Big data-based construction and material AI demand forecasting system and its method

Abstract

According to an embodiment of the present invention, a construction material demand forecasting system comprises a memory storing a construction material demand forecasting program, a processor that executes the construction material demand forecasting program to forecast the demand for construction materials for a target period, and a collection module that collects building construction industry GDP, building / housing permit information, building / housing commencement performance information, new construction unit price information, and a plurality of standard schedules for a base period. The processor calculates the expected total floor area for construction commencement for a predetermined target period after the base period based on the building construction industry GDP, the permit information, and the commencement performance information, forecasts the total monthly construction cost for each of the plurality of months represented by the target period based on the new construction unit price information, forecasts the total monthly demand for material quantities for each of the plurality of months based on the plurality of standard schedules, and the plurality of standard schedules includes the required period for each of the plurality of processes and the demand for material quantities for each of the plurality of processes corresponding to a plurality of construction types.

Description

Technology Field

[0001] The present invention relates to a construction material demand forecasting system and a construction material demand forecasting method. Background Technology

[0003] Market forecasts for domestic construction materials are published by research institutions and other organizations; these forecasts are released on an annual basis and include predictions for major construction materials.

[0004] The demand for construction materials may be imbalanced by region; however, since it is difficult to forecast regional markets in current market forecasts, difficulties may arise in the procurement and supply of construction materials for local communities.

[0005] Furthermore, current market forecasts rely on demand projections centered on the Financial Supervisory Service and news articles, which may result in limitations in the underlying data. Consequently, this can lead to a problem where the accuracy of demand forecasting for construction materials is compromised. Prior art literature

[0007] Patent Document 0001) Korean Published Patent No. 10-2011-0019121 (Published February 25, 2011) The problem to be solved

[0008] The objective of the present invention is to solve the aforementioned problems by providing a construction material demand forecasting system and a construction material demand forecasting method that utilize various data to forecast the demand for construction materials and classify and forecast the demand for construction materials by month and region. means of solving the problem

[0010] According to an embodiment of the present invention, to achieve the above-mentioned purpose, a construction material demand forecasting system comprises a memory storing a construction material demand forecasting program, a processor that executes the construction material demand forecasting program to forecast the demand for construction materials for a target period, and a collection module that collects building construction industry GDP, building / housing permit information, building / housing commencement performance information, new construction unit price information, and a plurality of standard schedules for a base period. The processor calculates the expected total floor area for construction commencement for a predetermined target period after the base period based on the building construction industry GDP, the permit information, and the commencement performance information, forecasts the total monthly construction cost for each of the multiple months represented by the target period based on the new construction unit price information, forecasts the total monthly demand for material quantities for each of the multiple months based on the plurality of standard schedules, and the plurality of standard schedules includes the required period for each of the multiple processes and the demand for multiple material quantities for each of the multiple processes corresponding to multiple construction types.

[0011] The processor may further include a first prediction unit that derives a first gross floor area corresponding to each of a plurality of first construction projects that have commenced construction based on a reference point after the base period from the commencement performance information, estimates the total construction cost of each of the plurality of first construction projects based on the first gross floor area corresponding to each and the new construction unit price information, and predicts the first monthly construction cost and the first demand for the monthly material quantity for each of the plurality of months based on the total construction cost and the time required for each process and the demand for the monthly material quantity indicated by the plurality of standard schedules.

[0012] The processor may include a correlation analysis unit that analyzes a first correlation between the building construction industry GDP and the permit information and a second correlation between the building construction industry GDP and the construction start performance information, predicts an estimated value of the building construction industry GDP for the target period, and predicts a total floor area for construction start for the target period corresponding to the estimated value of the building construction industry GDP based on the first correlation and the second correlation.

[0013] The processor further includes a second forecasting unit that calculates a second floor area by subtracting the sum of the first floor areas corresponding to each of the plurality of first construction projects from the total floor area to be started, and forecasts the demand for monthly material quantities for the target period for the second floor area based on the second floor area and a plurality of detailed schedules, wherein the plurality of detailed schedules correspond to a plurality of construction types and may include the required period for each of the plurality of processes and the demand for a plurality of material quantities for each of the plurality of processes.

[0014] The second prediction unit above can generate an artificial intelligence model to learn a method for predicting the approval date for use of each of the multiple construction projects, and use the learned artificial intelligence model to generate the multiple detailed process tables corresponding to the second floor area.

[0015] The second prediction unit can predict a second total construction cost corresponding to the second floor area based on the second floor area and the new construction unit price information, predict a second monthly construction cost for each of the plurality of months based on the second total construction cost and the plurality of detailed schedules, and predict a second demand for the monthly material quantity based on the demand for the monthly material quantity for the target period corresponding to the second floor area.

[0016] The processor may further include a summing unit that calculates a total monthly construction cost corresponding to each of the plurality of months by summing the first monthly construction cost and the second monthly construction cost for each of the plurality of months, and calculates a total monthly material quantity by summing the first demand and the second demand for the monthly material quantity.

[0017] The above summing unit can predict the monthly construction cost, the monthly construction cost by work type, and the monthly material quantity demand for each of the multiple regions and each of the multiple work types represented by the multiple construction projects, based on the total monthly construction cost and the total demand for the monthly material quantity.

[0018] According to another embodiment of the present invention, a method for a processor to execute a construction material demand forecasting program stored in memory to forecast the demand for a plurality of construction materials for a target period comprises: collecting building construction industry GDP, building / housing permit information, building / housing start performance, new construction unit price information, and a plurality of standard schedules for a base period; calculating the expected total floor area for construction starts for a predetermined target period after the base period based on the building construction industry GDP, the permit information, and the start performance information; forecasting the total monthly construction cost for each of the plurality of months represented by the target period based on the new construction unit price information; and forecasting the total monthly demand for material quantities for each of the plurality of months based on the plurality of standard schedules, wherein the plurality of standard schedules may include the required period for each of the plurality of processes and the demand for a plurality of material quantities for each of the plurality of processes corresponding to a plurality of construction types.

[0019] The method may further include the steps of: deriving a first gross floor area corresponding to each of a plurality of first construction projects that have commenced construction based on a reference point after the base period from the above commencement performance information; estimating the total construction cost of each of the plurality of first construction projects based on the first gross floor area corresponding to each and the above new construction unit price information; and predicting the first monthly construction cost and the first demand for monthly material volume for each of the plurality of months based on the total construction cost and the time required for each process and the demand for monthly material volume indicated by the plurality of standard schedules.

