KR101815700B1 - System of analyzing commercial properties based on timely accumulated data - Google Patents

Abstract

The present invention relates to a forecast data system for analyzing commercial real estate based on temporal accumulation data. According to an embodiment of the present invention, the forecast data system comprises: a central control unit to receive information about a first area and first category information from a client, and transmit forecast data to the client; a sales change prediction unit to generate forecast data including sales change values of one or more shopping areas included in the first category information in the first area; an intra-category prediction unit to generate forecast data including sales change values of shopping areas in the first category in accordance with change values of the shopping areas included in the first category information in the first area; and an inter-category prediction unit to generate forecast data including the sales change values of the shopping areas in the first category in accordance with change values of shopping areas included in second category information which is a category to be compared in the first area.

Description

[0001] System for analyzing commercial real estate based on temporal cumulative data [0002]

The present invention relates to a system for analyzing commercial real estate based on temporal cumulative data.

As the information processing is based on the Internet and computers, various commercial information is processed and provided to the Internet. Traditionally, home appliances, furniture, and books were mainly used for commerce through the Internet. Recently, real estate transactions have also provided basic information through the Internet.

On the other hand, real estate can be classified as residential real estate and commercial real estate. The residential real estate is usually based on the standard price, and the information needed to estimate the price of residential real estate was limited. Therefore, information on residential real estate can be easily collected, and such information could easily be provided through the Internet. Commercial real estate, on the other hand, must consider a wide variety of factors in the calculation of prices, and it was also difficult to search for suitable commercial real estate. This is due to the inherent characteristics of commercial real estate.

Thus, there is a growing demand for methods and systems for searching and analyzing commercial real estate based on the unique characteristics of commercial real estate.

In the present specification, forecast data on commercial real estate is provided so that commercial real estate can be selected according to scientific prediction results.

Further, in the present specification, the search and analysis result of the commercial real estate is provided based on the category including the commercial real estate.

The objects of the present invention are not limited to the above-mentioned objects, and other objects and advantages of the present invention which are not mentioned can be understood by the following description and more clearly understood by the embodiments of the present invention. It will also be readily apparent that the objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.

According to an aspect of the present invention, there is provided a forecast data system for analyzing commercial real estate based on temporal cumulative data according to an embodiment of the present invention includes: A sales fluctuation predicting unit for generating forecast data including a sales change value of one or more commercials included in the first category information in the first region, An intra-category predictor for generating forcast data including a sales change value of the malls in the first category according to a change value of the malls included in the first category information, 2 category information according to the change value of the commercial value included in the first category And an inter-category predicting unit for generating forecast data including a sales change value of the salesperson.

According to an aspect of the present invention, there is provided a forecast data system for analyzing commercial real estate based on temporal cumulative data according to an embodiment of the present invention includes: A center control unit for transmitting the cast data to the client, a mall variation predictor for generating the forcast data including the increase / decrease change value of at least one mall included in the first category information in the first area, Category prediction unit for generating the forcast data of the malls included in the first category and the forecast data of the malls in the first category according to the increase / decrease value of the mall included in the second category information as the comparison target category in the first area And an inter-category prediction unit for generating the inter-category prediction unit.

When one embodiment of the present invention is applied, forecast data on commercial real estate can be provided to select commercial real estate according to scientific prediction results.

In addition, when another embodiment of the present invention is applied, search and analysis results of commercial real estate can be calculated based on categories including commercial real estate.

The effects of the present invention are not limited to the effects described above, and those skilled in the art of the present invention can easily derive the various effects of the present invention in the constitution of the present invention.

FIG. 1 is a diagram illustrating a process of collecting row data by a low data system according to an embodiment of the present invention. Referring to FIG.
FIG. 2 is a diagram illustrating a configuration of a database for storing row data in the row data system 100 according to an embodiment of the present invention.
FIG. 3 is a view showing a screen for listing information on a shopping mall when a search condition is given, such as belonging to a specific category or setting a specific amount on a map according to an embodiment of the present invention.
FIG. 4 is a diagram showing an association between a secondary data system 400 according to an embodiment of the present invention and a database of raw data necessary for accumulating secondary data.
FIG. 5 is a diagram illustrating a process of generating secondary data for commercials within a certain range according to a request for generating secondary data according to an embodiment of the present invention and providing the secondary data.
6 is a diagram illustrating a configuration of a forecast data system according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating a process of calculating sales fluctuation and forecast data according to an embodiment of the present invention.
FIG. 8 is a view showing a screen on which sales forecast data including estimated sales of selected shopping districts in a specific area according to an embodiment of the present invention is output.
9 is a diagram illustrating a process of confirming the influence of sales increase in a category in the intra-category predicting unit according to an embodiment of the present invention.
FIG. 10 is a diagram showing the influence of sales increase within a category of an intra-category prediction unit within a specific area according to an embodiment of the present invention, according to an embodiment of the present invention.
FIG. 11 is a diagram illustrating a process of confirming the influence of sales increase between different categories in the inter-category predicting unit according to an embodiment of the present invention.
FIG. 12 is a diagram showing the effect of the increase in sales among different categories of the inter-category predicting unit within a specific area according to an embodiment of the present invention, according to an embodiment of the present invention.
13 is a diagram illustrating a configuration of a forecast data system according to another embodiment of the present invention.
FIG. 14 is a diagram illustrating a process of confirming the influence of the change information of the commercial value in the category in the intra-category predicting unit according to another embodiment of the present invention.
FIG. 15 is a diagram showing the effect of intra-category prediction within a specific region according to an embodiment of the present invention, according to variation of malls within a category.
16 is a diagram illustrating a process of confirming the influence of the increase in sales among different categories in the inter-category predicting unit according to an embodiment of the present invention.
17 is a diagram showing the influence between different categories of the inter-category predicting unit within a specific region and a specific period according to another embodiment of the present invention.

Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that, in adding reference numerals to the constituent elements of the drawings, the same constituent elements are denoted by the same reference symbols as possible even if they are shown in different drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In describing the components of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. When a component is described as being "connected", "coupled", or "connected" to another component, the component may be directly connected to or connected to the other component, It should be understood that an element may be "connected," "coupled," or "connected."

In this specification, information possessed by commercial real estate includes raw data collected from the outside, secondary data calculated by converting low data or applying a specific filter, and temporal data of secondary data And forecast data including forecast information about the commercial real estate or the area where the commercial real estate is located or the business related to the commercial real estate.

A system collecting and storing raw data is referred to herein as a row data system. A system for generating and storing secondary data using low data and other additional data is referred to as a secondary data system.

On the other hand, a system that generates forecast data using raw data, secondary data, and other data is called a forecast data system.

The commercial valuation analysis system according to an embodiment of the present invention may include all of the above-described low data system, secondary data system, and forcast data system. In addition, the shopping mall analysis system according to another embodiment of the present invention may include the above-described secondary data system and forcast data system. In addition, the system for analyzing a commercial value according to another embodiment of the present invention may include the forcast data system described above.

An example of low data includes visual information such as a moving image or an image of peripheral information of an actual shopping street or a mall. It also includes financial information needed to trade, lease, and lease a store, in another embodiment of raw data. Hereinafter, the row data based on the visual information is referred to as image data. Also, raw data based on information such as a sale or lease is referred to as transaction data. The image data according to one embodiment of the present invention means storing the exterior of a shopping mall or the surroundings of a shopping mall in the form of a moving picture or a still picture. This image data may be displayed in combination with information related to another mall, or may be provided as information of all mall.

A method for acquiring image data is a method of using an external database for storing images by inputting images through various information sources, or by using a load view device.

The transaction data can be entered into a row data system by the party or his agent who buys or sells the shop. In addition, the transaction data may be entered into the row data system by the party currently operating the store or its agent.

Since the row data can be continuously varied, the row data system can determine the update period according to the characteristics of the row data. The image data has a short update period in a region where the change rate of the commercial value is high or in a region where the variation period is short, and the update period can be long in the region where the variation rate of the commercial value is low or the variation period is long. On the other hand, the transaction data can be updated only in real time, or in case of a change, since the update point may be different for each market value. The row data system 100 of FIG. 1 may be operated so that the row data can be updated sufficiently accurately according to the characteristics of the fluctuation period of the shop price.

