Method and system for simulating creation of smart farm complex by using geographic information

Abstract

Provided is a method for simulating creation of a smart farm complex by using geographic information, the method comprising: a target area input step of inputting, from a terminal to a server, information for specifying a target area in which is to be created a smart farm complex comprising at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility together with a smart farm; a complex design step of selecting each facility constituting the smart farm complex to be created in the target area specified via geographic information and setting parameters of the area and image design of each zone of each facility from the terminal to the server; a smart farm design step of setting, from the terminal to the server, parameters of crops, smart farm-specific area, smart farm type, external facilities, internal facilities, and energy facilities of the smart farm; and an economic feasibility analysis step of performing, on the server, a simulation of a creation cost, an operation cost, and a sales amount of the smart farm, on the basis of parameters of the respective facilities of the smart farm complex and respective parameters of the smart farm.

Description

Simulation Method and System for Establishing a Smart Farm Complex Using Geographic Information

[0001] The present invention relates to a method and system for simulating using geographic information to know in advance the economic feasibility and visual results prior to the construction of a smart farm complex including a smart farm and related facilities.

[0002] A smart farm is a type of indoor facility farm (greenhouses, livestock barns) that refers to a science-based farming method utilizing information and communication technologies (ICT), such as the Internet of Things (IoT) and big data, to remotely and automatically monitor growing environments "without spatial or temporal constraints." This enables management and farming decision-making to ensure optimal conditions are maintained at the right time. In smart farms, the agricultural environment is improved through increased production yields and reduced labor hours. Furthermore, by combining with big data technology to provide an optimized growing environment, it is possible to make optimized production and management decisions, including predicting harvest times and yields, as well as increasing quality and production volume.

[0003] The establishment and operation of smart farms are not easy tasks; they are specialized fields requiring advanced knowledge across various disciplines, including land, buildings, crop cultivation, and distribution. Furthermore, predictions regarding establishment and operation have been reliant on experience and lacked accumulated data. Additionally, because initiatives were pursued in a fragmented manner by multiple experts and entities across different sectors, they suffered from a lack of consistency and were unable to escape a high-cost structure.

[0004] Smart farms and related sixth-industry facilities, such as processing and distribution facilities for their products, exhibition and learning facilities, and commercial facilities, which were previously perceived as separate entities, are expected to be standardized and integrated and operated in the near future, and the inventors refer to this as a "smart farm complex." Just like smart farms, the smart farm complex will inevitably be driven by various entities in a broader and unsystematized field regarding its construction and operation.

[0005] Although the following patent documents disclose simulation technology for educational experience in smart farm operation, this is not a simulation of the construction (construction and operational performance) of a smart farm like the present invention.

[0006] [Prior Art Literature]

[0007] [Patent Literature]

[0008] (Patent Document 1) Patent Registration No. 10-2269687 Publication

[0009] Considering government policies and market trends, smart farming is expected to become a key future industry and is growing at a very rapid pace; however, the reality is that actual implementation is rare due to limited information, a lack of knowledge, and a shortage of clear, standardized systems and case studies. To address this, separate consulting services are required, but since the costs of most consulting exceed hundreds of millions of won, access is limited for the majority of small-scale farmers and corporations. Conversely, large corporations and local governments willing to undertake such projects also require simulations for various sites within their regions, but most face difficulties in execution due to constraints on cost and knowledge.

[0010] Despite the government and the market actively promoting the introduction of smart farms under these circumstances, smart farms account for less than 6,000 hectares (0.1%) of the approximately 4.92 million hectares of agricultural and forestry land nationwide (approximately 11% based on facility horticulture standards). In particular, private farmland, which accounts for about 2.52 million hectares (46%) of the total farmland, appears to face a more difficult situation regarding access to smart farms, suggesting that there is a market need and a potential demand base.

[0011] Establishing a smart farm requires a massive investment, and such an investment must guarantee a return on investment above a certain level. Therefore, anyone intending to establish a smart farm needs to determine in advance whether investing and operating within a budget can generate profits exceeding the return on investment.

[0012] Specifically, as mentioned above, land is required to establish a smart farm. If site analysis confirms that the smart farm can be built, the facilities of the complex must be arranged on a designated area of ​​the land, crops must be selected, external and internal facilities installed, and energy facilities installed. Subsequently, the costs required for the construction of the smart farm must be calculated and ensured to fall within the budget. Furthermore, an economic feasibility analysis must be conducted by forecasting crop yields and revenue resulting from the operation of the smart farm. If necessary, similar construction examples should be referenced and utilized as comparative data. Additionally, if visual rendering is available to allow for a visual prediction of the smart farm's results, it would aid in decision-making.

[0013] Traditionally, the process for establishing smart farm complexes and conducting feasibility analyses relied on experience and was carried out in a fragmented manner, often lacking data-driven analysis. In other words, the only option was partial consulting by experts in specific fields. For instance, regarding land, since its legal and administrative use is predetermined, it is necessary to verify whether the land is suitable for smart farm development through experts such as local government officials or legal professionals. Furthermore, when arranging facilities on the land, the size, orientation, and form of the buildings must be determined based on factors such as water flow within and outside the site, the slope and direction of the inclination, the orientation of the road frontage, and the solidity of the soil; this requires verification by experts such as architects. Additionally, regarding crop selection, the choice must be based on the local climate and soil quality, and factors such as projected demand and sales channels must be considered, requiring verification by experts from agencies like the Rural Development Administration. Furthermore, crop growth potential and efficiency vary depending on whether the smart farm is a greenhouse or a building-type facility, and expert advice is required regarding external, internal, and energy facilities. Moreover, cost estimates for the development of a smart farm complex must be verified by the smart farm development company or the construction firm, and estimates regarding operational yields and sales revenue also require expert confirmation.