[0020] The step of calculating the expected total floor area for construction starts for the above target period may include: a step of analyzing a first correlation between the building construction industry GDP and the permit information; a step of analyzing a second correlation between the building construction industry GDP and the construction start performance information; and a step of predicting an expected value of the building construction industry GDP for the above target period, and predicting a total floor area for construction starts for the above target period corresponding to the expected value of the building construction industry GDP based on the first correlation and the second correlation.

[0021] The method further includes the step of calculating a second floor area by subtracting the sum of the first floor areas corresponding to each of the plurality of first construction projects from the total floor area at commencement, and the step of predicting the demand for monthly material quantities for the target period for the second floor area based on the second floor area and a plurality of detailed schedules, wherein the plurality of detailed schedules correspond to a plurality of construction types and may include the required period for each of the plurality of processes and the demand for a plurality of material quantities for each of the plurality of processes.

[0022] The method may further include a step of learning how an artificial intelligence model predicts the approval date for use of each of a plurality of construction projects, and a step of generating the plurality of detailed process tables corresponding to the second gross floor area using the learned artificial intelligence model.

[0023] The method may further include the step of predicting a second total construction cost corresponding to the second floor area based on the second floor area and the new construction unit price information, the step of predicting a second monthly construction cost for each of the plurality of months based on the second total construction cost and the plurality of detailed schedules, and the step of predicting a second demand for the monthly material quantity based on the demand for the monthly material quantity for the target period corresponding to the second floor area.

[0024] The method may further include the step of calculating the total monthly construction cost corresponding to each of the plurality of months by adding the first monthly construction cost and the second monthly construction cost for each of the plurality of months, and the step of calculating the total demand for the monthly material quantity by adding the first demand and the second demand for the monthly material quantity.

[0025] Based on the above-mentioned total monthly construction costs and total demand for monthly material quantities, the method may further include the step of predicting monthly construction costs, monthly construction costs by work type, and monthly material quantity demand for each of the multiple regions and each of the multiple work types represented by the above-mentioned multiple construction projects.

[0026] According to another embodiment of the present invention, a computer-readable recording medium may have a program recorded thereon for executing a method of predicting the demand for a plurality of construction materials for the aforementioned target period. Effects of the invention

[0028] According to the present invention, accurate demand for construction materials can be predicted by utilizing the GDP of the building construction industry.

[0029] According to the present invention, construction material demand can be predicted by month and by region by utilizing a standard process table.

[0030] According to the present invention, difficulties in procuring and supplying construction materials in local communities can be resolved by predicting the demand for construction materials by region.

[0031] According to the present invention, construction-related companies can reduce economic losses caused by incorrect demand forecasting by facilitating the supply and demand of materials through construction material demand forecasting.

[0032] According to the present invention, the demand for construction materials by region and by type of work is predicted, and the user can receive information such as the demand for materials. Brief explanation of the drawing

[0034] FIG. 1 is a conceptual diagram showing a framework of basic data for predicting construction material demand in a construction material demand prediction system according to one embodiment of the present invention. FIGS. 2 and FIGS. 3 are example diagrams of a condition setting screen displayed in a user interface section according to one embodiment. FIG. 4 is an example of a screen displayed in a user interface section according to one embodiment without setting conditions. FIG. 5 is an example diagram of a screen showing the demand for material quantities displayed in a user interface section according to one embodiment. FIG. 6 is a flowchart of a method for forecasting demand for construction materials according to one embodiment. Specific details for implementing the invention

[0035] The present invention may be implemented with various modifications without departing from the spirit, and may have one or more embodiments. Furthermore, the embodiments described in the "specific details for implementing the invention" and "drawings," etc., in the present invention are examples for specifically explaining the present invention and do not limit or restrict the scope of the rights of the present invention.

[0036] Accordingly, anything that a person skilled in the art to which the present invention pertains can easily deduce from the “specific details for carrying out the invention” and “drawings,” etc., of the present invention may be interpreted as falling within the scope of the present invention.

[0037] In addition, the size and shape of each component shown in the drawings may be exaggerated for the purpose of explaining the embodiments and do not limit the actual size and shape of the invention.

[0038] Unless specifically defined otherwise in the specification of the present invention, terms used therein may have the same meaning as generally understood by those skilled in the art to which the present invention pertains.

[0039] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0040] FIG. 1 is a conceptual diagram showing a framework of basic data for predicting construction material demand in a construction material demand prediction system according to one embodiment of the present invention.

[0041] Referring to FIG. 1, the construction material demand forecasting system (1) may include a memory (100), a processor (200), a collection module (300), and a user interface unit (400).

[0042] The memory (100) may include volatile memory and / or non-volatile memory. The memory (100) may store commands or data related to the processor (200), collection module (300), and user interface unit (400), one or more programs and / or software, operating systems, etc., for example, to implement and / or provide operations, functions, etc. provided by the construction material demand forecasting system (1).

[0043] The memory (100) may include a database (110). The database (110) may store data collected from the collection module (300) after refining it.

[0044] The program stored in memory (100) may include a program for predicting the demand for construction materials (hereinafter, "construction material demand prediction program"). The construction material demand prediction program may utilize big data such as GDP of the building construction industry, a building permit information DB, and a housing start performance DB, and provide a construction material demand prediction service based on a standard construction schedule.

[0045] The processor (200) can control the overall operation of each of the memory (100), the collection module (300), and the user interface unit (400). The processor (200) may be a computing device. The processor (200) may include at least one of a processing device (Processor), such as an ASIC (Application Specific Integrated Circuit), a DSP (Digital Signal Processor), a PLD (Programmable Logic Device), a FPGA (Field Programmable Gate Array), a CPU (Central Processing Unit), a microcontroller, and a microprocessor.

[0046] The processor (200) can predict the demand for construction materials for multiple construction projects by running a construction material demand forecasting program.

[0047] The collection module (300) can receive building construction GDP information (311) from a public data provision server (310) via an API (Application Programming Interface). The public data provision server (310) may be a server that provides statistical data such as GDP. For example, the public data provision server (310) may be the "Statistics Korea." The collection module (300) may provide the building construction GDP information (311) to the processor (200). The processor (200) may extract the building construction industry GDP from the building construction GDP information (311). In the following, the building construction GDP may be the GDP value of the building construction sector corresponding to a predetermined base period (hereinafter, "base period"). Here, the base period may be a predetermined period prior to the current point in time. The base period may be a value entered by the user or a value predetermined by the processor (200). For example, the base period may be a period corresponding to the previous year (e.g., from January 1, 2022 to December 31, 2022) based on the current time (e.g., October 1, 2023) when the processor (200) ran the construction material demand forecasting program.

