TWM614593U - Identification device based on big data for house purchase demand - Google Patents

Abstract

An identification device based on big data for house purchase needs includes an object database, a customer database, a data collection module, a processing module, and a transmission module. The processing module calculates the customer type and the customer's house purchase ability level based on the plural component characteristics of the first search condition issued by the customer code, provides a list of customer house purchase requirements, and transmits it to the client device through the transmission module.

Description

Identification device based on big data for house purchase demand

This disclosure relates to a display processing device, and in particular, to an identification device based on big data for housing purchase requirements.

According to a survey of home purchase intentions, people care about the convenience of their homes. The property needs that people who want to buy a house will consider include: proximity to consumer shopping districts, proximity to parks and green spaces, proximity to MRT/high-speed rail/stations, etc. Housing objects with good objects can increase the convenience of the residents in their lives.

Nowadays, the Internet is popular, so people are accustomed to searching for information on the Internet. For the supply of real estate items, some providers of real estate items will present the pictures, layout and housing information of the items on the website. Some real estate providers will use the map to show the surrounding school districts, hospitals and other things.

In order to improve the convenience of users when using home buying websites, most home buying websites will provide users with the function of favorites. When browsing and searching for housing objects, users click the "favorite" button or similar function buttons on the object display page To perform the collection operation, you can add your favorite objects to your favorites. After that, users can use their favorites to check, track, compare, or purchase items they like or are interested in daily.

However, in many cases, users can only Search for a certain number of objects, and then see if they meet your needs one by one, and include them in your favorites. However, the efficiency of finding objects is still too low, and it will take a lot of time and energy to find objects of the same nature. (For example, they are all in Taipei City, and they all have the conditions of being close to hospitals, parks, etc.).

In order to solve these problems, a variety of object recommendation mechanisms are derived. However, these mechanisms often lack a clear summary and classification of home buyers, so that when the broker obtains the list of customers, they often have to go through telephone interviews one by one. , Personally visit, and then you can start to have a preliminary understanding, generalization and classification of the customers you contact, and then you can know how to serve different types of customers.

In order to solve the problem that there are too many objects to choose from, one purpose of this creation disclosure is to provide an identification device based on big data for home purchase needs. An identification device based on big data for house purchase needs includes an object database, a customer database, a data collection module, a processing module, and a transmission module. The processing module calculates the customer type and the customer's house purchase ability level based on the plural component characteristics of the first search condition issued by the customer code, provides a list of customer house purchase requirements, and transmits it to the client device through the transmission module.