[0014] Therefore, in the past, it was necessary to visit each of these experts individually to request forecasts and pay the fees; in reality, this was a very burdensome amount for a business operator simply exploring the feasibility of establishing a smart farm. In the worst-case scenario, if the development of the smart farm complex is abandoned, these costs become unrecoverable expenses.

[0015] The present invention aims to resolve these problems by providing a technology for forming a pre-decision-making platform that enables land analysis and economic feasibility analysis using geographic information through preliminary simulations prior to the establishment of a smart farm or smart farm complex.

[0016] Furthermore, the aim is to provide simulation technology capable of specifically and sequentially setting the layout of each facility in the smart farm complex required for simulation, smart farm crops, area per farm, farm type, external facilities, internal facilities, and energy facilities, while also providing automatic layout functions or selection list presentation functions when necessary.

[0017] Furthermore, the aim is to provide simulation technology that utilizes geographic information and external databases to produce results suitable for the target site in the estimation of construction costs or sales revenue during economic feasibility analysis.

[0018] Furthermore, the aim is to provide simulation technology that enables land and economic analysis based on realistic data by utilizing the results of economic analysis to modify standard data in an external database to construct an internal database, and by utilizing this internal database during subsequent simulations.

[0019] Furthermore, the aim is to provide simulation technology that accumulates the results of land analysis and economic feasibility analysis, calculates statistical indicators from accumulated prior cases, and presents them as reference data for future simulations.

[0020] In addition, the aim is to provide simulation technology that has 3D modeling capabilities based on BIM, which can visually represent the appearance of the smart farm after construction.

[0021] Furthermore, this aims to provide technology that eliminates business risks and enhances the effectiveness of business implementation through future predictions to individuals or corporations who wish to pursue smart farm projects—the current direction of agriculture—but have faced difficulties due to the lack of development plans and corresponding simulation results. By offering effective complex development plans and preliminary integrated simulation results, this technology enables the business to succeed.

[0022] In addition, by subdividing items and automatically performing design based on the user's level of knowledge about smart farm complexes, a development plan reflecting the user's intentions is derived, and analysis simulation results and a smart farm 3D model are provided, thereby providing a technology that enables the user to obtain a practical approach to smart farm development easily and affordably and serves as a practical decision-making tool for the business.

[0023] The method of the present invention for achieving the above objective comprises: a target site input step in which information for specifying a target site to be developed for a smart farm complex including at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility along with a smart farm is input from the terminal to the server; a complex design step in which each facility constituting the smart farm complex to be developed for the target site specified through geographic information is selected, and parameters for the area of ​​each facility and image design by zone are set from the terminal to the server; a smart farm design step in which parameters for the crop of the smart farm, the area of ​​each smart farm, the type of smart farm, external facilities, internal facilities, and energy facilities are set from the terminal to the server; and an economic analysis step in which a simulation of the development cost, operating cost, and sales revenue of the smart farm is performed on the server based on the parameters of each facility of the smart farm complex and each parameter of the smart farm.

[0024] Here, it is preferable that information for specifying the above-mentioned target area be entered by inputting a lot number or address, by specifying it via a graphic interface on a map image displayed on a terminal, or by uploading a file for a geographic information system.

[0025] In addition, it is desirable that land analysis for the above-mentioned target site be performed on the above-mentioned server.

[0026] In addition, it is desirable that the area and image design parameters of each of the above facilities be automatically set.

[0027] In addition, for the simulation of the above-mentioned construction costs, operating costs, and sales revenue, it is desirable to use the respective standard data for the construction costs, operating costs, and sales revenue along with the geographical information of the above-mentioned target site.

[0028] In addition, it is desirable that the standard data for each of the above composition costs, operating costs, and sales be modified after the simulation of the above composition costs, operating costs, and sales.

[0029] In addition, it is desirable to further include a statistical step in which at least one of the self-indicators and overall indicators is statistically processed from the accumulated results of the above economic analysis.

[0030] In addition, among the accumulated economic analysis results, cases where the parameters of each facility of the smart farm complex and each parameter of the smart farm are similar are selected, and it is desirable that at least one of the parameters and the economic analysis results be presented as comparative data.

[0031] In addition, it is desirable to further include a 3D modeling step in which 3D modeling is performed using BIM based on the parameters of each facility of the smart farm complex and each parameter of the smart farm.

[0032] Meanwhile, the system of the present invention for achieving the above objective is a smart farm complex construction simulation system using geographic information, comprising: a target site input module in which the server receives information from the terminal for specifying a target site for which a smart farm complex is to be constructed, including at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility along with a smart farm; a complex design module in which the server receives from the terminal parameters for selecting each facility constituting the smart farm complex to be constructed at the target site specified through geographic information, and parameters for the area of ​​each facility and image design by zone of each facility; a smart farm design module in which the server receives from the terminal parameters for the crop of the smart farm, area of ​​each smart farm, type of smart farm, external facilities, internal facilities, and energy facilities; and an economic analysis module in which the server performs a simulation of the construction cost, operating cost, and sales revenue of the smart farm based on the parameters of each facility of the smart farm complex and each parameter of the smart farm.

[0033] According to the present invention, a technology is provided for forming a platform that enables land analysis and economic feasibility analysis using geographic information through a preliminary simulation prior to the establishment of a smart farm or a smart farm complex.

[0034] In addition, simulation technology is provided that can perform the specific sequential configuration of the layout of each facility of the smart farm complex required for simulation, smart farm crops, area per farm, farm type, external facilities, internal facilities, and energy facilities, while also providing automatic layout functions or selection list presentation functions when necessary.

[0035] In addition, simulation technology is provided that utilizes geographic information and external databases to generate results suitable for the target site in the estimation of construction costs or sales revenue during economic analysis.