[0048] The collection module (300) can receive permit information and construction start performance information (321) from the construction data providing server (320) via an API. The construction data providing server (320) may be a server that provides construction administration services. Here, the permit information may include construction / housing permit information, and the construction start performance information may include construction / housing construction start performance information. The collection module (300) can provide the permit information and construction start performance information (321) to the processor (200). The processor (200) can extract construction / housing permit information and construction / housing construction start performance information from the permit information and construction start performance information (321).

[0049] Information on building / housing permits and building / housing commencement records may include the gross floor area, region, time of permit issuance, time of commencement, types of construction included, and use for each of multiple construction projects. The gross floor area may be the sum of the floor areas of buildings constructed on the site represented by each construction project. The unit of the gross floor area is thousand square meters (m²). 2 ...may be. The region may indicate the location of the building, etc., that is the subject of each construction project. The type of work may indicate the type of construction, such as steel frame work, masonry work, tile work, electrical work, and plumbing work. The use may be a single-family house, multi-family house, neighborhood living facility, cultural and assembly facility, religious facility, retail facility, medical facility, etc.

[0050] The building / housing permit information and building / housing commencement performance information may include information on building projects that have been permitted and construction projects that have commenced based on a base period, as well as information on building projects that have already been permitted and construction projects that have already commenced based on a reference point after the base period. The reference point may be, for example, the final point in time when the collection module (300) collected the permit information and construction commencement performance information (321).

[0051] Building / housing permit information may include the gross floor area and the date of permit issuance for each building / housing project that received building / housing permits based on the reference point. Building / housing commencement performance information may include the gross floor area and the date of commencement for each building / housing project that started construction based on the reference point.

[0052] Building / housing permit information and building / housing start performance information can be classified by region, structure, and multiple building types.

[0053] In the case of a construction project, once an application for a building / housing permit is submitted, construction may commence after the permit is granted. Hereinafter, the period from the commencement date of construction to the date of approval for use for a construction project may be referred to as the "construction period."

[0054] In one embodiment, the processor (200) can calculate the expected total floor area for a predetermined target period (hereinafter, "target period") following a predetermined base period based on building construction industry GDP, licensing information, and construction start performance information, predict the total monthly construction cost for each of the multiple months represented by the target period based on new construction unit price information, and predict the total monthly demand for material volume for each of the multiple months based on multiple standard schedules.

[0055] The processor (200) can predict the construction period for at least one construction project that has not commenced within the base period prior to the current point in time. Additionally, the processor (200) can predict the total construction cost for each of the multiple months belonging to the target period and the total demand for the quantity of materials required for each of the multiple months based on the predicted construction period. Here, the target period may be a predetermined period after the base period. The target period may be a value entered by the user or a value predetermined by the processor (200). For example, the target period may be a period corresponding to the current year (e.g., from January 1, 2023 to December 31, 2023) that includes the current point in time (e.g., October 1, 2023) when the processor (200) executed the construction material demand prediction program. For example, the multiple months may be 12 months from January 2023 to December 2023.

[0056] A construction project may be a concept that includes multiple processes, multiple types of work, etc. In the following description, multiple construction projects may represent construction projects in which a construction material demand forecasting system (1) according to one embodiment predicts that at least one of multiple processes, multiple types of work, etc. will be performed within a target period. However, this is merely an example and the invention is not limited thereto.

[0057] The monthly total demand for total construction costs and material volumes predicted by the processor (200) may represent the demand for construction costs and material volumes for corresponding construction projects among multiple construction projects. In the following, the operation of the processor (200) predicting the monthly total demand for total construction costs and material volumes may include an operation of predicting the demand for construction costs and material volumes by dividing them into the demand for each corresponding construction project.

[0058] The aforementioned reference point may be a point in time within the target period, or the target period may be a predetermined period after the reference point.

[0059] The collection module (300) can receive or collect new construction unit price information (331) from the real estate data provision server (330) via API or through data crawling. The real estate data provision server (330) can provide services such as public disclosure of prices related to real estate. For example, the real estate data provision server (330) is "Korea Real Estate Institute," and the collection module (300) can collect new construction unit price information (331) by crawling data from the Korea Real Estate Institute. The new construction unit price information (331) may include the new construction unit price per unit area corresponding to each of the multiple building types and / or the new construction unit price per unit area by construction process corresponding to each of the multiple building types.

[0060] The collection module (300) can receive a standard schedule (341) from the input unit (340). The user can manually create the standard schedule (341), and the input unit (340) can receive the standard schedule (341) from the user through a user terminal, etc. When the user creates the standard schedule (341), they can create it by including information obtained through expert interviews corresponding to each of the multiple construction types. There are differences in construction periods and standard schedules, etc., depending on the construction type corresponding to each construction project. Therefore, the standard schedule can be created through experts corresponding to each of the multiple construction types.

[0061] A standard schedule (341) includes multiple standard schedules, and the multiple standard schedules may include the required duration for each of the multiple processes belonging to each of the multiple types of construction work corresponding to each of the multiple types of construction work, as well as the types and demands of multiple material quantities for each of the multiple processes. Additionally, the required duration for each of the multiple processes belonging to each of the multiple standard schedules may be represented by the types of processes corresponding to each month. The multiple types of construction work, multiple processes, etc. corresponding to each of the multiple types of construction work may not be identical to one another.

[0062] In the following description, "materials" may refer to construction materials, etc., required for the subject construction project. "Demand for material quantities" may refer to the quantity of materials that need to be ordered among each material quantity. "Multiple construction types" may refer to the uses of facilities constructed in the subject construction project. For example, multiple construction types may include housing, offices, apartments, neighborhood living facilities, etc. "Required period" may include the required period for each of the multiple processes and / or the required period for the entire construction corresponding to the respective construction type. "Multiple processes" may include major construction processes included in the construction. For example, multiple processes may include foundation work, concrete work, etc.

[0063] The standard construction schedule (341) may include information obtained through expert interviews corresponding to each of the multiple construction types.

[0064] In this specification, the method by which the collection module (300) collects building construction GDP information (311), permit information and construction start record information (321), new construction unit price information (331), standard construction schedule (341), etc. is described as being through API, data crawling, input unit (340), etc., but this is for convenience of explanation only and the invention is not limited thereto. The collection module (300) can collect building construction GDP information (311), permit information and construction start record information (321), new construction unit price information (331), and standard construction schedule (341) not only through collection via API, data crawling, and input unit (340), but also through various data collection methods.

[0065] The processor (200) can derive a monthly process table indicating the main process for each month, the demand for monthly material quantities, etc. from a plurality of standard process tables.

[0066] The collection module (300) can format data from the standard process table (341). For example, the collection module (300) can extract multiple standard process tables from the standard process table (341) and classify them for each of the multiple construction types.