FIG. 1 is a diagram illustrating a process of collecting row data by a low data system according to an embodiment of the present invention. Referring to FIG.

The row data system 100 receives image data or transaction data at a plurality of input terminals 101a, 101b, 101c, and 101d. The image input terminals 101a and 101b store moving images or image files taken by the load view device and the image input terminal 101a stores the stored files in a batch format or in a real- (100). The image data may include information identifying a street value, identifying a location, or identifying an address. Alternatively, row data such as category, moving picture information, and image information may be included in combination with the image data.

In addition, the row data system 100 can accurately store the data provided by the image input terminals 101a and 101b by combining the image information. In one embodiment, if the moving image or image file includes only latitude and longitude information, the row data system 100 automatically compares the image or moving image file of a number of shops corresponding to the latitude and longitude, Can be converted and stored.

Alternatively, the row data system 100 may receive the provided image data through the various input terminals 101a and 101b, and may compare the image data of a specific mall or specific position among the received image data, Only a part of them can be selected and stored. To this end, the row data system 100 may include a database for storing files.

On the other hand, the row data system 100 can also receive transaction data from the various input terminals 101c and 101d, and when a plurality of different transaction data is received for the same commercial value, It is possible to select the optimal transaction data based on the reliability given to the user. In addition, when the transaction data provided by the plurality of input terminals 101c and 101d is different, the row data system 100 may store the transaction data together with the identification information on the input terminals 101c and 101d. The input terminals 101c and 101d may be identification information of a broker who is in charge of a transaction or may be identification information of a landlord, a tenant, a seller, and the like. In addition, identification information of the agents responsible for the start-up consulting business may be included.

FIG. 2 is a diagram illustrating a configuration of a database for storing row data in the row data system 100 according to an embodiment of the present invention. 210 is a diagram showing a configuration of Store_DB showing information related to the physical location and industry of a shopping mall. The ID is an identification number indicating the commercial value. One identification information may be assigned to one store, and a building may include a plurality of stores. Addr1 means the address information where the mall is located. Addr2 includes information such as a floor or a lake that can identify the shop in a building when a plurality of shops are arranged in one building. In addition to Addr1 and Addr2, address related fields such as Addr3, Addr4, etc. can be extended in consideration of the geographical characteristics of the mall.

In addition to the above address information, absolute information such as latitude and longitude can also be stored in the Store_DB to combine with the map information. Space is information about the space occupied by the mall. But it is also possible to include information on height (height). Space can be subdivided to provide fields with Space1, Space2, Space3, and can be configured to indicate the width, height, and height, respectively.

Category means the type of business currently operating in the shop. Category can be divided into Top_Category, Middle_Category, and Precise_Category to represent business type. Also, when it is difficult to specify one category, it can be categorized in duplicate. For example, in case of a multifamily shopping mall that provides various services, a plurality of categories may be stored in duplicate so as to cover all the features of the service provided by the mall. In one embodiment, both the coffee shop category and the bakery category can be stored for a mall where the coffee shop and the bakery are combined.

Such as Category1, Category2, etc., as 210a to cover these two or more categories. Likewise, the subdivided categories may also be composed of Top_Category1, Top_Category2, Middle_Category1, Middle_Category2, Precise_Category1, Precise_Category2, etc. as shown in 210b. That is, the fields associated with the category are expandable.

Within the category, a french name can also be set. For example, Top_Category may be a coffee shop, and Middle_Category may include a specific coffee shop franchise. In this case, it is possible to retrieve the commercial value of the franchise.

220 shows the configuration of Price_DB, which is a database showing the monetary information required for the lease or sale of the mall. The ID indicates the identification information of the shop. The same information as the ID of the Store_DB 210 can be used. Type is the type of property. And whether or not the selling or leasing of the mall is carried out. Price includes information on the sale price of the shop or the rental deposit. Rent means rent. This can vary depending on the type. If Rent is the sale of the house, Rent can mean the current monthly rent. On the other hand, if Type is a lease of a commercial area, Rent may mean the rent amount that the landlord wants to receive. In addition, the item Premium can include information on the price of the shopping center.

Source is the information about the entity that uploaded information such as sell / lease to the shop. The source includes information about whether the owner of the mall, the current tenant is an active tenant, a broker, or a startup consultant. Like 220a, the Source can be subdivided again into Source_Type and Source_ID.

Depending on the source, different information can be uploaded through multiple information providers for the same merchant. In this case, priority can be given to the information. In one embodiment, the information uploaded by the shop owner can have priority. This can be indicated in the process of searching for the shopping information, or the Priority field can be set separately to set the priority of the corresponding price information. It is also possible to prioritize information uploaded by a particular information provider, for example a broker or a startup consultant, in the system by placing a separate priority.

230 indicates Map_DB associated with the map data. Map_DB can be composed of multiple images, and different images can be placed for the same terrain depending on the scale of the map. The Map_DB includes Position, which is information on the position, a scale RatioGrade centered on the position, and an Image field, which is a map image file in the corresponding RatioGrade. Clients such as external mobile devices, laptops, etc.

Reference numeral 240 denotes a RoadView_DB including road view image information coupled to the map data. RoadView_DB has one or more image information combined at a specific location, for example, at the center of latitude / longitude, or at a specific distance. In the RoadView_DB, the same image information can be enlarged or reduced as the user enlarges or reduces the load view image, or the image information photographed differently or more precisely photographed can be arranged according to the enlargement or reduction magnification. For this purpose, the RoadView_DB stores information related to the position, Direction indicating the direction taken at the position, the position and direction, and the image according to the magnification when the image is different, and information about the magnification is stored In the RatioGrade field, the image is stored in the Image field. For default images, a default value can be set for RatioGrade.

Based on the configuration of the database storing the row data in the row data system 100 as shown in FIG. 2, the client device can retrieve information about the mall belonging to a specific category. In particular, there is a location in the Store_DB 210 or 210a, which can be interlocked with the Map_DB 230, so that shopping malls belonging to a specific category can be searched on the displayed map.

FIG. 3 is a view showing a screen for listing information on a shopping mall when a search condition is given, such as belonging to a specific category or setting a specific amount on a map according to an embodiment of the present invention. The screen shown in Fig. 3 can be installed in a mobile device, a notebook computer, a computer, or the like, or can be confirmed on a web site providing the service. A plurality of buildings are arranged around the road 301 at a map 320. In Map_DB and Store_DB, the map image and the information of the shops in upper left coordinates and lower right coordinates are retrieved and displayed.

310 is a configuration for selecting a type of a mall to be displayed on the map. We chose coffee shop as Top_Category, and selected specific coffee shop franchise and personal shop as sub category Middle_Category. The selected result appears on the 320 map so that the same pattern is displayed on the building. The display method can be variously implemented.

In addition, you can search according to the size of the commercial house rent, or according to the right money. The search condition can be selected variously according to the configuration of the information provided by Store_DB.

When the user clicks on the specific shopping street shown in FIG. 3, the detailed information of the shopping street, such as the rent, the right money or the sales information extracted from the secondary data to be described later, can be displayed.

In FIGS. 1 to 3, configurations of row data or a method of representing row data have been described. The row data is primary data inputted from the outside, and the technical features such as adjusting the physical location thereof or adjusting the financial information related to the lease constitute the low data system 100.

On the other hand, in the configuration of Fig. 3 in which the row data is displayed, secondary data, which is secondary information about a specific commercial value, sales of a specific category, or a specific region, can be generated. Secondary data can be generated at various points in time, that is, from past to present. A configuration of a secondary data system for generating secondary data will be described.

FIG. 4 is a diagram showing an association between a secondary data system 400 according to an embodiment of the present invention and a database of raw data necessary for accumulating secondary data.