[0036] In addition, a simulation technology is provided that enables land and economic analysis based on realistic data by utilizing the results of economic analysis to modify standard data in an external database to construct an internal database, and by utilizing this internal database during subsequent simulations.

[0037] In addition, a simulation technology is provided that accumulates the results of land analysis and economic feasibility analysis, calculates statistical indicators from accumulated prior cases, and presents them as reference data for future simulations.

[0038] In addition, simulation technology is provided that has a 3D modeling function based on BIM, which can visually represent the appearance of the smart farm after construction.

[0039] Furthermore, for individuals or corporations who wish to pursue smart farm projects—the current direction of agriculture—but have faced difficulties due to the lack of development plans and corresponding simulation results, this technology provides effective complex development plans and preliminary integrated simulation results. By doing so, it eliminates business risks and enhances the effectiveness of business implementation through future predictions.

[0040] In addition, by subdividing items and automatically performing design based on the user's level of knowledge regarding smart farm complexes, a development plan reflecting the user's intentions is derived, and analysis simulation results and a smart farm 3D model are provided, thereby enabling the user to obtain a practical approach to smart farm development easily and affordably, and serving as a practical decision-making tool for the business.

[0041] FIG. 1 is a block diagram of a system including a server in which the method of the present invention is implemented.

[0042] Figure 2 is a flowchart of the same method.

[0043] Figure 3 is the overall time flow processing diagram of the same method.

[0044] Figure 4 is a block diagram of an input / output UI.

[0045] Figure 5 is a block diagram for inputting a target area.

[0046] Figure 6 is a block diagram for GIS analysis of the target site.

[0047] Figure 7 is a block diagram for selecting facilities of a smart farm complex.

[0048] Figure 8 is a block diagram for the facility layout of the complex.

[0049] Figure 9 is a block diagram for crop selection in a smart farm.

[0050] FIG. 10 is a block diagram for designing external facilities of a smart farm.

[0051] FIG. 11 is a block diagram for designing the internal facilities of a smart farm.

[0052] FIG. 12 is a block diagram for calculating the cost of establishing a smart farm.

[0053] Figure 13 is a block diagram for economic analysis.

[0054] Figure 14 is a block diagram for an economic analysis formula.

[0055] Figure 15 is an example of a login screen.

[0056] Figure 16 is an example of a target site input screen.

[0057] Figure 17 is an example of a GIS land analysis screen for a target area.

[0058] Figure 18 is an example of a facility layout screen for the complex.

[0059] Figure 19 is an example of a facility layout screen for the complex.

[0060] Figure 20 is an example of a screen for inputting the area of ​​each zone of the complex.

[0061] Figure 21 is an example of a screen showing the area of ​​each smart farm crop.

[0062] Figure 22 is an example of a smart farm crop production and sales screen.

[0063] Figure 23 is an example of a screen showing the types of farms in the external facilities of a smart farm.

[0064] Fig. 24 is an example of a smart farm internal facility screen.

[0065] Fig. 25 is an example of a smart farm internal energy facility screen.

[0066] Figure 26 is an example of a screen for calculating land analysis development costs.

[0067] Figure 27 is an example of a screen showing the results of an economic analysis.

[0068] Figure 28 is an example of a statistical screen.

[0069] Figure 29 is an example of a 3D modeling screen.

[0070] Figure 30 is an example of a rendering screen of a 3D model.

[0071] The present invention will be described in detail below with reference to the attached drawings. However, for components having the same function due to the same configuration, detailed descriptions may be omitted by maintaining the same reference numerals even if the drawings differ.

[0072] Furthermore, a relationship in which another member is positioned or connected to the front, back, left, right, top, or bottom of a certain member includes cases where a separate member is inserted in between. Conversely, when it is stated that a certain member is 'immediately' front, back, left, right, top, or bottom of another member, it means that there is no separate member in between. And when it is stated that a certain part 'includes' another component, unless specifically stated otherwise, this means that it may include additional components rather than excluding them.

[0073] Furthermore, the classification of configurations into names such as "first," "second," etc., is intended to distinguish them based on their identical nature and is not strictly limited to that order. Additionally, terms such as "unit," "means," "part," "component," and "module" described in the specification refer to a comprehensive unit of configuration that performs at least one function or operation. Moreover, information processing devices such as terminals and servers described in the specification fundamentally refer to hard wiring, which means hardware on which a specific function or operation is implemented; however, they should not be interpreted as being limited to specific hardware, nor do they exclude soft wiring, which consists of software running to enable that specific function or operation to be implemented on general-purpose hardware. In other words, a terminal or server may be a device, or it may be software installed on a device, such as an app.

[0074] Furthermore, the size and thickness of each component shown in the drawings are depicted arbitrarily for the convenience of explanation, and thus the present invention is not necessarily limited to what is shown in the drawings; in some cases, thicknesses, etc., may be exaggeratedly enlarged or reduced to clearly represent various parts and regions, such as layers and areas.

[0075] Basic System

[0076] The present invention provides a layout model suitable for smart farm operation and determines the feasibility of introducing a complex when constructing a smart farm complex through land analysis and multidimensional analysis technology. It is a program capable of analyzing the feasibility of a project prior to the design and business stages of a smart farm complex by analyzing the construction costs and profit structure. It is also a method or system that includes a process capable of visualizing the design details of a smart farm complex as a 3D model.

[0077] The present invention can be implemented through a platform system such as FIG. 1. A server (100) constituting the present invention may be connected to a user terminal (10) that uses the service via a communication network, such as the Internet. A user refers to an actor who intends to build a smart farm or obtain information about it (purchase it). The Internet may be connected, for example, to a cloud system, and the server (100) may be implemented distributedly within the cloud system.