[0067] The collection module (300) can store permit information and construction start record information (321), new construction unit price information (331), and standard construction schedule (341) in the database (110).

[0068] The processor (200) may include a correlation analysis unit (201), a detailed process table determination unit (202), and a prediction unit (203).

[0069] The correlation analysis unit (201) can analyze the correlation between the building construction industry GDP and building / housing permits and / or the correlation between the building construction industry GDP and building / housing start-up performance based on building construction GDP information (311) and permit information and start-up performance information (321) for a base period. The collection module (300) can provide the building construction GDP information (311) and permit information and start-up performance information (321) to the correlation analysis unit (201).

[0070] The correlation analysis unit (201) can analyze the first correlation between the GDP of the building construction industry and the building / housing permits for the base period. The correlation analysis unit (201) can analyze the second correlation between the GDP of the building construction industry and the construction / housing start performance for the base period.

[0071] The correlation analysis unit (201) can calculate the total floor area for construction starts for the target period (hereinafter, "expected total floor area for construction starts") from the expected value of the building construction industry GDP for the target period based on the correlation between the building construction industry GDP and building / housing permits and / or the correlation between the building construction industry GDP and the actual number of construction starts for the target period. The expected total floor area for construction starts can be represented as the floor area value scheduled to be started within the target period in correspondence with the actual number of construction starts for the target period. Here, the expected total floor area for construction starts may be the sum of the first floor area corresponding to each of the multiple first construction projects that have already started construction based on the reference point included in the permit information and construction start performance information (321), and the second floor area corresponding to the multiple second construction projects that have not yet started construction based on the reference point.

[0072] The detailed schedule determination unit (202) can predict multiple detailed schedules based on permit information, construction commencement performance information (321), and standard schedule (341). The collection module (300) can provide the permit information, construction commencement performance information (321), and standard schedule (341) to the detailed schedule determination unit (202).

[0073] The detailed process schedule determination unit (202) can generate an artificial intelligence model (2021). The artificial intelligence model (2021) may be a model that has learned a method to predict the required duration and material quantity for each of the multiple processes included in the construction projects from the permit and commencement records of the construction projects. And / or the artificial intelligence model (2021) may be a model that has learned a method to predict the required duration and material quantity for each of the multiple processes from the scale of each construction project. In the following, the scale may represent the gross floor area value.

[0074] The detailed schedule determination unit (202) can extract building / housing permit information and building / housing construction start performance information from permit information and construction start performance information (321). The building / housing permit information and building / housing construction start performance information extracted by the detailed schedule determination unit (202) may include the permit / construction start time, building type, and gross floor area indicating the scale of construction for multiple first construction projects that have already started construction based on the reference time.

[0075] The detailed process schedule determination unit (202) can extract a plurality of standard process schedules from the standard process schedule (341). The plurality of standard process schedules extracted by the detailed process schedule determination unit (202) may correspond to a plurality of construction types. For example, the plurality of standard process schedules may include a first standard process schedule corresponding to a "housing" construction type, a second standard process schedule corresponding to a "multi-unit housing" construction type, and a third standard process schedule corresponding to a "neighborhood living facility" construction type.

[0076] The detailed schedule determination unit (202) can match one of the multiple standard schedules to the permit information and construction commencement record information corresponding to each of the multiple first construction projects. Specifically, the detailed schedule determination unit (202) can extract the construction type of each of the multiple first construction projects and match the standard schedule among the multiple standard schedules that corresponds to the construction type to the permit information and construction commencement record information corresponding to the construction project.

[0077] The detailed process schedule determination unit (202) may provide the permit information and construction commencement performance information (321) collected from the construction data provision server (320) as training data to the artificial intelligence model (2021). The training data may include the permit date, construction commencement date, and occupancy approval date corresponding to projects that have already been approved for use among the plurality of first construction projects represented by the permit information and construction commencement performance information (321). The permit information and construction commencement performance information for projects that have already been approved for use among the permit information and construction commencement performance information (321) may include the occupancy approval date approved after the building permit and construction commencement.

[0078] The artificial intelligence model (2021) can learn how to predict the approval date of construction projects using training data. For example, the artificial intelligence model (2021) can determine weights and biases in an activation function for deriving the approval date of a project based on at least one of the permit date, commencement date, and gross floor area of ​​a project among the training data, and can predict the approval date for projects that have not yet been approved for use (without approval date information) among a plurality of first construction projects represented by permit information and commencement record information (321) based on the determined weights and biases.

[0079] The detailed schedule determination unit (202) can predict the approval date for a plurality of second construction projects indicated by permit information and commencement of construction performance information (321) using a learned artificial intelligence model (2021), and predict the required period and material quantity for each of the plurality of processes included in the plurality of second construction projects in accordance with the predicted approval date. Hereinafter, the approval date and the required period and material quantity for each of the plurality of processes predicted by the detailed schedule determination unit (202) using the learned artificial intelligence model (2021) are referred to as "multiple detailed schedules." Similar to a standard schedule, the multiple detailed schedules correspond to multiple construction types and may include the required period for each of the plurality of processes and the required material quantity for each of the plurality of processes.

[0080] The prediction unit (203) can predict the permit and construction performance corresponding to the second floor area by excluding the sum of the first floor area for each of the multiple first construction projects from the expected total floor area for construction, and predict the demand for the quantity of materials corresponding to the second floor area according to the detailed schedule corresponding to the permit and construction performance corresponding to the second floor area among the multiple detailed schedules.

[0081] The prediction unit (203) can predict the total construction cost corresponding to the total floor area based on the new construction unit price information (331), by building type, region, etc., and derive the required period for each process and the demand for material volume by building type, region, etc. based on multiple standard schedules and multiple detailed schedules. The prediction unit (203) can predict the total construction cost and the total demand for material volume by dividing the total construction cost and the demand for material volume into each of the multiple months represented by the target period based on multiple standard schedules and multiple detailed schedules.

[0082] The user interface unit (400) can generate a user interface in which the processor (200) visualizes the demand for monthly construction costs and monthly material quantities, and provide the generated user interface to the user through an output device, a user terminal, etc. Here, the output device may be a display device. For example, the user interface unit (400) can generate a screen showing the demand for monthly construction costs and monthly material quantities predicted by the processor (200) and provide it through an output device, a user terminal, etc. The user can view the screen provided through the user interface unit (400).

[0083] The user interface unit (400) may generate and provide multiple screens displaying the total monthly construction costs and the total demand for monthly material quantities for a target period. The multiple screens generated by the user interface unit (400) may include screens displaying the total monthly construction costs and the total demand for monthly material quantities for a target period, categorized by region, building type, construction type, etc.