The store_DB shown in FIG. 2 has information on a specific commercial property. However, this information can vary over time. For example, in the past, the coffee shop was operated in the same shop, but now it is changed to a bakery business type. Such past history information is accumulated and stored in a history store database, for example, History_Store_DB 410. Therefore, in the History_Store_DB 410, information on the time when the corresponding commercial is operated is included in addition to the field of FIG. Also, if there is information on the time when the shopping center has opened (or opened), a field for the opening date (Open) may be further included in 410. [

Store_DB stores information on the time when a shop opened in a shop of various types of businesses opened (opened) and closed (closed) in the same shopping mall. Therefore, when Store_DB is used, it is possible to grasp the fluctuation period of the commercial buildings in the building. If the period of fluctuation of the store price is too short in Store_DB, it can be judged that there is a problem in judging the possibility of commercial operation in the building.

In addition, the secondary data can accumulate and store the sales information in a timely manner. A sales database, for example Sales_DB (420), which is a database of sales information, for example, is input by various entities such as the National Tax Service, credit card company or a franchisee, a franchisee or a tenant who operates a shopping center, a business consultant or a real estate agent. And accumulates and stores information on sales of the malls existing so far.

In the Sales_DB 420, information on the Monthly Sales and the time at which the corresponding sales are confirmed can be stored for each shopping center based on the ID, which is the merchant identification information of the Store_DB shown in FIG. According to an embodiment, there may be a plurality of information providers for one shopping center, and in this case, it can be divided into Source_Type / Source_ID / Priority as shown in 220a of FIG. In addition, since the sales are changed at each point in time, more than one sales information can be recorded according to the time when sales are confirmed (Time).

In addition, the secondary data system 400 may collect the databases 210, ..., 240 stored in the row data shown in FIG. 2 in real time to provide secondary information.

FIG. 5 is a diagram illustrating a process of generating secondary data for commercials within a certain range according to a request for generating secondary data according to an embodiment of the present invention and providing the secondary data.

First, a user requesting a search for the characteristics of the malls in a specific area requests a secondary category for a specific category, for example, a coffee shop, using a mobile device, a notebook computer, a computer, or the like, . A mobile device, a notebook computer, a computer, and the like are hereinafter referred to as clients. There may be a server 10 including both a low data system 100 and a secondary data system 400 for transmitting and receiving information to and from a client. Or the server 10 may serve as an interface and may transfer the information requested by the client to at least one of the low data system 100 and the secondary data system 400 as appropriate. In addition, the server 10 may also provide a function of integrating the information generated by the low data system 100 and the secondary data system 400 together.

The client transmits the information on the limited area and the category information to the server 10 or the row data system 100 (S510). The low data system 100 searches the Store_DBs 210, 210a, and 210b for the information on the merchants and merchants corresponding to the categories included in the received area. The low data system 100 generates information to be sorted and sorted according to the result of the search using the requested information and category information (S520). If the subcategories exist even within the category, information for outputting is generated by classifying the stores according to the subcategories (S520). As shown in FIG. 3, the low data system 100 provides information so that it can be displayed on the screen of the client.

3, the client can collectively select malls corresponding to specific subcategories, or select one mall. Also, the client can select identification information (ID Etc.) to the server 10 or the secondary data system 400 (S530). At this time, it is also possible to selectively transmit the information about the period. For example, information about a period can be included, such as the last three months, or the last one year. Further, it is possible to select and request information of necessary secondary data. In other words, sales information of Sales_DB can be requested, or history_store_DB can be used to check which category has been operated in the past. Also, if more than one mall is selected, for example, if all coffee shops in the map are selected, the sales trend of the coffee shops in the area can be confirmed.

The secondary data system 400 collects secondary data of the received one or more stores and transmits the secondary data to the client through the server 10 or directly to the client (S540). As described above, the secondary data includes sales for a current or past certain period corresponding to one or more merchants or one or more categories, or category of a previously operated business of a particular merchant. In the case of sales, clients can output visual image information such as graphs. As for the sales that were operated in the past shopping mall, it is possible to display the image information about the past shopping mall or the logo in the case of the franchise in a chronological order.

The types of secondary data that can be checked in the process of FIG. 5 are as follows. When a client inputs a particular interest category, a region of interest, or a maximum value of a start-up cost, the secondary data system 400 is presented as a current sale in categories and regions of the industry using a plurality of databases, It is possible to provide the information of the shop that was the sale. In this process, other shops belonging to the same category can also check the rent, deposit, right money, opening day and area of the shopping mall. You can also check the rent, deposit, and money of the commercial real estate, the date of opening, and the size of the area. In addition, the secondary data system 400 also provides information on the distribution of distributions of malls of a specific category among all the malls, in the area having a certain area, for example, the "dong" unit in the administrative district or the neighborhood of the subway station, . Also, when the category is subdivided, a distribution chart can be newly calculated for each subcategory. In one embodiment, the coffee shop may be set as the upper category and the lower category may be set for each franchise by the coffee shop. And you can compare distribution and sales by subcategory.

The secondary data can be generated using the filter / reference / category of the secondary data. In addition, one embodiment of the secondary data may be accumulated and stored in the database in advance. Another embodiment of the secondary data may be generated by the secondary data system 400 in real time or periodically and provided to the client. For example, when the range of sales of the malls within a specific range is received in real time by the client, the secondary data system 400 can calculate and provide sales of the malls included in the range in real time.

1 to 5, the configuration of the raw data input from the outside, the secondary data generated by accumulating or converting the data, and the configuration for displaying the secondary data have been described. Next, we will look at the precast data system, which predicts the information needed to operate the mall, such as forecasts of sales in a particular mall or certain category based on raw data and secondary data, I will look at it.

The forecast data system combines the above-described low data and secondary data to calculate forecast data such as estimated sales amount, estimated fixed cost, and estimated net profit of the corresponding shopping mall. In addition, the forcast data system calculates the surrounding situation and the situation, the degree of competition, and the variation rate of the interested industry transmitted by the client.

On the other hand, the forcast data system can generate forecast data based on secondary data such as the past history and the change rate of interest, the rent, the right money, the sales, the profit and the operation period. Other than that, it is possible to calculate the degree of possibility of peripheral synergy compared with other stores. This is because the number of restaurants increases the sales of coffee shops, and if the forcast data system belongs to a different category, it can analyze the impact on sales to determine the possibility of sales increase.

On the other hand, the forcast data system is able to provide information on promising business types and specification business types by identifying the trends of the merchants that have been opened up to now with secondary data and checking the trend change. When aggregating this information, the forcast data system can provide a success rate of interest.

As described above, the secondary data is obtained by adding time information to static raw data input from the outside. The precast data system shows the change from a specific time boundary, for example from a specific time in the past to the present, or from the first time in the past to the second time in the past. In one embodiment, embodiments that illustrate changes in the distribution or turnover of a mall or a mall included in a particular category within a particular physical area (e.g., a region displayed on a map). In another embodiment, by analyzing the degree of association by category, the cumulative analysis of the increase / decrease of the sales according to the distance between the commercial area belonging to the first category and the commercial area belonging to the second category, The results of the analysis can be shown.

In addition, after generating big data by analyzing words or contents appearing on Internet blogs, social network services (SNS), newspaper articles, and the like, it is possible to check trends of specific categories, specific areas, Can be selected.

6 is a diagram illustrating a configuration of a forecast data system according to an embodiment of the present invention.

6, the low data system 100 and the secondary data system 400 may be included in the precast data system 600. [ In another embodiment, the low data system 100 and the secondary data system 400 are disposed outside the precast data system 600 and the low data system 100 and the secondary data system 400 are communicated through the communication network, And can transmit and receive information to and from the cast data system 600.

More specifically, the forcast data system searches and analyzes commercial real estate based on temporal cumulative data, and for this purpose, a central control unit 690, a sales fluctuation predicting unit 610, an intra-category predicting unit 620, A prediction unit 630, a trend analysis unit 640, and the like.

The sales fluctuation predicting unit 610 generates forecast data based on a variation value of sales in a field requested by the client device, such as a shopping area included in a specific area or a specific shopping area or a specific category.

The intra-category predicting unit 620 analyzes the degree of influence of the sales in the category to generate forecast data.

The inter-category predicting unit 630 analyzes the degree of influence of sales among the categories of the different industries to generate forecast data.

The trend analysis unit 640 collects information on current or past trends, and generates forcast data on promising business categories or prospective categories. For this, the trend analyzing unit 640 may include a database including trend analysis results.