[0078] The server (100) of the above system is configured as a simulation system for creating a smart farm complex using geographic information, and comprises a target site input module (110), a complex design module (120), a smart farm design module (130), and an economic analysis module (150).

[0079] The above target site input module (110) is a module in which the server receives information from the terminal to specify a target site for which a smart farm complex is to be established, including at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility along with a smart farm.

[0080] The above complex design module (120) is a module in which the server receives parameters for the area and image design of each facility constituting the smart farm complex to be constructed in the above target site specified through geographic information from the terminal.

[0081] The above complex design module (120) can be connected to an automatic placement module (121) that automatically arranges each facility constituting the smart farm complex, such as a smart farm, processing and distribution facility, exhibition and learning facility, commercial facility, etc., according to a predetermined logic.

[0082] The smart farm design module (130) is a module in which the server receives parameters of the crops of the smart farm, the area of ​​each smart farm, the type of smart farm, external facilities, internal facilities, and energy facilities from the terminal.

[0083] And the smart farm design module (130) can be connected to a selection list database (131) that stores a selection list for the convenience of design and for the design without omission, particularly regarding various crops of the smart farm, area per farm of the smart farm, type of smart farm, external facilities, internal facilities, energy facilities, etc.

[0084] The economic analysis module (150) is a module in which the server performs a simulation of the construction cost, operating cost, and sales revenue of the smart farm based on the parameters of each facility of the smart farm complex and each parameter of the smart farm.

[0085] The above economic analysis module (150) is essentially included in the simulation module (140). The simulation module (140) may be composed of at least one of a statistics module (160) and a 3D modeling module (170) together with the economic analysis module (150).

[0086] And the above statistical module (160) can accumulate and store the economic analysis results of the preceding cases by the above economic analysis module (150), statistically process the overall indicators for all of these results, and also statistically process the self-indicators for the results of the current economic analysis, and have the function of presenting a comparison thereof.

[0087] And the above 3D modeling module (170) can show what the smart farm complex will look like visually based on the input and output data of each module performed previously, and can be connected to an external BIM system (230) for this purpose.

[0088] BIM (Building Information Modeling) is a digital model that enables the combination and utilization of all information appearing in a building based on a three-dimensional information model.

[0089] At least one of an input / output means (111), a graphic I / F (interface) means (112), and a file input / output means (113) may be connected to each of the above modules. And all modules of the server are configured to be connected to an external GIS (Geographic Information System) to receive geographic information.

[0090] GIS is an information system that digitizes geographic information necessary for human life into computer data for utilization. Geographic information regarding objects with geographical locations consists of spatial data and attribute data; by integrating and managing these, it provides information in various forms such as maps, charts, figures, and icons.

[0091] The above geographic information is primarily used for specifying the target area in the target area input module (110), but it can also be used for facility layout in the complex design module (120), or for limiting the data area of ​​the cost / sales database (151) or the external database (220) connected thereto, which is used for estimating construction costs or sales revenue in the economic analysis module (150). As the above external database (220), a previously developed reference point (mechanism), such as data on construction costs for LH Corporation complex development projects or data on estimated infrastructure installation costs, may be utilized.

[0092] Basic Method

[0093] The method of the present invention, implemented by a series of programs executed on the server (100) of the above system, is a method for simulating the creation of a smart farm complex using geographic information, and comprises, as shown in the flowchart of FIGS. 2 and 3, a login and target site input step (S10), a complex design step (20), a smart farm design step (S30), and an economic analysis step (S50). Additionally, steps of statistics and 3D modeling may be performed.

[0094] As shown in Fig. 15, login is permitted when user information is registered and entered as an ID / PW upon login and verified. When the user registers their information in the form of an ID and PW and enters the relevant information, the results analyzed by internal and external integration algorithms are presented to the user in the form of drawings and charts. The information purchased by the user can be stored through a database process and utilized as statistical analysis data. User registration information can be linked with land analysis and economic feasibility analysis results, as well as self-indicator statistical data.

[0095] Depending on the login, a program can be developed to recognize and manage the user's characteristics entered by the user and the data input and output during the analysis process on a per-user basis, or an existing program related thereto can be used as a basis. To this end, as shown in Fig. 4, an input / output and call structure connecting the user, UI / UX, database, and analysis process can be constructed.

[0096] The above target site input step (S10) is a step in which information for specifying the target site where the smart farm complex is to be established is input from the terminal to the server.

[0097] The above-mentioned smart farm complex is a complex that includes at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility along with a smart farm. Although smart farms are often operated independently, given the characteristics of smart farms that allow for production volume prediction and combine advanced technology, it appears that establishing them in the form of a sixth industry is an effective business model, especially on a large scale (e.g., Smart Farm Innovation Valley, etc.). Therefore, the present invention presents four facilities as basic concepts: a smart farm (glass greenhouse and plant factory), a processing and distribution facility, an exhibition and learning center (sixth industry), and a commercial facility. Subsequently, the addition of an agricultural industrial complex, housing, etc., is possible. The above-mentioned target site input step (S10) is performed by the above-mentioned target site input module (110).

[0098] To input a target area, a structure connecting a user, a specific method, a GIS system, an analysis process, and a database can be established as shown in Fig. 5. When a user inputs a target area, a process is carried out to store necessary information through external integration and internal analysis. The data stored in the database is used as the result value of the corresponding stage in subsequent processes.

[0099] When inputting a target site for establishing a smart farm (complex), three input methods can be established to consider user convenience, as shown in Fig. 16. That is, when selecting a target site, information to specify the target site can be input through the input of a lot number or address (a method in which the information is registered by selecting the corresponding lot number after searching for the address of the target site), by specifying it via a graphic interface on a map image displayed on a terminal (a method in which the target site is selected or drawn on an output GIS map), or by uploading a file for a geographic information system (a method in which the format is recognized and registered when data in existing DXF / DWG / SHP formats is uploaded (this method is currently used in GIS systems)). The first method has the advantage of being easy to register if the address is known, the second method has the advantage of allowing the user to establish the target site in the desired way regardless of the lot number, and the last method has the advantage of being the simplest to register and having high system accessibility.