[0084] Additionally, the screen provided by the user interface unit (400) may include at least one menu. When a user selects one of at least one menu on the screen, the user interface unit (400) may transmit a signal corresponding to the menu selected by the user to the processor (200).

[0085] The correlation analysis unit (201) can predict the expected value of the building construction industry GDP for the target period based on the building construction industry GDP for the base period, or receive the expected value of the building construction industry GDP for the target period.

[0086] The correlation analysis unit (201) can analyze the trend of the building construction industry GDP for a base period and predict the expected value of the building construction industry GDP for a target period based on the analyzed trend. For example, the correlation analysis unit (201) can determine the trend prediction value of the building construction industry GDP by performing regression analysis on the building construction industry GDP for a predetermined period and determine the expected value of the building construction industry GDP for a target period based on the trend prediction value of the building construction industry GDP.

[0087] Or, in some embodiments, the correlation analysis unit (201) can determine the estimated GDP value of the building construction industry for the target period based on the value received from the user through the user interface unit (400).

[0088] The prediction unit (203) may include a first prediction unit (2031), a second prediction unit (2032), and a summation unit (2033).

[0089] The first prediction unit (2031) can extract the first gross floor area and the start date corresponding to each of the multiple first construction projects that have already started construction based on the reference point from the construction start performance information, estimate the first total construction cost for each of the multiple first construction projects, and predict the first demand for the first monthly construction cost and monthly material volume for the target period.

[0090] The second prediction unit (2032) derives the remaining second floor area excluding the sum of the first floor area corresponding to each of the multiple first construction projects from the total floor area expected to start construction for the target period calculated by the correlation analysis unit (201), predicts the time of permit issuance and the time of start construction for each of the multiple second construction projects corresponding to the second floor area, estimates the second total construction cost corresponding to the second floor area, and can predict the second demand for the second monthly construction cost and monthly material volume for the target period.

[0091] The summing unit (2033) can derive the final monthly total construction cost and material quantity demand by summing the first demand for the first monthly construction cost and material quantity predicted by the first prediction unit (2031) and the second demand for the second monthly construction cost and material quantity predicted by the second prediction unit (2032).

[0092] The summing unit (2033) can predict monthly construction materials and construction based on the total monthly construction cost and the total demand for monthly material volumes and construction costs by type of work. The monthly construction materials and construction may include the demand for monthly construction costs and material volumes corresponding to each of multiple types of work and multiple regions.

[0093] The final monthly total construction cost and material volume demand derived by the aggregation unit (2033) may be matched with construction / housing permit information and construction / housing commencement performance information for each of the multiple construction projects corresponding to the monthly total construction cost and material volume demand. The aggregation unit (2033) can extract information representing regions and information representing construction types from the construction / housing permit information and construction / housing commencement performance information matched to each of the multiple construction projects.

[0094] The aggregation unit (2033) can predict regional construction costs and material demand. Regional construction costs and material demand may correspond to construction costs and material demand for each of multiple regions indicated by building / housing permit information and building / housing commencement performance information. The aggregation unit (2033) can predict monthly construction costs, monthly construction costs by type of work, and monthly material volume demand for each of the multiple regions.

[0095] Additionally, the summing unit (2033) can predict construction costs and material demand by type of construction (type of construction work). It can predict construction costs and material demand corresponding to each type of construction that proceeds within the target period among the multiple types of construction represented by multiple construction projects. The summing unit (2033) can predict monthly construction costs, monthly construction costs by type of construction, and monthly material demand for each of the multiple types of construction.

[0096] The permit and construction performance records of the construction projects included in the permit information and construction performance records (321) may include information that can be identified by construction type and by region. The summation unit (2033) can derive the total construction cost, construction cost by construction type, and demand for material volume by construction type, region, month, material, and use of the construction project based on the information that can be identified by construction type and by region included in the permit information and construction performance records (321).

[0097] The user interface unit (400) can generate a screen that displays the total construction cost, construction cost by type of work, and demand for material quantities derived from the summation unit (2033), categorized by type of work, region, month, material, and use of the construction project.

[0098] FIGS. 2 and FIGS. 3 are example diagrams of a condition setting screen displayed in a user interface section according to one embodiment.

[0099] Referring to FIG. 2, the user interface unit (400) can generate and display a filter screen (DP1) including a "filter setting" button.

[0100] When the user selects the "Filter Settings" button on the filter screen (DP1), the user can select conditions to display the total construction cost, construction cost by type of work, and demand for material quantities derived from the summation unit (2033) on the screen by type of work, region, month, material, use of the construction project, etc.

[0101] Referring to FIG. 3, the user interface unit (400) can display a screen (DP2) that displays candidate conditions that the user can select.

[0102] The screen (DP2) includes a menu representing multiple months ("1"), multiple materials ("2"), multiple regions ("3"), and multiple uses ("4"), and may include a checkbox that allows the user to select at least one of the menus displayed on the screen (DP2).

[0103] The user can input at least one of multiple months ("1"), multiple materials ("2"), multiple regions ("3"), and multiple uses ("4") from the menu displayed on the screen (DP2) into the user interface section (400).

[0104] The screen (DP2) can display detailed items according to the user's selection for a menu that includes detailed items among menus representing multiple months ("1"), multiple materials ("2"), multiple regions ("3"), and multiple uses ("4"). For example, if the user selects a menu corresponding to multiple materials ("2"), the user interface unit (400) can display a detailed screen ("2-1") representing the materials ("2") on the screen. If the user selects a menu corresponding to multiple regions ("3"), the user interface unit (400) can display a detailed screen ("3-1") representing the regions ("3") on the screen.

[0105] Based on a signal input by the user, the user interface unit (400) can generate a screen including the total construction cost, construction cost by type of work, and the demand for material quantities in response to at least one selected from multiple months ("1"), multiple materials ("2"), multiple regions ("3"), and multiple uses ("4").

[0106] FIG. 4 is an example of a screen displayed in a user interface section according to one embodiment without setting conditions.

[0107] The user may not select the "Filter Settings" button on the filter screen (DP1) shown in FIG. 2, or select any of the multiple months ("1"), multiple materials ("2"), multiple regions ("3"), or multiple uses ("4") shown in FIG. 3. Referring to FIG. 4, if the user does not select the "Filter Settings" button on the filter screen (DP1) shown in FIG. 2, or select any of the multiple months ("1"), multiple materials ("2"), multiple regions ("3"), or multiple uses ("4") shown in FIG. 3, the user interface unit (400) may display a screen (DP3) that displays the total construction cost, construction cost by type of work, and demand for material quantities derived from the summation unit (2033), categorized by type of work, region, month, material, or use of the construction project.