The central control unit 690 controls the above-described sales fluctuation predicting unit 610, the intra-category predicting unit 620, the inter-category predicting unit 630, the trend analyzing unit 640, The central control unit 690 transmits the data stored or generated in the low data system 100 and the secondary data system 400 to the other components 610, 620, 630, 640).

In summary, the central control unit 690 receives the first area information and the first category information from the client, and forwards the forecast data to the client.

Then, the sales fluctuation predicting unit 610 generates forecast data including the sales change value of one or more shops included in the first category information in the above-mentioned first region.

On the other hand, the intra-category predicting unit 620 generates forecast data including a sales change value of the malls in the first category according to the change value of the malls included in the first category information in the first area.

The inter-category predicting unit 630 generates forecast data including a sales change value of the malls in the first category according to the change value of the malls included in the second category information as the comparison target category in the first area.

The manner and process by which each of these components generate forecast data will now be described in more detail.

The secondary data system 400 provides secondary data necessary for generating forcast data. As shown in FIG. 4, the history store database 410 accumulates and accumulates the changes in the commercial price in a timely manner. The sales database 420 accumulates and stores the sales information of the mall in a timely manner. In addition, the secondary data system 400 is included in the forcast data system 600 as an embodiment. In another embodiment, the secondary data system 400 can communicate with the forecast data system 600 via communication.

FIG. 7 is a diagram illustrating a process of calculating sales fluctuation and forecast data according to an embodiment of the present invention.

The sales fluctuation predicting unit 610 confirms information on the target merchant's information (for example, the IDs of the merchants) and the starting and ending time information transmitted by the client (S710). Then, the sales secondary data is extracted from the Sales_DB 420 including the secondary data using the confirmed information (S720). At this time, a plurality of sales secondary data may exist between the start point and the end point. Also, there may be sales secondary data uploaded by a plurality of information providers even at the same time. Therefore, the sales fluctuation predicting unit 610 applies the priority of the information provider to the sales secondary data to correct it. In one embodiment, if the shop operator has sales of 12 million won at a certain point in time, the revenue reported to the Internal Revenue Service is 8 million won, and the tax return rate of the category including the shop is 80% The value of the sales secondary data can be corrected with a full charge.

Then, the central control unit 690 collects the corrected sales secondary data and transmits it to the client (S740). At this time, the central control unit 690 may also transmit the forcast data generated by the intra-category predicting unit 620, the inter-category predicting unit 630, and the trend analyzing unit 640 to the client.

On the other hand, two or more malls may represent sales by each mall, but the results can be presented as sales relative to the area occupied by each mall. In this case, the areas of the Store_DBs 210, 210a, and 210b of the respective malls can be collected, and the sales can be presented based on the area. In another embodiment, in predicting the sales of a particular shopping center, the sales of nearby shopping malls can be confirmed.

FIG. 8 is a view showing a screen on which sales forecast data including estimated sales of selected shopping districts in a specific area according to an embodiment of the present invention is output.

The client selects a specific area, such as 820. This can be selected using zoom in / zoom out. Or 810, or select the Middle_Category of interest. And a result obtained by the sales fluctuation predicting unit 610 using the data stored in the Sales_DB 420 based on the unit area (for example, 3.3 m ² ) of the store as the selected result 830. In particular, forecasted sales are displayed for each Middle_Category, which can be calculated by extracting sales secondary data for the period from 2014Q (4Q) to 2016.3Q (3Q). For example, there was an increase-stagnation-increase in coffee beans. At this time, 2016.4Q can be selected as increase or stagnation. In one embodiment, when the trend analysis unit 640 has recently increased the search term for coffee beans, the sales forecast data can be generated due to an increase in sales.

In summary, if the client provides the information on the starting point and the ending point of a specific area, the specific commercial districts, or the malls belonging to a specific category, the sales fluctuation predicting unit 610 of the forecast system 600 calculates The information on sales is calculated for each viewpoint, and the sales information is corrected by applying priorities or accuracy to each information provider, and the corrected results are collected and provided as sales forecast data. In this case, when there are a plurality of malls, the sales area variation estimator 610 of the forecast system 600 may convert and provide the sales forecast data based on the area because the area may differ for each shopping area. Whether the sales increase or decrease and the width thereof can reflect the increase / decrease of accumulated sales in the past and the trend results related to the category provided by the trend analysis unit 640. Trend results can also include macroeconomics, for example, if a large number of favorable articles on a particular category of business prospects are searched, forecast data can be generated by predicting revenue growth.

Also, although not shown in FIG. 8, the client can define a specific information provider. For example, the category including the franchise may be set to reflect the sales information received from the franchise headquarters. You can also choose to exclude the information uploaded by merchants and reflect the revenue data generated by the Internal Revenue Service, or a trustworthy startup consultant, only in generating sales forecast data.

In FIGS. 7 and 8, the central control unit 690 determines that the sales information at the predetermined time based on the identification information of the first category information, for example, one or more commercials included in the coffee shop, in the first region, The secondary data including the sales information can be extracted from the cumulative stored database (e.g., Sales_DB 420).

When the secondary data extracted by the central control unit 690 is increased to a predetermined reference or more, the sales fluctuation predicting unit 610 generates forecast data for determining the sales fluctuation predicting as an increase, Can be calculated. Here, the case where the increase is greater than a preset reference means that the sales increase by quarter is higher than the increase of the previous quarter as one embodiment.

The sales fluctuation predicting unit 610 may calculate the sales change value by determining the increase range of sales for the next quarter or next month by using the secondary data extracted by the central control unit 690. [

9 is a diagram illustrating a process of confirming the influence of sales increase in a category in the intra-category predicting unit according to an embodiment of the present invention.

In a category, for example, when a Top_Category is set as a coffee shop and a specific area is set, the size in which the shop price increases or decreases in the corresponding Top_Category is compared with the increase or decrease in the sales of coffee shops in the corresponding area, It is possible to determine whether opening a corresponding shopping mall is profit or loss of sales.

To this end, the intra-category predicting unit 620 confirms information for selecting the target area and Top_Category (S910). In step S920, the intra-category predicting unit 620 restricts the intra-category prediction unit 620 to a predetermined analysis period (for example, three years or five years) and extracts the shopping areas corresponding to the Top_Category in the target area from the History_Store_DB 410.

In addition, the intra-category predicting unit 620 calculates sales secondary data in the Sales_DB 420 for the extracted commercials (S930). In the process of calculating sales secondary data, it can be selectively selected by reflecting the priority, accuracy, and reliability of the information provider. In addition, the forecast data system 600 transmits the data to the client through a process of correcting the sales secondary data to improve the accuracy (S940).

The intra-category predicting unit 620 calculates sales based on the area of the commercial areas operated by each period, for example, monthly, quarterly, yearly, or a preset period using the sales secondary data, 10, as shown in FIG. The intra-category predicting unit 620 may include at least one of various information such as increase / decrease of sales by store size, increase / decrease of sales by each store, increase / decrease of a floating population, and the like, have.

FIG. 10 is a diagram showing the influence of sales increase within a category of an intra-category prediction unit within a specific area according to an embodiment of the present invention, according to an embodiment of the present invention.

1010 shows the increase or decrease in total sales of the mall included in that category within a particular geographical area from October 2014 to October 2016. It can be seen that sales have increased at points indicated by 1011 and 1012. When the corresponding region is clicked, the intra-category predicting unit 620 or the forecast data system 600 including the intra-category predicting unit 620 can display a change state in the corresponding category in the corresponding area at the corresponding point in a pop-up form. For example, if you click on the square of 1011, you can indicate that there was an open coffee shop within that area. 1010 is a graph showing whether sales of the category are increased due to the opening of a specific shopping mall.

1020 shows the increase or decrease in sales relative to the area of the merchants in that category within a particular geographical area from October 2014 to October 2016. 1021 shows the temporal area corresponding to 1011 and the sales increase situation in the following two months. 1021, the change situation in the corresponding category in the corresponding area at that time can be displayed in a pop-up form. In addition to the changes shown in 1011 above, the changes between June and August 2015 can also be displayed in a pop-up form. More specifically, it can show at which point a shop has opened or how the number of shops has increased or decreased.