10: Object display system

111, 112, 113, 114: objects

111A, 112A, 113A, 114A: actual distance

111P, 112P, 113P, 114P: composition characteristics

121: Extra Object

121A: Actual distance

121P: Additional composition features

131: Recommended objects

131A: Actual distance

131P: Recommended composition characteristics

15: Customer code

15P: where customers are often

20: display system

21: Data processing device

211: Object Database

212: Processing Module

2121: Search component

2122: Map Generator Component

2123: recommended components

213: Data Collection Module

22: client device

221: display screen

26: Customer database

28: Identification device based on big data for home purchase demand

31: Transmission module

91: User

D111, D112, D113, D114: object label

D111A, D112A, D113A, D114A: data unit of activity range

D111A1, D112A1, D113A1, D114A1: attribute words

D111A2, D112A2, D113A2, D114A2: Quantity string

D111A3, D112A3, D113A3, D114A3: interactive content words

D111H, D112H, D113H, D114H: Tag category indicator

D111P, D112P, D113P, D114P: location information

D121: Extra Object Label

D121A: Additional activity scope data unit

D121A1: Extra attribute words

D121A2: Extra quantity string

D121A3: Additional interactive content terms

D121H: Additional object category indicator

D121P: Additional location information

D131: Recommended item label

D131A: Actual distance data unit

D131A1: Recommended attribute words

D131A2: Recommended quantity string

D131A3: Recommended interactive content words

D131H: Recommended label category indicator

D131P: Recommended location information

D15: Real Estate Information Unit

D15H: Real estate category indicator

D15P: Real estate location information

D1A: Scope of Activities Data Unit

D5: Geographic Information

D51: Icon data

D52: Map data block

D53: The second map data block

D54: The third map data block

D55: The fourth map data block

D61: Object attribute image data block

D62: Object attribute image data block

DA1: First object label

DHA1, DHA2: Object type indicator

DL1: Activity range value

EA1, EA2, EA3, EA4: Object candidate category items

EL1, EL2, EL3, EL4: Candidate distance items

H111, H112, H113, H114: label category

HA1, HA2: Object category

HB1: Additional object category

HC1: Recommended object category

HD1: Real estate category

K111, K112, K113, K114: icon

K121: Icon

KA2: Second icon

KA3: The third icon

KB3: The third icon

KA4: Fourth icon

L1: radius of activity range

M1: Map

M111P, M112P, M113P, M114P: mark position

M121P: Additional marking location

M131P: Recommended marking position

M15P: Real estate marked location

M2: second map

M3: Third map

M4: Fourth map

M5: Fifth Map

MA1: The first marking position

MA2: second marking position

MA3: third marking position

MA4: Fourth marking position

MA5: Fifth marking position

MR1: specific map area

Q1: Optional icon

R1: Range of activity

S11: First search condition

S12: Second search condition

S13: Third search condition

S14: Fourth search condition

U1: Screen

This disclosure can be further understood by the detailed description of the following diagrams:

Figure 1: Illustration of a display system in various embodiments of the present disclosure intention.

Figure 2: A schematic diagram of an object display system in various embodiments of the present disclosure.

Figure 3: A schematic diagram of the structure of an object database in Figure 1.

Figure 4: A schematic diagram of a map in various embodiments of the present disclosure.

Figure 5 shows a classification schematic diagram of a specific embodiment of the tag-based recommendation device proposed in this creation.

Please refer to Figure 1, Figure 2 and Figure 3. FIG. 1 is a schematic diagram of a display system 20 in various embodiments of the present disclosure. FIG. 2 is a schematic diagram of an object display system 10 in various embodiments of the present disclosure. Figure 3 is a schematic diagram of the structure of an object database in a customer database 26 in Figure 1. As shown in FIG. 1, the display system 20 includes a data processing device 21, the object database 211, a client database 26 and a client device 22 coupled to the data processing device 21. The data processing device 21 includes an object database 211, a processing module 212, and a data collection module 213 coupled to the processing module 212. The client device 22 includes a display screen 221. For example, there is a transmission module 31 between the data collection module 213 and the client device 22, and the transmission module 31 is coupled therebetween.

As shown in FIG. 2, the object display system 10 includes a plurality of objects 111, 112, 113 and 114. For example, the object display system 10 may further include at least one additional object 121, a recommended object 131, and a customer code 15. There are a plurality of interactive relationships 111A, 112A, 113A, and 114A between the customer code 15 and the plurality of objects 111, 112, 113, and 114, respectively. The plurality of objects 111, 112, 113, and 114 respectively have a plurality of composition features 111P, 112P, 113P, and 114P. The additional object 121 has an additional location 121A and an additional composition feature 121P. The recommended object 131 has a location 131A and a recommended composition feature 131P. The customer code 15 has a location 15P where a real estate customer is often located. The processing module 212 calculates an actual distance between the plurality of objects falling within the issued first search condition and the location where the customer is often located.

The plurality of objects 111, 112, 113, and 114 belong to a plurality of tag categories H111, H112, H113, and H114, respectively, and each of the plurality of tag categories H111, H112, H113, and H114 is of a plurality of object categories HA1 and HA2. one of them. The additional object 121 belongs to an additional object category HB1, and the additional object category HB1 is different from any one of the plurality of object categories HA1 and HA2. The recommended object 121 belongs to a recommended object category HC1.

For example, the plurality of object categories HA1 and HA2 are respectively a hospital category and a store category, and the additional object category HB1 is a restaurant category. For example, two objects 112 and 113 belong to the hospital category and the store category respectively, so that the real estate customer of the customer code 15 is often located at the actual distance 112A between the location 15P and the object 112, and the actual distance 113A from the object 113, respectively It is 10 meters and 100 meters.