[0100] This enables a target site selection method that considers various user characteristics and integration with a GIS (Geographic Information System). Data related to the target site parcel numbers is retrieved, and the areas are aggregated. The registered information is saved and applied to subsequent processes. The input information is linked to a GIS program, and related data is derived.

[0101] When a target site is identified, land analysis, such as analysis of relevant laws, development feasibility, and development-related conditions regarding the target site, can be performed on the server (100). To this end, a structure connecting target site selection data, data selection, a database, and a GIS analysis tool can be established as shown in FIG. 6. Here, information stored in the target site identification step is transmitted to a GIS program, and a sequence (data selection) is performed to select and retrieve related elements from the content analyzed in the program. This enables the linkage between the analysis program and the GIS program and the system for selecting and applying analysis data. As shown in FIG. 17, the derived data and land analysis results are expressed in charts and drawings and provided to the user, and the main contents are linked to facility layout logic and can be used in subsequent processes (facility layout and economic feasibility analysis, etc.).

[0102] The list of selected data for land analysis results includes: land category status, ownership status, area status, officially assessed land price, land shape in Basic Information - Land Information; use status, structure status, number of floors status, elapsed years, total floor area status, building coverage ratio status, floor area ratio status in Basic Information - Building Information; elevation analysis, slope analysis, aspect analysis, cross-sectional analysis, ecological natural map, vegetation (by forest type), vegetation (by age class), national land environment, hydraulics / hydrology, weather and climate in Location Analysis - Land and Building Analysis; land use, land cover, land price change / standard land, land shape, deterioration analysis, housing density analysis, road access ratio, surrounding building status in Location Analysis - Urban Planning Analysis; basic plan (living area, urban indicators, analysis of developable land), management plan (land use zone, district, area, facility analysis), development zone analysis, and public regulation analysis.

[0103] The above complex design step (20) is a step in which each facility constituting the smart farm complex to be constructed at the above target site specified through geographic information is selected, and parameters for the area of ​​each facility and image design are set from the terminal to the server. The above complex design step (20) is performed by the above complex design module (120).

[0104] Among the complex facilities presented in the selection list (131), a user selects an appropriate facility according to the situation, and a program is constructed that links to a subsequent process according to the selected facility. To this end, a structure can be constructed that connects a database, a facility selection list (selection list), a database, and a user selection tool as shown in FIG. 7. Here, smart farm complex facilities capable of securing synergy are presented (facility selection list), and the user can be allowed to select the complex they want.

[0105] In the design of a smart farm complex, the area of ​​each facility zone within the complex can be entered to perform image design. This process can be referred to as facility layout. This is the stage of placing facilities on the site.

[0106] At this time, the placement of facilities may be manually entered (set) by the terminal (10) user, but may also be automatically set according to a predetermined logic on the server (100). To this end, as shown in FIG. 8, a structure connecting the user, the business site call, the area placement input, the area input, the automatic placement, the placement process, the database, the location analysis logic, and the facility placement logic can be constructed.

[0107] Manual input of facility placement is a method of calling up the project site and placing it according to the area and placement plan defined by the user. It is desirable to perform this when the user has clear judgment criteria regarding the site and the project, and can be implemented, for example, by manually inputting an appropriate area for a facility selected in a preceding step. At this time, inputting as a table value as shown in FIG. 20 or selecting one of the drawing methods such as facility icons, placement, or dragging on a map as shown in FIG. 19 may be done.

[0108] In the case of automatic execution (automatic distribution) of facility placement, as a step for automatic calculation of area and placement, geographic information, user information, selected site, selected facility, and land analysis results are retrieved, and the automatic placement module (121) performs facility placement according to a predetermined logic (existing standards and self-development, location analysis logic and facility placement logic). That is, the area available for placement is calculated first according to the facility site area calculation logic (location analysis logic), and based on this, the area and placement plan for each facility can be automatically derived according to the facility placement logic. Even if the user only inputs the area, a placement plan can be presented at a predetermined ratio based on the input area, for example, at a level of -30 to 100%, and if placement is impossible, a warning can be displayed, for example.

[0109] For example, the facility layout process can achieve the two goals of maximizing economic effectiveness and eliminating risk factors by deriving the maximum area while adhering to constraints. Based on location analysis logic, areas that are unfavorable or impossible to locate are excluded from the target site to identify feasible and advantageous areas, and a facility layout logic is executed for efficient operation and organic linkage through operational analysis.

[0110] An automated system for optimized location selection can be provided for the input land by utilizing a pre-programmed algorithm system. Upon automatic generation, the user can readjust the results (e.g., facility icons, drag placement).

[0111] After saving the results and calculating the area for each facility, the data is saved. It is linked to the next step of the layout data and, as shown in Fig. 18, is tabulated and drawn in the subsequent step and output.

[0112] The smart farm design step (S30) is a step in which parameters of the crops of the smart farm, the area of ​​each smart farm, the type of smart farm, external facilities, internal facilities, and energy facilities are set from the terminal to the server. The smart farm design step (S30) is performed by the smart farm design module (130).

[0113] In the detailed design of the smart farm, various data corresponding to the construction of the smart farm are designed to calculate the construction cost and economic analysis results. Most of the data consists of values ​​determined according to the user's intention, but minimal guidelines may be provided through facility tables or option selections based on the selection list (131).