[0108] The user can select one of the material quantity demands classified by construction type, region, month, material, or use of the construction project, and view the list representing that demand.

[0109] Referring to FIG. 4, the screen (DP3) displayed by the user interface unit (400) may include multiple graphs showing the demand for material quantities by multiple months, region, use, and structure.

[0110] FIG. 5 is an example diagram of a screen showing the demand for material quantities displayed in a user interface section according to one embodiment.

[0111] Referring to FIG. 5, when a user selects one item among the demand for material quantities shown as a graph in FIG. 4, the user interface unit (400) can generate and display a screen (DP4) that displays a list of projects corresponding to the demand for the material quantities.

[0112] The list of each project displayed on the screen (DP4) may include site information, gross floor area, estimated quantity, commencement date, date of approval for use, total commencement period, start and end dates for each construction / process, etc. Here, the date of approval for use may be the actual date of approval for use, or, according to one embodiment, may be the date of approval for use predicted by an artificial intelligence model (2021) ("AI prediction").

[0113] The user can select one of the projects on the list displayed on the screen (DP4) and input it into the user interface unit (400). Based on the signal input by the user, the user interface unit (400) can generate and display a screen showing the address of the selected project on the list, the type of permit (e.g., new construction, reconstruction, redevelopment, etc.), the use (e.g., multi-unit housing), the construction progress stage (e.g., scheduled for use inspection), and the construction / design / supervision / building owner.

[0114] FIG. 6 is a flowchart of a method for forecasting demand for construction materials according to one embodiment.

[0115] Below, any redundant descriptions of the construction material demand forecasting system (1) described above may be omitted.

[0116] The first prediction unit (2031) can extract the first floor area and the scheduled start date for each of the multiple first construction projects that have already started construction based on the reference point from the construction start performance information (S110).

[0117] The first prediction unit (2031) can estimate the first total construction cost for each of the multiple first construction projects based on the first floor area and new construction unit price information (S120).

[0118] Since the new construction unit price information includes new construction unit prices per unit area for multiple building types, regions, structures, and building types, the first prediction unit (2031) can calculate the construction cost for multiple building types, regions, structures, types of work, and construction start schedules corresponding to the first gross floor area based on the new construction unit price information. The first prediction unit (2031) can derive the first total construction cost corresponding to each of the multiple first construction projects by multiplying the new construction unit price of the building type corresponding to the first gross floor area of ​​each of the multiple first construction projects. The first prediction unit (2031) can derive the first total construction cost by classifying it into multiple building types, regions, structures, types of work, and construction start schedules based on the new construction unit price information.

[0119] Following step S120, the first prediction unit (2031) can estimate the construction cost for each of the first construction projects by dividing the first total construction cost for each of the first construction projects into corresponding construction types among the multiple construction types indicated by the multiple standard construction schedules based on the multiple standard construction schedules (S130).

[0120] The first total construction cost can be divided into multiple first detailed construction costs by multiple building types, regions, structures, types of work, and start dates. The second forecasting unit (2032) can derive construction costs by type of work from the multiple first detailed construction costs.

[0121] Following step S110, the first forecasting unit (2031) can forecast the demand for monthly material volumes based on the first floor area of ​​each of the multiple first construction projects and multiple standard schedules (S140).

[0122] The first forecasting unit (2031) can forecast the monthly material volume demand of each of the multiple first construction projects based on the monthly material volume demand indicated by the multiple standard process tables.

[0123] Each of the multiple standard schedules may include the demand for material quantities in the corresponding construction type. Accordingly, the first forecasting unit (2031) can forecast the demand for material quantities based on the standard schedule corresponding to each of the multiple first construction projects among the multiple standard schedules.

[0124] The first forecasting unit (2031) can predict the monthly demand for material volume by dividing the demand for material volume by process corresponding to each of the multiple first construction projects by the required period corresponding to each process. For example, if the X process of Project A among the multiple first construction projects takes 6 months, the monthly demand for X1 material volume required for the X process is 0.6 tons, and the monthly demand for X2 material volume required for the X process is 1.2 tons, then the monthly demand for material volume corresponding to the X process may be 0.1 tons for X1 material volume and 0.2 tons for X2 material volume.

[0125] Following steps S120 and S140, the first forecasting unit (2031) can forecast the first monthly construction cost and the first demand for the monthly material volume for each of the multiple months represented by the target period (S150).

[0126] The first forecasting unit (2031) can predict the first monthly construction cost by summing the monthly construction costs of projects corresponding to each of the multiple months represented by the target period among the multiple first construction projects, based on the total construction cost per project estimated in step S120. The total construction cost per project can be divided by month based on the time required for each process represented by multiple standard schedules.

[0127] The first forecasting unit (2031) can predict the first demand for monthly material volume by summing the demand for monthly material volume of projects corresponding to each of the multiple months of the target period among the multiple first construction projects, based on the demand for monthly material volume predicted in step S140. For example, the explanation assumes a case where the X process of Project A and the Y process of Project B correspond to January 2023, which falls within the target period among the multiple first construction projects. If the X process takes 6 months and the demand for the X1 material volume for the X process is 0.1 tons per month, and the Y process takes 3 months and the demand for the Y1 material volume for the Y process is 0.2 tons per month, the first demand for monthly material volume corresponding to January 2023 may be 0.3 tons, which is the sum of 0.1 tons and 0.2 tons.

[0128] The correlation analysis unit (201) can predict the number of permits and the number of construction starts for a target period based on the expected value of the building construction industry GDP for the target period, based on the first correlation between the building construction industry GDP and the building / housing permits and / or the second correlation between the building construction industry GDP and the number of construction starts for the target period, and can calculate the expected total floor area of ​​construction starts based on the number of permits and the number of construction starts for the target period (S210). For convenience of explanation below, the first correlation and / or the second correlation will be referred to as the "building construction industry GDP correlation."

[0129] The correlation analysis unit (201) analyzes the correlation of the building construction industry GDP and, based on the correlation of the building construction industry GDP, can predict the total permit performance and total construction start performance within a target period corresponding to the estimated value of the building construction industry GDP. The correlation analysis unit (201) can predict the total floor area of ​​construction start for a target period corresponding to the total permit performance and total construction start performance within a target period based on the correlation of the building construction industry GDP.

[0130] The second prediction unit (2032) can predict the time of permit issuance and the time of commencement of construction for each of the multiple second construction projects corresponding to the second floor area based on the first correlation between the building construction industry GDP and the building / housing permit information analyzed by the correlation analysis unit (201) and / or the second correlation between the building construction industry GDP and the construction / housing commencement performance.