In one embodiment, FIG. 10 may be a graph showing the sales of a coffee shop in a geographical area centered on Gangnam Station. In this case, the user can click a window-shaped cursor at a specific point in time 1011, 1012, 1021, 1022 or at specific points in time. In this case, the forcast data system 600 can display the information of the townships where the fluctuation occurred at the time point using the low data, the secondary data, or the forecast data. In addition, it is also possible to display the rents of buildings or new buildings of the shops where the change occurred at that time. It can also reflect the increase or decrease in the number of shops and new construction situations.

As shown in FIG. 10, when a shopping mall corresponding to a corresponding coffee shop is opened in 1011 and 1021, or in 1012 and 1022, thereby increasing the sales of the entire coffee shop category, It can be predicted that the establishment of a coffee shop in the area can be synergistic due to the nearby coffee shops. The forcast data system 600 can increase the success probability of opening a specific category of a specific area by using the raw data including the history of the accumulated merchants in the area and the secondary data and the forcast data calculated therefrom, If the area-to-area sales increase as the area increases, the intra-category prediction unit 620 may determine that the success rate of the shopping mall belonging to the category is high and provide information on the success rate.

9 and 10 are summarized as follows.

The central control unit 690 accumulates the sales information of the predetermined time based on the identification information of the shopping areas corresponding to the first category information in the first area, that is, the specific area, for example, the coffee shop, The first secondary data including the sales information can be extracted from the stored database. It can use the Sales_DB 420.

Also, the central control unit 690 can extract second secondary data related to the opening or closing of business based on the identification information of the malls. It can use the History_Store_DB 410.

The intra-category predicting unit 620 uses the first secondary data and the second secondary data to determine whether the sale of the shopping area corresponding to the first category information in the first area It is possible to generate forcast data that judges success probability as success.

As shown in FIG. 10, the criterion for increasing the sales by store area may correspond to the previously set criteria even though the new shopping center has entered.

On the other hand, FIG. 9 and FIG. 10 illustrate an embodiment in which the increase or decrease of the commercial value within the same category of the same region affects sales. Next, an example in which the inter-category predicting unit 630 affects the sales of the increase or decrease of the commercial value between different categories will be described.

FIG. 11 is a diagram illustrating a process of confirming the influence of sales increase between different categories in the inter-category predicting unit according to an embodiment of the present invention.

When a different category, for example, Top_Category, is set as a coffee shop and a different Inter_Category is set as a restaurant, the size of the increase or decrease of the price of Inter_Category in a predetermined area and the size of increase or decrease of sales, The size of the increase or decrease of the sales price in the Top_Category corresponding to the region where the sales increase or decrease is compared to find out whether opening the shopping center corresponding to the Top_Category in the region having a large number of Inter_Category is profit or loss of sales .

To this end, the inter-category predicting unit 630 confirms information for selecting the target area, Inter_Category, and Top_Category (S1110). The inter-category predicting unit 630 restricts the inter-category predicting unit 630 to a predetermined analysis period (for example, 3 years or 5 years) and stores in the History_Store_DB 410 the commercials corresponding to the Inter_Category and the commercials corresponding to the Top_Category (S1120).

Also, the inter-category predicting unit 630 calculates sales secondary data of the shopping malls extracted in S1120 from the Sales_DB 420 for the extracted shopping malls (S1130). In the process of calculating sales secondary data, it can be selectively selected by reflecting the priority, accuracy, and reliability of the information provider.

The inter-category predicting unit 630 may calculate the sales of each shop based on each period, for example, monthly or quarterly using the sales secondary data. In addition, the forecast data system 600 transmits the data to the client through a process of correcting the sales secondary data to improve the accuracy (S1140).

In summary, according to an embodiment of the forecast data described above, sales are presented in terms of area, but according to an embodiment of the present invention, sales of malls belonging to Top_Category operated on a monthly or quarterly basis, The flow population can be calculated as additional precast data.

FIG. 12 is a diagram showing the effect of the increase in sales among different categories of the inter-category predicting unit within a specific area according to an embodiment of the present invention, according to an embodiment of the present invention.

1210 shows the increase / decrease of sales by area of stores in Inter_Category within a specific geographical area from December 2014 to December 2016. 1211 and 1212, the sales by store size are increased. When the corresponding region is clicked, the change state of Inter_Category within the corresponding region at the corresponding time point can be displayed in a pop-up form in the inter-category predicting unit 620 or the forecast data system 600 including the inter-category predicting unit 620. For example, if you click on the square of 1211, you can indicate that there was a restaurant open in that area for that period. Or the sales of restaurants specific to that period within the area of the region may increase. 1212 also applies.

1220 shows the increase or decrease in sales relative to the area of the malls included in the Top_Category within a specific geographical area from December 2014 to December 2016. 1221 shows the temporal area corresponding to 1211 and the sales increase situation in the following two months. 1221, the change status in the corresponding category (Top_Category) in the corresponding region can be displayed in a pop-up form. It is possible to consider the situation such as the increase of the sales by the store size or the increase of the sales of the specific shopping area newly opened in the shopping area included in the Top_Category. 1222 also applies.

In FIG. 12, when the sales by store area of Inter_Category and the sales by store area of Top_Category are compared, a similar rising pattern can be confirmed.

In one embodiment, FIG. 12 can be a graph showing the correlation between Inter_Category (restaurant) and Top_Category (coffee shop) sales in a geographical area centered on Gangnam Station. In this case, the user can click the window-shaped cursor at a specific point in time 1211, 1212, 1221, 1222 or at specific points as if they were dragged. In this case, the forcast data system 600 can display the information of the townships where the fluctuation occurred at the time point using the low data, the secondary data, or the forecast data. In addition, it is also possible to display the rents of buildings or new buildings of the shops where the change occurred at that time.

As shown in FIG. 12, if the sales of restaurants corresponding to Inter_Category are increased at 1211 and 1221, or at 1212 and 1222, respectively, and corresponding to the top_category, sales of the coffee shops corresponding to the store sizes increase, There is a good circulation relationship among salespeople. Therefore, the inter-category prediction unit 620 or the forcast data system 600 including the inter-category prediction unit 620 can show the influence of increase / decrease of the sales of the malls belonging to the Inter_Category on the increase / decrease of sales of the malls belonging to the Top_Category.

11 and 12 are summarized as follows.

The central control unit 690 stores first secondary data including sales information in a database in which sales information of a preset time period is cumulatively accumulated based on the identification information of the commercial areas corresponding to the first category information (Top_Category) Can be extracted.

The central control unit 690 can extract second secondary data including sales information in a database in which sales information of a predetermined time period is cumulatively accumulated based on the identification information of the commercial areas corresponding to the second category information Inter_Category have.

Then, the inter-category predicting unit 630 uses the first secondary data and the second secondary data, and when the direction of change of the first secondary data and the second secondary data has a direction equal to or higher than a preset reference, It is possible to generate the forecast data for determining success probability of the commercial value corresponding to the first category information by the corresponding merchants as success.

In an embodiment, the increase points of 1211 and 1221 in FIG. 12 overlap, and the increase in sales of the commercial areas corresponding to the second category information based on overlapping of the increase points of 1212 and 1222, In the same period.

The forecast data system 600 that has been examined so far includes the forcasts calculated by the sales fluctuation predicting unit 610, the intra-category predicting unit 620, the inter-category predicting unit 630, the trend analyzing unit 640, Data can be used to analyze the expected benefits of opening a mall in a particular area. In particular, based on the information on sales by store size, the central control unit 690 receives the sales information from the information calculated by the sales fluctuation predicting unit 610, the intra-category predicting unit 620, and the inter-category predicting unit 630, It is possible to analyze the applicability of competition and scale economies, and the influx of customers from different categories.

According to an embodiment of the present invention, the trend analysis unit 640 can analyze the trend corresponding to specific category information through the Internet or the big data collection. For example, by analyzing the media, search results, and blog materials, we can calculate the index of the possibility of sales increase in a specific category, and finally estimate sales and net profit Can be calculated.

The sales fluctuation predicting unit 610, the intra-category predicting unit 620 and the inter-category predicting unit 630 can apply the trend analysis result calculated by the trend analyzing unit 640 to generate the forecast data have.

