes in environmental conditions (flexibility through the use of dynamic data).

[0056] Through airflow analysis, CO2 concentration analysis, and thermal fluid analysis, indoor environmental conditions can be analyzed in detail, providing indicators for optimizing green space placement and enabling the creation of scientifically-based placement plans (advanced environmental data analysis).

[0057] It features a function that automatically generates the guidelines and procedures necessary for implementing green space layouts, enabling designers and contractors to accurately and efficiently realize their layout plans. Furthermore, long-term effectiveness can be maintained through monitoring and adjustments (streamlining the implementation process).

[0058] The relaxation and stress-reducing effects of green spaces can promote the psychological and physiological health of users. Furthermore, the improved visual aesthetics brought about by green spaces can increase the satisfaction of residents and visitors (improving health and psychological effects).

[0059] In addition to green walls and plantings, this system supports a variety of greening forms, including ceiling greening, floor greening, and furniture incorporating plants. 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 uses (diverse application range).

[0060] While the above-mentioned interior mapping system primarily focused on green walls and indoor plantings, green placement that achieves environmental improvement (CO2 absorption, air purification, temperature and humidity control), aesthetic enhancement, psychological effects, and sustainable spatial design includes: Ceiling greenery (hanging plants), waterfront greenery, container gardens, automated greening systems, This can also include floor greening, furniture incorporating plants, art greenery, greening of the entire space (biophilic design), and greening in conjunction with lighting.

[0061] Furthermore, in the aforementioned interior mapping system S, data collected by sensors on a mobile device and fixed sensors installed in appropriate locations indoors are integrated and analyzed to determine the greening arrangement. However, as shown in Figure 5, the same processing may be performed using only sensors on a mobile device.

[0062] Furthermore, the aforementioned interior mapping system S can also be provided in the form of a program (interior mapping program) that causes a computer to execute each step of the interior mapping method described above. [Explanation of Symbols]

[0063] 1 LIDAR 2 cameras 3. Indoor positioning sensor 5.21 Temperature sensor 6.22 Humidity Sensor 7.23 CO2 sensor 8,24 Anemometer 9. Thermography 42 Data Integration Department 43 Data Analysis Department 44 Greening Placement Decision Section 45 Simulation Implementation Department 50 Machine Learning Devices 55 Learning Models S Interior Mapping 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, A greening arrangement determination unit determines the indoor greening arrangement based on the analysis results analyzed by the aforementioned data analysis unit, An interior mapping system characterized by being equipped with the following.

2. The interior mapping system according to claim 1, wherein the greening arrangement includes arrangement information for at least one of a green wall and indoor plantings.

3. The interior mapping system according to claim 1, further comprising a simulation implementation unit that simulates the greening arrangement determined by the greening arrangement determination unit and predicts its effects.

4. The interior mapping system according to claim 1, further comprising a visualization means for visualizing the greening arrangement determined by the greening arrangement determination unit.

5. The interior mapping system according to claim 1, further comprising: an implementation information generation unit that automatically generates guidelines and procedures necessary for implementing the greening arrangement determined by the greening arrangement determination unit.

6. The greening arrangement determined by the greening arrangement determination unit is The interior mapping system according to claim 1, which is determined by inputting input data including the indoor three-dimensional point cloud data, the indoor image data, the indoor positioning data and the environmental data, or input data including indoor environmental conditions analyzed by the data analysis unit, into a learning model that has been pre-trained to correlate with the indoor greening arrangement.

7. 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 method for optimizing green space arrangements comprising a mobile device equipped with the indoor 3D point cloud data acquisition unit, the indoor image acquisition unit, and the indoor positioning data acquisition unit, and also equipped with some or all of the environmental sensors, wherein the method optimizes green space arrangements, 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 is performed to analyze indoor environmental conditions based on the data integrated in the aforementioned data integration step, A greening arrangement determination step in which the indoor greening arrangement is determined based on the analysis results analyzed in the data analysis step, An interior mapping method characterized by comprising the following:

8. The interior mapping method according to claim 7, further comprising a simulation implementation step of simulating the greening arrangement determined in the greening arrangement determination step and predicting its effects.

9. The interior mapping method according to claim 7, further comprising a visualization step for visualizing the greening arrangement determined by the greening arrangement determination step.

10. The interior mapping method according to claim 7, further comprising an implementation information generation step for automatically generating guidelines and procedures necessary for implementing the greening arrangement determined by the greening arrangement determination step.

11. The greening arrangement determined in the greening arrangement determination step is The interior mapping method according to claim 7, which is determined by inputting input data including the indoor three-dimensional point cloud data, the indoor image data, the indoor positioning data and the environmental data, or input data including the indoor environmental conditions analyzed in the data analysis step, into a learning model that has been pre-trained to correlate with the indoor greening arrangement.

12. An interior mapping program for causing a computer to perform each step of the interior mapping method according to any one of claims 7 to 11.

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