[0131] The correlation analysis unit (201) can analyze building construction GDP information (311 in FIG. 1) and permit information and construction start performance information (321 in FIG. 1) to derive the permit and construction start performance within the base period indicated by the building construction GDP information.

[0132] For example, the correlation analysis unit (201) can calculate the expected volume of permit orders within the target period from the expected value of GDP for the building construction industry for the target period based on the first correlation. Additionally, the correlation analysis unit (201) can predict the performance (e.g., number of construction starts) predicted to start within the target period from the expected volume of permit orders within the target period based on the second correlation.

[0133] In one embodiment, the correlation analysis unit (201) derives a trend for the GDP of the building construction industry, determines a trend prediction value based on the derived trend, and can predict the demand for construction start performance using the trend prediction value. The processor (200) can predict the demand for material volume (concrete, rebar, formwork, steel frame, etc.) according to the demand per floor area based on the correlation with construction start performance.

[0134] The correlation with GDP of the building construction industry may include the performance of permits and construction starts for the period corresponding to the GDP of the building construction industry. The correlation analysis unit (201) can predict the performance of permits and construction starts within the target period and the expected total floor area of ​​construction starts for the target period based on the correlation with GDP of the building construction industry.

[0135] In some embodiments, the correlation analysis unit (201) may predict the total permit performance, total construction start performance, and the expected total floor area for construction start during the target period by classifying them by building type, structure, and use. For example, the expected total floor area for construction start may be the sum of the detailed floor areas corresponding to each of the multiple building types.

[0136] The second forecasting unit (2032) can predict the demand for monthly material volumes for the remaining second floor area, excluding the sum of the first floor area corresponding to each of the multiple first construction projects, based on the permit performance, construction performance, and the expected total floor area for construction for the target period and multiple detailed schedules (S220).

[0137] The second prediction unit (2032) generates an artificial intelligence model (2021), and the artificial intelligence model (2021) learns a method to predict the required duration and material quantity for each of the multiple processes included in the multiple construction projects from the permit and commencement records of multiple construction projects, and can generate multiple detailed process tables using the learned artificial intelligence model (2021).

[0138] The second forecasting unit (2032) can predict the demand for monthly material quantities from the second construction cost for the target period for the second floor area based on the demand for monthly material quantities indicated by a plurality of detailed schedules.

[0139] Each of the multiple detailed schedules may include the demand for material quantities corresponding to the permit performance and commencement performance in the corresponding building type. Accordingly, the second forecasting unit (2032) extracts a detailed schedule corresponding to the permit performance and commencement performance among the second floor area, total permit performance within the target period, total commencement performance, and estimated total commencement floor area for the target period among the multiple detailed schedules, and can predict the required period and material quantity demand for each of the multiple processes corresponding to the second floor area, the permit performance corresponding to the second floor area, and the commencement performance corresponding to the second floor area using the corresponding detailed schedule. Here, the demand for material quantities can be predicted by classifying them according to the use and structure for which each material is required.

[0140] The second prediction unit (2032) can predict the second total construction cost corresponding to the second floor area based on the second floor area and new construction unit price information, predict the second monthly construction cost for each of the multiple months representing the target period based on the second total construction cost and multiple detailed schedules, and predict the second demand for the monthly material quantity for each of the multiple months representing the target period for the second floor area based on the required period and material quantity demand for each of the multiple processes corresponding to the second floor area (S230).

[0141] Since the new construction unit price information includes new construction unit prices per unit area for multiple building types, regions, structures, and building types, the second prediction unit (2032) can calculate the construction cost for multiple building types, regions, structures, building types, and construction start schedules corresponding to the second total floor area based on the new construction unit price information. The second prediction unit (2032) can derive the detailed total floor area corresponding to each of the multiple building types from the second total floor area, and derive the second total construction cost by summing the values ​​obtained by multiplying each detailed total floor area by the new construction unit price of the building type corresponding to it. Based on the new construction unit price information, the second prediction unit (2032) can derive the second total construction cost by classifying it into multiple building types, regions, structures, types of work, and construction start schedules. Each of the multiple detailed schedules may include the time required for multiple processes in the corresponding building type.

[0142] The second forecasting unit (2032) can predict the second monthly construction cost by summing the monthly construction costs corresponding to each of the multiple months represented by the target period in the second total construction cost. The second total construction cost can be divided by month based on the time required for each process indicated by multiple detailed schedules. The second total construction cost can be divided into multiple second detailed construction costs, such as by multiple building types, regions, structures, types of work, and start dates. The second forecasting unit (2032) can divide the multiple second detailed construction costs by month by matching them with the time required for each process indicated by multiple detailed schedules.

[0143] The second forecasting unit (2032) can predict the second demand for monthly material volume by summing the demand for monthly material volume of the project corresponding to each of the multiple months represented by the target period from the demand for monthly material volume predicted in step S220.

[0144] Following steps S150 and S230, the summing unit (2033) can calculate the total monthly construction cost corresponding to each of the multiple months by adding the first monthly construction cost predicted by the first prediction unit (2031) and the second monthly construction cost predicted by the second prediction unit (2032). Additionally, the summing unit (2033) can calculate the total monthly demand for material volume corresponding to each of the multiple months by adding the first demand for monthly material volume predicted by the first prediction unit (2031) and the second demand for monthly material volume predicted by the second prediction unit (2032) (S310).

[0145] The summing unit (2033) can predict monthly construction materials and construction by region and by construction type based on the total monthly construction cost and total demand for monthly material volume calculated in step S310 and the construction cost by construction type estimated in step S130 (S320).

[0146] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0147] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0148] Although embodiments of the present invention have been described above, the present invention is not limited to the above embodiments. Various modifications may be made within the scope of the detailed description of the invention and the attached drawings, provided that such modifications do not depart from the spirit of the invention and do not impair its effects. Furthermore, it is obvious that such embodiments fall within the scope of the present invention. Explanation of the symbols

[0150] 1 : Construction Material Demand Forecasting System 100: Memory 110: Database 200: Processor 201: Correlation Analysis Department 202: Detailed Process Schedule Determination Department 2021: Artificial Intelligence Model 203: Prediction Department 2031: 1st Prediction Department 2032: The Second Prediction Department 2033: Consolidation 300: Collection Module 310: Public data provision server 311: Building Construction GDP Information 320: Architectural data provision server 321: Permit Information and Construction Start Record Information 330: Real estate data provision server 331: New Construction Unit Price Information 340: Input section 341: Standard Schedule 400: User Interface Section