[0114] Crop parameter setting is a process of selecting a single or multiple crops from the entire smart farm data defined in the preceding step, as shown in Fig. 21, and distributing the area for each crop within the maximum smart farm area. The types of smart farms are broadly categorized into glass greenhouses and plant factories (selected), and vinyl greenhouses and plastic greenhouses can be added to the list of selectable options. First, after selecting the type of smart farm, the variety (selected) and the corresponding area are entered. Regardless of the type of smart farm, varieties such as (cherry) tomatoes, strawberries, bell peppers, and leafy vegetables can be presented.

[0115] To input the area for each crop (facility), a structure can be established as shown in Fig. 9 that links the database with price information (economic data) from an external database, such as KAMIS (Agricultural and Fisheries Distribution Information System). The selected varieties are automatically linked with KAMIS, and average price data based on grade is reflected. (Connection to subsequent processes) Linked data, such as crop yield, can also be retrieved from internal data and linked to subsequent stages. This allows for the structuring of the smart farm facility and crop selection system, enables the division of zones to allow for multiple selections, and facilitates the automatic linking of market data for the selected crops. The crops and areas are stored.

[0116] The production volume and sales revenue data of the cultivated crops can be estimated as shown in Fig. 22.

[0117] For external design (selection), a structure can be established that links the area per smart farm, the database, and the construction cost calculation process, as shown in Fig. 10. This is a selection of smart farm external designs for economic analysis; as shown in Fig. 23, a list of existing external designs can be presented for the user to select. For greenhouses, vinyl greenhouses, light steel frame greenhouses, and glass greenhouses can be provided as priority options, while plant factories can be calculated separately by adding building elements. Depending on the selected design, the construction cost (construction cost) data per unit area of ​​the corresponding smart farm, which is already entered into the data, can be retrieved. This data can be retrieved from values ​​stored in the database or entered separately considering the user's situation. The types and areas of the smart farm external facilities are stored.

[0118] Based on the selected data, the construction cost (partial) is calculated. Basic data is established to quantify management factors according to the external form of the smart farm. Standard data for calculating smart farm construction costs is established, and based on this, it becomes possible to develop a rational method for estimating management costs.

[0119] For internal design (selection), a structure can be constructed that links the area, database, and energy facility selection for each smart farm as shown in Fig. 11. As shown in Fig. 24, the internal facilities of the smart farm and the associated data can be cataloged and standardized to enable the user to acquire knowledge about smart farm construction and build a more complete facility. The facility design can be performed by presenting the internal facilities of the smart farm and allowing the user to select them. This can be implemented by designing facilities for each smart farm classified in the preceding step and summing them up.

[0120] The list of internal facilities should refer to national standards but prioritize facilities currently in actual use; environmental measurement sensors, control systems, and big data systems may be presented. A UI can be displayed allowing the user to check and select the presented facilities. Upon selection, existing basic data, such as the construction cost per unit area, can be retrieved and linked to the next step. The quantity per unit area for each facility can be entered (a standard is provided if not entered), and the required quantity and cost can be calculated by multiplying this by the area and cost (a standard is provided if not entered).

[0121] Among the selection list of internal facilities based on the national standard smart farm facility list, the facility horticulture ICT convergence equipment (Korea Agricultural, Forestry and Fisheries Food Education and Culture Information Service) lists temperature sensors, humidity sensors, CO2 sensors, and light intensity sensors in the environmental management device-sensors-internal-microweather sensors; soil moisture, EC, soil temperature, and pH in the environmental management device-sensors-internal-nutrient solution / soil sensors; temperature sensors, humidity sensors, wind direction sensors, wind speed sensors, rainfall sensors, and rain sensors in the environmental management device-sensors-external-sensors; circulation fans, side window openers, thermal curtain openers, shading curtain openers, CO2 suppliers, nutrient solution dispensers (in the case of nutrient solution cultivation), watering / fertilization dispensers, and cooling / heating units in the environmental management device-controllers; nutrient solution dispensers in the nutrient solution dispensers; fixed cameras and rotating / zoom cameras in the image management device-CCTV; PCs in the image management device; and greenhouse safety management devices (safety sensors).

[0122] By receiving equipment cost data from internal and external facility equipment suppliers through prior consultation, users can select a supplier before the analysis stage, or if they do not, the average value of existing data can be presented.

[0123] For detailed design of energy facilities, a separate page is allocated as shown in Fig. 25, and reference values ​​such as oil boilers (tax-exempt oil), air source, geothermal, water source, and waste heat pumps can be presented. The system can be configured to retrieve internal energy data while also allowing user data to be entered. Energy data can be linked to facility costs and operating costs. This can be adjusted by providing and reflecting user input fields.

[0124] This enables the establishment of foundational data for quantifying management factors based on the internal structure of smart farms. It can provide a standard for the appropriate scale of internal facilities. The quantity and costs of internal facilities can be aggregated and stored, and linked to construction and operating costs.

[0125] The above economic analysis step (S50) is a step in which a simulation of the construction cost, operating cost, and sales revenue of the smart farm is performed on the server based on the parameters of each facility of the smart farm complex and each parameter of the smart farm. The above economic analysis step (S50) is performed by the economic analysis module (150).

[0126] In the economic analysis, the analysis is performed based on the data set input or derived from the preceding process. The economic analysis requires the estimation of construction costs (initial investment costs), revenue, and operating costs. After executing the respective predetermined result derivation logics, the final economic analysis indicators are calculated.

[0127] In the analysis of construction costs, the data established through the procedures described above is synthesized to calculate the construction costs based on internal data, external standard data, or user data. Smart farm facility construction costs (establishment costs) and other building costs are reflected based on the data derived in the preceding stage.

[0128] When calculating the smart farm construction cost, the following formula is followed.