Meanwhile, the sales fluctuation predicting unit 610, the intra-category predicting unit 620, and the inter-category predicting unit 630 can correct using the reliability of the raw data or the secondary data necessary for generating the forecast data. For example, reliability can be measured for an input to which raw data or secondary data is input, or reliability can be reflected based on the priority of the information. That is, the reliability is the reliability of the input terminal to which the low data or the secondary data is input.

In addition, the sales fluctuation predicting unit 610, the intra-category predicting unit 620, and the inter-category predicting unit 630 do not merely reflect the sales portion in generating forecast data, The forcast data can be generated reflecting the increase or decrease in rent. In this case, even if the actual sales increase, the sales profit may decrease. The sales fluctuation predicting unit 610, the intra-category predicting unit 620, and the inter-category predicting unit 630, Can be generated.

Next, an embodiment will be described in which foreground-category prediction and inter-category prediction generate forecast data by analyzing the opening and closing points of a store, rather than increasing or decreasing sales. To this end, the forcast data system can be configured as shown in FIG.

13 is a diagram illustrating a configuration of a forecast data system according to another embodiment of the present invention.

6, the low data system 100 and the secondary data system 400 may be included in the precast data system 1300. [ In another embodiment, the low data system 100 and the secondary data system 400 are disposed outside the precast data system 1300 and the low data system 100 and the secondary data system 400 are communicated with each other via communication, And transmit and receive information to and from the cast data system 1300. [

More specifically, the precast data system searches for and analyzes changes in commercial real estate based on temporal cumulative data, that is to say, changes such as closing or closing a shop or a store, or building a new building, An intra-category prediction unit 1320, an inter-category prediction unit 1330, a trend analysis unit 1340, and the like.

The shop price fluctuation predicting unit 1310 predicts the forecast data based on the change of opening / closing / building of the shop in the field requested by the client apparatus, such as a shopping area included in a specific area, a specific shop price or a specific category .

The intra-category predicting unit 1320 analyzes the degree of influence of the opening or closing of other shops in the same category within the category to generate forecast data. The impact can be seen when the number of open stores increases steadily, and that the degree of competition is rising. In addition, it can show whether synergy such as increased sales is caused by the increase in competition due to sales information. 9 and 10, synergy of sales increase was confirmed. Likewise, if the number of closures increases, it can be confirmed that the competitiveness of the category is lowered. In addition, competition may be reflected by reflecting the sales information, but it may be confirmed whether the sales increase or decrease according to the decrease.

The inter-category predicting unit 1330 generates forecast data by analyzing the degree of influence on the opening or closing of shops of different categories to the opening and closing points of the commercial value among the categories of business having different categories.

The trend analyzing unit 1340 collects information on current or past trends and generates forecast data on the promising business category or the prospective category. To this end, the trend analysis unit 1340 may include a database including a trend analysis result. The trend analysis unit 640 of FIG. 6, which has been described above, may be similarly applied.

The central control unit 1390 controls the above-described up-market fluctuation predicting unit 1310, the intra-category predicting unit 1320, the inter-category predicting unit 1330, the trend analyzing unit 1340, Or collects and processes the data into a form that can be provided to the client and transmits it to the client. The central control unit 1390 may provide or transmit data stored or generated by the low data system 100 and the secondary data system 400 to the other components 1310, 1320, 1330, and 1340.

In summary, the central control unit 1390 receives the first region information and the first category information from the client, and forwards the forecast data to the client.

The shop price fluctuation predicting unit 1310 generates forecast data including the increase / decrease change value, which is variation information related to the opening or closing of at least one shop included in the above-described first category information in the first area.

On the other hand, the intra-category predicting unit 1320 generates forecast data of the malls in the first category in accordance with the variation information relating to the opening or closing of one or more malls included in the first category information in the first area. The forecast data can be the opening / closing of a shopping mall, the sales change value of the malls, the sales change value of the malls by the store size, the change of the floating population, or the mall table turnover rate. In addition, it may include increased sales of a particular menu in a shopping mall, and the number of times referred to in a blog or the like.

The inter-category prediction unit 630 generates forecast data of the malls in the first category according to the variation information related to the opening or closing of the malls included in the second category information as the comparison target category in the first area. The forecast data can be the opening / closing of a shopping mall, the sales change value of the malls, the sales change value of the malls by the store size, the change of the floating population, or the mall table turnover rate. In addition, it may include increased sales of a particular menu in a shopping mall, and the number of times referred to in a blog or the like.

FIG. 14 is a diagram illustrating a process of confirming the influence of the change information of the commercial value in the category in the intra-category predicting unit according to another embodiment of the present invention.

In the category, for example, when the Top_Category is set as a coffee shop and a specific area is set, the fluctuation information in which the commercial value is increased or decreased in the Top_Category is compared with the increasing or decreasing size of the forecast data of the coffee shop in the corresponding area It is possible to grasp forcast data about opening a shopping center corresponding to the same category. Here, the forecast data may include one or more of various information such as increase / decrease of sales by store size, increase / decrease of sales by each store, and increase / decrease of the floating population.

To this end, the intra-category predicting unit 1320 confirms information for selecting the target area and Top_Category (S1410). In step S1420, the intra-category predicting unit 1320 restricts the intra-category prediction unit 1320 to a predetermined analysis period (for example, three years or five years) and extracts malls corresponding to the Top_Category in the target area from the History_Store_DB 410.

In addition, the intra-category predicting unit 1320 calculates secondary data relating to the variation information in the History_Store_DB 410 and the Sales_DB 420 for the information on the extracted shopping areas and the information that has been opened or closed within the analysis period on the same Top_Category in the corresponding area (S1430). Variable information secondary data may include the opening / closing of a shopping mall, the sales of the shopping malls corresponding to the shopping mall, cumulative floating population, etc., and may be selected selectively in accordance with the priority, accuracy, and reliability of the information provider . In addition, the forecast data system 1300 transmits the updated information to the client through a process of improving the accuracy of the variation information secondary data (S1440).

The intra-category predicting unit 1320 predicts, using the fluctuation information secondary data, the stores opened or closed for each period, for example, on a monthly basis or a quarter basis, on an annual basis, It is possible to calculate the increase / decrease of sales by store area, the increase / decrease of sales by each store, the increase / decrease of the flow population, and output about the influence between them, as shown in Fig.

14 is summarized as follows.

The central control unit 1390 determines whether the opening or closing information of the shopping malls at a predetermined time period is accumulated temporally based on the identification information of the shopping centers corresponding to the first category information in the first area, Data is extracted. In this process, the above-described damping fluctuation predicting unit 1310 can also participate in the extraction of secondary data.

The intra-category predicting unit 1320 may generate forcast data that determines the success probability of the first category of commercials in the first area using the secondary data. For example, when the change in the increase / decrease of the commercial value included in the first category of the first region is calculated for the last three years, it is determined that the competition rate of the first category of the first category is increasing The cast data can be calculated. On the other hand, it is possible to judge the success of the shopping malls by using the low data and the secondary data such as the sales by the store size, the sales of each merchant, the increase of the floating population, and the increase / decrease of rents of the shopping mall during this period. If the sales increase, it is possible to calculate forecast data that is likely to succeed even if the competition rate increases. Also, it is possible to calculate forecast data that is likely to succeed even if the flow population increases. Likewise, if the rent is constantly rising, it is possible to produce forecast data that is likely to be successful. In addition, if the number of shops opening is increasing but the number of shops closing is small, it is possible to calculate forecast data that are likely to be successful. By using these various indexes, integrated forcast data can be calculated and output in graph form. Also, if the opening-closing time is short, the forecast data indicating that the possibility of success is low can be generated by analyzing the opening and closing periods of the shops.

Forecast data can be output as a score or graph for each element, and each element can be output as a combined score or graph reflecting the weight or accuracy of each element.

In addition, for the area of interest (for example, a hot place), the forecast data may be continuously stored, and the intra-category prediction unit 1320 may refer to or correct the generated forecast data. For example, if the forecast data is generated in the past, the weight for the opening-closing period is determined to be 25%. If this part is confirmed through the accumulated forecast data that is important for the success of the shopping, The weight can be adjusted up to 35%.