Claims

Claim 1 A construction material demand forecasting system comprising: a memory storing a construction material demand forecasting program; a processor that executes the construction material demand forecasting program to forecast the demand for construction materials for a target period; and a collection module that collects building construction industry GDP, building / housing permit information, building / housing commencement performance information, new construction unit price information, and a plurality of standard schedules for a base period, wherein the processor calculates the expected total floor area for construction commencement for a predetermined target period after the base period based on the building construction industry GDP, the permit information, and the commencement performance information, forecasts the total monthly construction cost for each of the plurality of months represented by the target period based on the new construction unit price information, forecasts the total monthly demand for material quantities for each of the plurality of months based on the plurality of standard schedules, and wherein the plurality of standard schedules include the required period for each of the plurality of processes and the demand for multiple material quantities for each of the plurality of processes corresponding to a plurality of construction types. Claim 2 A construction material demand forecasting system according to claim 1, wherein the processor further comprises a first forecasting unit that derives a first gross floor area corresponding to each of a plurality of first construction projects that have commenced construction based on a reference point after the base period from the commencement performance information, estimates the total construction cost of each of the plurality of first construction projects based on the first gross floor area corresponding to each and the new construction unit price information, and forecasts the first monthly construction cost and the first demand for the monthly material quantity for each of the plurality of months based on the total construction cost and the time required for each process and the demand for the monthly material quantity indicated by the plurality of standard schedules. Claim 3 A construction material demand forecasting system according to paragraph 2, wherein the processor includes a correlation analysis unit that analyzes a first correlation between the building construction industry GDP and the licensing information and a second correlation between the building construction industry GDP and the construction start performance information, predicts an estimated value of the building construction industry GDP for the target period, and predicts a total floor area for construction start corresponding to the estimated value of the building construction industry GDP for the target period based on the first correlation and the second correlation. Claim 4 In paragraph 3, the processor further comprises a second forecasting unit that calculates a second floor area by subtracting the sum of the first floor areas corresponding to each of the plurality of first construction projects from the total floor area to be started, and forecasts the demand for monthly material quantities for the target period for the second floor area based on the second floor area and a plurality of detailed schedules, wherein the plurality of detailed schedules correspond to a plurality of construction types and include the required period for each of the plurality of processes and the demand for a plurality of material quantities for each of the plurality of processes, a construction material demand forecasting system. Claim 5 In paragraph 4, the second prediction unit generates an artificial intelligence model to learn a method for predicting the approval date for use of each of a plurality of construction projects, and uses the learned artificial intelligence model to generate the plurality of detailed schedules corresponding to the second floor area, a construction material demand forecasting system. Claim 6 A construction material demand forecasting system according to claim 4, wherein the second forecasting unit forecasts a second total construction cost corresponding to the second total floor area based on the second total floor area and the new construction unit price information, forecasts a second monthly construction cost for each of the plurality of months based on the second total construction cost and the plurality of detailed schedules, and forecasts a second demand for the monthly material quantity based on the demand for the monthly material quantity for the target period corresponding to the second total floor area. Claim 7 A construction material demand forecasting system according to claim 6, wherein the processor further comprises an aggregation unit that calculates a total monthly construction cost corresponding to each of the plurality of months by summing the first monthly construction cost and the second monthly construction cost for each of the plurality of months, and calculates a total monthly material quantity by summing the first demand and the second demand for the monthly material quantity. Claim 8 In claim 7, the aggregation unit predicts the monthly construction cost, the monthly construction cost per construction type, and the monthly material quantity demand for each of the plurality of regions and each of the plurality of construction types represented by the plurality of construction projects, based on the total monthly construction cost and the total demand for the monthly material quantity, in a construction material demand forecasting system. Claim 9 A method for predicting the demand for multiple construction materials for a target period by executing a construction material demand prediction program stored in memory by a processor, comprising: a step of collecting building construction industry GDP, building / housing permit information, building / housing start performance, new construction unit price information, and multiple standard schedules for a base period; a step of calculating the expected total floor area of ​​construction for a predetermined target period after the base period based on the building construction industry GDP, the permit information, and the start performance information; a step of predicting the total monthly construction cost for each of the multiple months represented by the target period based on the new construction unit price information; and a step of predicting the total monthly demand for material quantities for each of the multiple months based on the multiple standard schedules, wherein the multiple standard schedules include the required period for each of the multiple processes and the demand for multiple material quantities for each of the multiple processes corresponding to multiple construction types. Claim 10 A method for predicting demand for construction materials according to claim 9, further comprising: a step of deriving a first gross floor area corresponding to each of a plurality of first construction projects that have commenced construction based on a reference point after the base period from the above commencement performance information; a step of estimating the total construction cost of each of the plurality of first construction projects based on the first gross floor area corresponding to each and the above new construction unit price information; and a step of predicting the first monthly construction cost and the first demand for the monthly material quantity for each of the plurality of months based on the total construction cost and the time required for each process and the demand for the monthly material quantity indicated by the plurality of standard schedules. Claim 11 A method for predicting demand for construction materials according to claim 10, wherein the step of calculating the expected total floor area for construction starts for the above target period comprises: a step of analyzing a first correlation between the building construction industry GDP and the permit information; a step of analyzing a second correlation between the building construction industry GDP and the construction start performance information; and a step of predicting an expected value of the building construction industry GDP for the above target period, and predicting a total floor area for construction starts for the above target period corresponding to the expected value of the building construction industry GDP based on the first correlation and the second correlation. Claim 12 A method for predicting demand for construction materials according to claim 11, further comprising: a step of calculating a second floor area by subtracting the sum of the first floor areas corresponding to each of the plurality of first construction projects from the total floor area at commencement; and a step of predicting the demand for monthly material quantities for the target period for the second floor area based on the second floor area and a plurality of detailed schedules, wherein the plurality of detailed schedules correspond to a plurality of construction types and include the required period for each of the plurality of processes and the demand for a plurality of material quantities for each of the plurality of processes. Claim 13 A method for predicting construction material demand according to claim 12, further comprising: a step of learning how an artificial intelligence model predicts the approval date for use of each of a plurality of construction projects; and a step of generating a plurality of detailed schedules corresponding to the second gross floor area using the learned artificial intelligence model. Claim 14 A method for predicting construction material demand according to claim 13, further comprising: a step of predicting a second total construction cost corresponding to the second floor area based on the second floor area and the new construction unit price information; a step of predicting a second monthly construction cost for each of the plurality of months based on the second total construction cost and the plurality of detailed schedules; and a step of predicting a second demand for the monthly material quantity based on the demand for the monthly material quantity for the target period corresponding to the second floor area. Claim 15 delete Claim 16 delete Claim 17 delete

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