[0129] Development Cost = Land Cost + Infrastructure Cost + Construction Cost

[0130] = External Design + Internal Design

[0131] = Detailed Design (Exterior) Data * Area + Interior Facilities * Area

[0132] Land costs are calculated by retrieving the officially assessed land prices by parcel number from the GIS system, summing them for the specified area, and multiplying them by a parameter set according to the target site. (Default 2, user configurable)

[0133] Land Cost = GIS Officially Assessed Land Price * Factor (Factors default to 2, changeable)

[0134] The total sum of all selected target areas in the target area input step (S10) can be calculated, and in the case of division, it can be calculated as a ratio.

[0135] Standard data, such as foundation construction costs and supervision fees, is calculated by selecting based on the 'Development Costs and Infrastructure Installation Costs for Complex Development Projects' (hereinafter referred to as 'LH Standards') published by the Complex Technology Department of the Korea Land and Housing Corporation. This is achieved through a proprietary rational logic (development cost estimation method) that applies empirical figures. It is possible to establish a system that automatically calculates the reference points of existing specialized institutions using proprietary logic.

[0136] Infrastructure Construction Cost = Basic Facility Cost + Survey and Design Cost + Incidental Expenses

[0137] Construction Cost = Facility Construction Cost + Design & Supervision Fee

[0138] By configuring composition ratio elements into standardized modules and summing the elements affecting the previous stage through logic, it is possible to build a system for deriving pre-objectified composition ratio data.

[0139] In the economic analysis (for the entire complex), as shown in Fig. 27, the results of estimating the construction costs and the revenue and operating costs for each individual business (smart farm, processing and distribution, 6th industry, etc.) for N years (basic 20 years) can be estimated, and then an overall economic analysis can be performed. Based on the detailed data of the first year, the annual inflow-outflow over 20 years (basic 3%) can be estimated. The economic analysis can derive indicators such as NPV, IRR, B / C, PI, and break-even point using the Net Present Value method. Each indicator can be derived using known formulas or Excel formulas. By providing the break-even point, the year of the positive cash flow transition can be predicted.

[0140] By establishing a system capable of quantifying and datafying agricultural productivity and economic flows—which were previously impossible to achieve—and predicting them, the potential for capital and personnel participation can be enhanced. Numerical models for sales, operations, and profits are constructed for each business, and through comprehensive simulations and scientific analysis, indicators of economic feasibility can be provided to users.

[0141] In economic analysis (smart farm), sales and operating costs in the smart farm sector can be standardized, theorized, and quantified as shown in the economic calculation items by facility in Fig. 27, thereby enabling the calculation of annual operating data. While applying predetermined calculation formulas for each item of the smart farm analysis, user input of criteria can also be made possible as needed. Other commercial facilities, etc., can be additionally presented by establishing a separate logic. The relevant data can be provided for comprehensive analysis.

[0142] By listing smart farm inflow and outflow items by year and establishing practical / theoretical logic for each, reliable economic feasibility data can be derived.

[0143] The comprehensive results can be plotted, charted, and graphed based on the preceding data as shown in Fig. 26, and linked with user data to be viewable through UI / UX. By plotting and charting the analysis results, user accessibility and usability can be enhanced.

[0144] In this way, for the estimation / simulation of the above construction costs, operating costs, and sales revenue, the standard data for the above construction costs, operating costs, and sales revenue through the external database (220) and the cost / sales database (151) can be used along with the geographic information of the above target site through the above GIS (210). At this time, for the construction cost calculation process, a structure connecting the GIS, standard data (LH Corporation), and internal design data can be established as shown in FIG. 12. In addition, for the calculation of the overall economic analysis results of the complex, a structure connecting the database, user, input of application details, BM design, economic calculation by facility (sales, operation, profit), Excel formula, net present value method, and overall economic analysis can be established as shown in FIG. 13. Here, for the analysis formula, a structure connecting the database, user, analysis formula, UI / UX, and user can be established as shown in FIG. 14.

[0145] Furthermore, after simulating the above-mentioned construction costs, operating costs, and sales revenue, the standard data for each of the above-mentioned construction costs, operating costs, and sales revenue in the above-mentioned cost / sales database (151) can be modified to reflect reality, such as price increases, sudden changes in the supply and demand of materials, and fluctuations in consumption trends. That is, a virtuous cycle system can be established to enhance data values ​​through a system that analyzes the collected data internally and displays them to improve the accuracy of the data internally. Smart farm-related data can be standardized and systematized by collecting user data, such as farm operating costs entered by the user.

[0146] <Statistical Level>

[0147] To utilize the results of the above economic analysis, a statistical step may be further provided in which at least one of the self-indicators and overall indicators is statistically processed from the accumulated results of the above economic analysis.

[0148] In statistics, statistical results are presented for the convenience of the user, and analysis results performed by oneself or others can be presented in the form of comparison. That is, among the accumulated economic analysis results as shown in FIG. 28, cases where the parameters of each facility of the smart farm complex and each parameter of the smart farm are similar are selected, and at least one of the parameters and economic analysis results can be presented as comparison data.

[0149] In other words, simulation statistics provide a basis for a multi-faceted review of the project by publishing comparative data between the data viewed by the user and other data from similar searches. The key indicators subject to comparison are as follows: NPV, IRR, B / C, construction cost per unit area, operating cost, and revenue.

[0150] In this way, users' ability to approach data from multiple angles can be enhanced through comparative analysis of other data with similar areas.

[0151] In addition, by retrieving accumulated economic analysis results and parameters, it is possible to provide the ability to modify and review user-entered data and view analysis history, thereby promoting user convenience and efficiency.

[0152] <3D Modeling Stage>

[0153] Based on each parameter of the designed smart farm, a 3D modeling step in which 3D modeling by BIM is performed may be further provided.