FIG. 15 is a diagram showing the effect of intra-category prediction within a specific region according to an embodiment of the present invention, according to variation of malls within a category.

1510 shows the change information (opening / closing, etc.) of merchants included in the category within a specific geographical area from October 2014 to October 2016. It can be seen that the number of commercials has increased at the points indicated by 1511 and 1512. When the corresponding region is clicked, the intra-category prediction unit 1320 or the forecast data system 1300 including the intra-category prediction unit 1320 can display the change status of the shop in the corresponding category in the corresponding area at the corresponding time in a pop-up form. For example, if you click on the square of 1511, you can indicate that there was a coffee shop opened (open) in that time zone. 1510 is a graph that can confirm the increase / decrease of the merchants in the category.

1520 shows the forecast data of the merchants in that category within a specific geographical area from October 2014 to October 2016. One example of the forecast data may be increase or decrease of sales by store size, increase or decrease of sales by stores, increase or decrease of the floating population, and the like. 1521 shows the temporal area corresponding to 1511 and the sales by store width for the following two months, or the sales by the store and the increase of the floating population. 1521, the change situation in the corresponding category in the corresponding area at that time can be displayed in a pop-up form. In addition to the changes shown in 1511 above, the changes between June and August 2015 can also be displayed in a pop-up form. More specifically, at what point in time a particular mall opens or closes, or how the number of malls are increased or decreased.

In one embodiment, FIG. 15 is a graph showing changes in coffee shops in a geographical area centering on Gangnam Station, sales by store area, sales by store, and change of a floating population. In this case, the user can click the window-shaped cursor at a specific point in time 1511, 1512, 1521, 1522 or at specific points in time. In this case, the forecast data system 1300 can display the information of the townships where the fluctuation occurred at the time point using the low data, the secondary data, or the forcast data. In addition, it is also possible to display the rents of buildings or new buildings of the shops where the change occurred at that time. It can also reflect the increase or decrease in the number of shops and new construction situations.

As shown in FIG. 15, in 1511 and 1521, or in 1512 and 1522, a shopping mall corresponding to the corresponding category of coffee shop was opened. As a result, sales of the entire coffee shop category rose or the floating population increased, , It can be predicted that the establishment of a coffee shop in the area can be synergistic due to the nearby coffee shops. The forecast data system 1300 uses the row data and the secondary data including the history of the opening or closing of the accumulated malls in the area, the possibility of opening the specific category of the specific area, and the forecast data calculated therefrom If the number of commercial areas is increased and the area is increased and sales of the area are increased, the intra-category predicting unit 1320 determines that the success rate of the shopping area belonging to the category is high in the area, . 14 and 15, it is possible to avoid the risk of opening the mall by confirming the competition and synergy effect in opening the mall in a specific area in a specific area.

16 is a diagram illustrating a process of confirming the influence of the increase in sales among different categories in the inter-category predicting unit according to an embodiment of the present invention.

In the case where a different category, for example, Top_Category, is set as a coffee shop and a different Inter_Category is set as a restaurant, fluctuation information that increases or decreases the price of Inter_Category in a predetermined region and an analysis corresponding to the above- During the period, the fluctuation information of the increase or decrease of the shop price in the Top_Category is compared, and it is possible to grasp the profit or loss of opening the shop corresponding to the Top_Category in the area where the Inter_Category is large.

To this end, the inter-category predicting unit 1330 confirms information for selecting the target area, Inter_Category, and Top_Category (S1610). The inter-category predicting unit 1330 limits variation to a predetermined analysis period (for example, 3 years or 5 years) and extracts variation information of commercials corresponding to the Inter_Category in the target area from the History_Store_DB 410 (S1620 Similarly, variation information of the malls corresponding to Top_Category is extracted (S1630).

In addition, the inter-category predicting unit 1330 generates variation information of the extracted commercial values as forecast data (S1640). In this process, for example, the sales of the malls belonging to the Top_Category, which are operated on a monthly basis or a quarter basis, or the floating population around the sales or shopping area can be calculated as additional forcast data. The forcast data system 1300 can then send to the client.

16 is summarized as follows.

The central control unit 1390 determines whether the opening or closing information of the shopping malls at a predetermined time period is accumulated temporally based on the identification information of the shopping centers corresponding to the first category information in the first area, 1 Extract secondary data. In addition, the central control unit 1390 stores the store opening / closing change value in the database in which store opening or closing information of a predetermined time period is cumulatively accumulated based on the identification information of the shopping areas corresponding to the second category information in the first area The second secondary data is extracted. In this process, the above-described damping fluctuation predicting unit 1310 can also participate in the extraction of secondary data.

The inter-category predicting unit 1330 uses the first secondary data and the second secondary data, and when the direction of change of the first secondary data and the second secondary data has a direction equal to or higher than a predetermined reference, It is possible to generate the forecast data for determining success probability of the commercial value corresponding to the first category information by the corresponding merchants as success. For example, it is assumed that the change in the increase / decrease of the commercial value included in the first category of the above-mentioned first category has been increased for the past three years, and that of the second category of the first region has also increased for the third year It can be seen that synergies can be provided between the commercials belonging to the first category and the commercials belonging to the second category if the commercial value change change value is calculated. Also, in order to calculate more accurate precast data, it is necessary to determine whether or not the sales amount of the merchants belonging to the first category during the period, the sales of each of the merchants, the increase of the floating population, Using the low data and the secondary data, it is possible to determine the success of the shopping districts. If the sales increase, you can calculate forecast data that is likely to be successful. Also, it is possible to calculate forecast data that is likely to succeed even if the flow population increases. Likewise, if the rent is constantly rising, it is possible to produce forecast data that is likely to be successful. In addition, if the number of shopping streets belonging to the first category increases, but the number of shopping streets to be closed is small, it is possible to calculate forecast data having a possibility of success reflecting the synergy effect of the second category. By using these various indexes, integrated forcast data can be calculated and output in graph form. Also, if the opening-closing time is short, the forecast data indicating that the possibility of success is low can be generated by analyzing the opening and closing periods of the shops.

Forecast data can be output as a score or graph for each element, and each element can be output as a combined score or graph reflecting the weight or accuracy of each element.

In addition, for the area of interest (for example, a hot place), the forecast data may be continuously stored so that the inter-category predicting unit 1330 may refer to or correct the generated forecast data. For example, if the forecast data is generated in the past, the weight for the opening-closing period is determined to be 25%. If this part is confirmed through the accumulated forecast data that is important for the success of the shopping, The weight can be adjusted up to 35%.

17 is a diagram showing the influence between different categories of the inter-category predicting unit within a specific region and a specific period according to another embodiment of the present invention.

1710 shows the forcast data of the stores included in the Inter_Category within a specific geographical area from December 2014 to December 2016, that is, data on the fluctuation of the commercial value. It can be seen that the number of downtown commercial districts increased at points indicated by 1711 and 1712. If the corresponding region is clicked, the change status of Inter_Category within the corresponding region at the corresponding time point can be displayed in a pop-up form in the inter-category prediction unit 1320 or the forecast data system 1300 including the inter-category prediction unit 1320. For example, if you click on the square of 1711, you can indicate that there was a restaurant open in that area for that period. Or if the closing and opening of a restaurant specific to the period within the area of the area occurs consecutively, the number of shops actually opening / closing the shop may increase. 1712 corresponds to the same.

1720 shows changes such as the opening or closing of shops included in the Top_Category within a particular geographical area from December 2014 to December 2016. 1721 shows the temporal area corresponding to 1711 earlier and the change in the commercial value in Top_Category for the following two months. 1721, the change status in the corresponding category (Top_Category) in the corresponding region can be displayed in a pop-up form at that point in time. A situation where a shopping street included in the Top_Category is newly opened or an area of a shopping street is expanded can be considered. 1722 likewise applies.

In FIG. 17, when the variation of the price of Inter_Category and the variation of the price of Top_Category are compared, a similar rising pattern can be confirmed.