[0154] A process is provided that enables the creation of a smart farm 3D model with simple operations using data such as facility area, smart farm type, cultivated crops, and internal facilities entered by the user in the preceding stage. By developing a process that allows for the easy creation of smart farm 3D models using commercial 3D modeling programs (BIM), an infrastructure is established to visualize and utilize designed facilities. In the 3D modeling process, smart farm 3D modeling is performed based on design information such as the complex and smart farm facility area, type, cultivated crops, and internal facilities designed by the user in the preceding stage. For example, modeling can be carried out using commercial BIM programs such as Revit and Twinmotion.

[0155] The modeling process is as shown in Fig. 29, and a standard smart farm data family (grouping) is established in advance to allow the user to easily design. By grouping key smart farm data, an infrastructure is established that allows the model to be modified simply by changing numerical values. Subsequently, as shown in Fig. 30, the 3D model is rendered by setting the smart farm material, sunlight / shadow, background, etc., to enhance completeness, and the results can be utilized in the form of photos, videos, etc.

[0156] In Revit, a process is established where standard smart farm information is stored (grouped) in families, allowing for the simple design of 3D models simply by inputting the information designed by the user in the preceding process. In Twinmotion, the 3D drawings created in Revit are rendered. At this stage, material information can be added, and backgrounds and other objects can be placed to generate a final result in the form of a photo or video.

[0157] Although preferred embodiments of the present invention have been described above, the present invention is not limited to the embodiments disclosed above, but can be implemented in various different forms within the scope of the claims, the detailed description of the invention, and the accompanying drawings, and equivalent alternative embodiments are possible, which are also obvious to those skilled in the art that such embodiments fall within the scope of the present invention. The embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims.

[0158] The present invention can be used in the industry of a method and system for simulating the creation of a smart farm complex using geographic information.

[0159] [Explanation of the symbol]

[0160] 10: Terminal 100: Server

[0161] 110: Target site input module 111: Input / output means

[0162] 112: Graphics I / F means 113: File I / O means

[0163] 120: Complex Design Module 121: Automatic Layout Module

[0164] 130: Smart Farm Design Module 131: Selection List Database

[0165] 140: Simulation Module

[0166] 150: Economic Analysis Module 151: Cost / Revenue Database

[0167] 160: Statistics Module 161: Statistical Indicator Database

[0168] 170: 3D Modeling Module

[0169] 210: GIS 220: External Database

[0170] 230: BIM System

Claims

1. A target site input step in which information for specifying a target site to be established for a smart farm complex including at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility along with a smart farm is input from the terminal to the server, and A complex design step in which each facility constituting the smart farm complex to be constructed at the aforementioned target site specified through geographic information is selected, and parameters for the area and image design of each facility zone are set from the terminal to the server, and A smart farm design step in which parameters such as crops of the smart farm, area per smart farm, type of smart farm, external facilities, internal facilities, and energy facilities are set from the terminal to the server, and An economic analysis step in which the simulation of the construction cost, operating cost, and sales revenue of the smart farm is performed on the server based on the parameters of each facility of the smart farm complex and each parameter of the smart farm. A method for simulating the creation of a smart farm complex using geographic information, comprising:

2. In Claim 1, Information for specifying the above-mentioned target area is, Input of lot number or address, or Specific, or via a graphic interface on the map image displayed on the terminal Uploading files for Geographic Information Systems A method for simulating the creation of a smart farm complex using geographic information, characterized by being input through 3. In claim 1 or claim 2, Land analysis for the above target site is performed on the above server. A method for simulating the creation of a smart farm complex using geographic information, characterized by 4. In claim 1 or claim 2, The area and image design parameters for each zone of the above facilities are automatically set. A method for simulating the creation of a smart farm complex using geographic information, characterized by 5. In claim 1 or claim 2, For the simulation of the above construction costs, operating costs, and sales revenue, the respective standard data for the above construction costs, operating costs, and sales revenue are used along with the geographic information of the above target site. A method for simulating the creation of a smart farm complex using geographic information, characterized by 6. In Claim 5, After the simulation of the above composition costs, operating costs, and sales revenue, each standard data of the above composition costs, operating costs, and sales revenue is modified. A method for simulating the creation of a smart farm complex using geographic information, characterized by 7. In claim 1 or claim 2, A statistical step in which at least one of the self-indicators and overall indicators is statistically processed from the accumulated results of the above economic analysis. A method for simulating the creation of a smart farm complex using geographic information, characterized by further including [ ].

8. In claim 1 or claim 2, Among the accumulated economic analysis results above, cases where the parameters of each facility of the smart farm complex and each parameter of the smart farm are similar are selected, and at least one of the parameters and the economic analysis results is presented as comparative data. A method for simulating the creation of a smart farm complex using geographic information, characterized by 9. In claim 1 or claim 2, A 3D modeling step in which 3D modeling is performed using BIM based on the parameters of each facility of the smart farm complex and each parameter of the smart farm. A method for simulating the creation of a smart farm complex using geographic information, characterized by further including [ ].

10. A target site input module in which the server receives information from the terminal for specifying a target site for establishing a smart farm complex that includes at least one of a processing and distribution facility, an exhibition and learning facility, and a commercial facility along with a smart farm, and A complex design module that selects each facility constituting the smart farm complex to be constructed at the aforementioned target site specified through geographic information, and receives parameters for the area and image design of each facility zone from the terminal, and A smart farm design module in which the server receives parameters from the terminal regarding the crops of the smart farm, area per smart farm, type of smart farm, external facilities, internal facilities, and energy facilities, and An economic analysis module in which the server performs a simulation of the construction costs, operating costs, and sales revenue of the smart farm based on the parameters of each facility of the smart farm complex and each parameter of the smart farm. A simulation system for the creation of a smart farm complex using geographic information, comprising...

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