In one embodiment, Fig. 17 can be a graph showing the correlation between the increase / decrease of the Intercategory (restaurant) and Top_Category (coffee shop) in the geographical area centering on Gangnam Station. In this case, the user can click the window-shaped cursor at a specific point in time 1711, 1712, 1721, 1722 or at specific points in time. In this case, the forecast data system 1300 can display the information of the townships where the fluctuation occurred at the time point using the low data, the secondary data, or the forcast data. In addition, it is possible to display the rents of new buildings, new buildings, increase / decrease of sales, increase / decrease of the floating population, and so on.

As shown in FIG. 17, if the number of restaurants corresponding to Inter_Category increases in 1711 and 1721, or in 1712 and 1722, and if the number of coffee shops corresponding to the Top_Category increases, there is a virtuous relationship between the commercial areas of these two categories . Accordingly, the inter-category predicting unit 1320 or the forecast data system 1300 including the inter-category predicting unit 1320 can be classified into the category of the Top_Category, such as increase / decrease of sales, increase / decrease of the store area, It is possible to suggest an effect on opening and closing, increase or decrease in sales, or increase in the floating population. The present invention is not necessarily limited to these embodiments. That is, within the scope of the present invention, all of the components may be selectively coupled to one or more of them. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. The codes and code segments constituting the computer program may be easily deduced by those skilled in the art. Such a computer program can be stored in a computer-readable storage medium, readable and executed by a computer, thereby realizing an embodiment of the present invention. As the storage medium of the computer program, a magnetic recording medium, an optical recording medium, a carrier wave medium, or the like may be included.

It is also to be understood that the terms such as " comprises, "" comprising," or "having ", as used herein, mean that a component can be implanted unless specifically stated to the contrary. But should be construed as including other elements. All terms, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Commonly used terms, such as predefined terms, should be interpreted to be consistent with the contextual meanings of the related art, and are not to be construed as ideal or overly formal, unless expressly defined to the contrary.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

100: Low data system 400: Secondary data system
600: Forecast data system 610: Revenue variation prediction unit
620: Intra-category predicting unit 630: Inter-
640: Trend analysis part 1300: Forecast data system
1310: a shop price fluctuation predicting unit 1320: an intra-
1330: inter-category predicting unit 1340:

Claims (11)

A central control unit for receiving information on a first area and first category information from a client, and transmitting forecast data to the client;
Generating forecast data including a sales change value of at least one mall included in the first category information in the first area, calculating information on the sales of the malls based on the viewpoints, A sales fluctuation predicting unit for collecting the results obtained by applying the priority or accuracy of each provider and converting and providing the forecast data based on the area of each shop;
And generates forcast data including a sales change value of the malls in the first category according to the change value of the malls included in the first category information in the first area, Or an increase or decrease in sales by each store; And
And an inter-category predictor for generating forecast data including a sales change value of the malls in the first category according to the change value of the malls included in the second category information as the comparison target category in the first area,
The intra-category prediction unit displays the increase / decrease of total sales within a predetermined time period of the shopping malls included in the first category in the first area and displays the information of the malls where the change occurred at a specific time point or the rent of the malls or the new building of the building , A forecast data system for analyzing commercial real estate based on temporal cumulative data.
The method according to claim 1,
The central control unit
Extracting secondary data including sales information in a database in which sales information of a predetermined time period is cumulatively accumulated based on the identification information of one or more commercials included in the first category information in the first region,
Wherein the sales fluctuation predicting unit generates forecast data for determining that the sales fluctuation forecast is an increase when the secondary data is increased beyond a preset reference, wherein the sales fluctuation predicting unit analyzes the commercial real estate based on the temporal cumulative data.
The method according to claim 1,
The central control unit extracts first secondary data including sales information in a database in which sales information of a predetermined time period is cumulatively accumulated based on the identification information of the commercial areas corresponding to the first category information in the first region ,
The central control unit extracts second secondary data related to the opening or closing of business based on the identification information of the malls,
Wherein the intra-category predicting unit predicts success of the shopping center corresponding to the first category information in the first area when sales of each store area exceeds a preset reference using the first secondary data and the second secondary data, A forecast data system for analyzing commercial real estate based on temporal cumulative data, generating forcast data that judges the likelihood of success.
The method according to claim 1,
The central control unit extracts first secondary data including sales information in a database in which sales information of a predetermined time period is cumulatively accumulated based on the identification information of the commercial areas corresponding to the first category information in the first region ,
The central control unit extracts second secondary data including sales information in a database in which sales information of a predetermined time period is cumulatively accumulated based on the identification information of the malls corresponding to the second category information,
The inter-category predicting unit may use the first secondary data and the second secondary data so that when the direction of change of the first secondary data and the second secondary data has a direction equal to or more than a preset reference direction, And generates forecast data that determines success probability of the commercial value corresponding to the first category information by the corresponding merchants as success. The forecast data system analyzes commercial real estate based on temporal cumulative data.
The method according to claim 1,
Further comprising a trend analyzer for analyzing trends corresponding to the first category information through internet or big data collection,
The sales fluctuation predicting unit, the intra-category predicting unit, and the inter-category predicting unit analyze the commercial real estate based on the temporal cumulative data, which is applied to generate the forecast data by using the trend analysis result calculated by the trend analyzing unit Forcast data system.
The method according to claim 1,
Wherein the sales fluctuation predicting unit, the intra-category predicting unit, or the inter-category predicting unit corrects using the reliability of the raw data or the secondary data necessary for generating the forecast data,
Wherein the reliability is a reliability of an input terminal to which the low data or the secondary data is inputted.
The method according to claim 1,
The secondary data required for generating the forcast data is provided by the secondary data system,
The secondary data system
A history store database for accumulating temporal accumulations of changes in shopping malls, and
And a sales database for accumulating and storing sales information of the mall in a timely manner,
Characterized in that said secondary data system is included in said forcast data system or communicates data via communication with said forcast data system.
A central control unit for receiving information on a first area and first category information from a client, and transmitting forecast data to the client;
An amusement variability predicting unit for generating forecast data including an increase / decrease change value of at least one store included in the first category information in the first area;
An intra-category predicting unit for generating forecast data of malls included in the first category information in the first area; And
And an inter-category predicting unit for generating forecast data of the malls in the first category according to the increase / decrease change value of the mall included in the second category information as the comparison target category in the first area, based on the temporal cumulative data Analyze commercial real estate,
The intra-category predicting unit analyzes the opening and closing periods of the shops to indicate that the possibility of success is low when the opening-closing time is short, the increase of the sales, rents or the floating population The intra-category prediction unit determines the success probability by reflecting at least one of the intra-category prediction and the intra-category prediction, 1 category, the change information is displayed within a predetermined period of time, and at least one of an increase / decrease of sales by store size, an increase / decrease of sales by stores, or an increase / decrease of a floating population is displayed within the period.
9. The method of claim 8,
Wherein the central control unit is operable to store the opening or closing information of the shopping malls at a preset time based on the identification information of the shopping centers corresponding to the first category information in the first area, Extracts the data,
Wherein the intra-category predicting unit generates forecast data for judging the success probability of the commercial value of the first category in the first area by using the secondary data, and forcast data for analyzing commercial real estate based on temporal cumulative data system.
9. The method of claim 8,
Wherein the central control unit is configured to store the opening or closing information of the shopping malls at a predetermined time based on the identification information of the shopping centers corresponding to the first category information in the first area, 1 < / RTI > secondary data,
Wherein the central control unit is configured to store the opening / closing information of the shopping malls at predetermined time based on the identification information of the shopping centers corresponding to the second category information in the first area, 2 Secondary data is extracted,
The inter-category predicting unit may use the first secondary data and the second secondary data so that when the direction of change of the first secondary data and the second secondary data has a direction equal to or more than a preset reference direction, And generates forecast data that determines success probability of the commercial value corresponding to the first category information by the corresponding merchants as success. The forecast data system analyzes commercial real estate based on temporal cumulative data.
9. The method of claim 8,
The secondary data required for generating the forcast data is provided by the secondary data system,
The secondary data system
A history store database for accumulating temporal accumulations of changes in shopping malls; And
A sales database for accumulating and accumulating sales information of malls in a timely manner; / RTI >
Characterized in that said secondary data system is included in said forcast data system or communicates data via communication with said forcast data system.

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