The customer database in the customer database 26, the customer database is used to store a plurality of customer data, each customer data mainly includes customer code, customer type, customer house purchase ability level, customer frequent location, and the first search condition S11 or The second search condition S12, where the customer The types of households are at least divided into first purchase, house swap, and property purchase. The first search condition S11 includes the customer code and multiple component features of the customer code, and the component features are mainly composed of these. The component features include selected from the abbreviation, price, community name, and address of the real estate object , Floor, building registration area, land registration area, unit price per unit area, type, layout, house age, parking space, seating orientation, elevator, management fee, layout diagram, and living functions. Each of the multiple component features has a priority. The higher the priority, the higher the priority and the higher the overlap, which will affect the results presented in the customer's housing demand list.

The data collection module 213 receives the first search condition S11 and the second search condition S12 at different times, and sends the second map data block D53 and the third map data block D54 to the client at different times The device 22 transmits so that the client device 22 displays the second map M2 and the third map M3 on the display screen 221 at different times, and stores the first search condition S11 and the second search condition S12 in the Customer database. For example, the data collection module 213 is coupled to the client device 22 via the transmission module 31.

In some embodiments, user 91 has sent a search record (ie, the first search condition) for objects 111 and 114, and user 91 has passed by a special device near objects 112 and 113, and can find that user 91 uses The existence of the device (that is, the second search condition), and through the above-mentioned geographic locations, the activity range R1 can be encircled on the map. In other words, if in the future when the user 91 makes a purchase request, the object of his interest falls within the activity range R1, the system will note that the user 91 is in Landlord. However, the system can flexibly expand the range covered by the activity range R1 to make the judgment of the local customers more accurate, because it may just happen that the user 91 has not enough information about the habit track left in the system, which leads to the system Misjudgment. In contrast, if the object of interest of the user 91 falls within the activity range R1, the system will mark the user 91 as a non-local visitor.

However, sometimes the user 91 only occasionally runs to an area where he is not frequently active. For this, the processing module 212 first removes the plural component features or the first search condition S11 issued by the customer code. Only when the predetermined deviation value in the second geographic location in the second search condition S12 is exceeded, the relative activity range of the customer code is calculated. In contrast, if the amount of data is too small, the system may misjudge and a threshold value needs to be set. Therefore, the processing module 212 first confirms the multiple component features or the first search condition S11 issued by the customer code. Only when the second geographic location in the second search condition S12 exceeds a predetermined number, the relative activity range of the customer code is calculated.

In addition, the processing module 212 can display relevant information about objects that the user 91 has interacted with on the electronic map, and send the activity range and the second map to the client through the transmission module 31装置22。 Device 22. Specifically, the processing module 212 also filters out the plurality of objects that fall within the issued first search condition, and then generates the second map data block representing a second map, wherein the second The map is a map in which a plurality of icons are respectively displayed at the plurality of marked positions on the map, and the plurality of icons respectively mark the plurality of objects.

In Figure 1, the data collection module 213 receives data from A first search condition S11 or a second search condition S12 of a user 91. The first search condition S11 includes the customer code and multiple component features of the customer code, and the component features are mainly issued for the objects 111, 112, 113, and 114. The component features are mainly issued for the object.

The processing module 212 summarizes the relative label of the object based on the plural component characteristics in the first search condition issued by each customer code, and classifies each customer code into the relative customer type according to the customer type induction principle , And according to the principle of the customer's home purchase ability level, the multiple component characteristics of the multiple customer codes are estimated to be the customer's home purchase ability level, so as to update the customer database, and at the same time calculate the customer's home purchase demand list.

The customer's home purchase demand list is composed of multiple customer codes corresponding to the customer type and the customer's home purchase ability level. The customer type induction principle is the first purchase, exchange, and purchase defined by multiple object tags and multiple component characteristics. The principle of the customer’s home-buying ability level of the property is divided into multiple levels, such as well-off, wealthy, etc., in relation to the budget range of the home purchase.

In other words, the identification device of the present invention generates a judgment on the relative demand of the customer code by analyzing and summarizing a plurality of component characteristics in the first search condition, and summarizes the so-called home purchase demand into the customer type and the customer home purchase ability level. Then consider the actual distance between the location of the customer’s real estate customer and the object, so as to eliminate objects that are not of interest to the customer, to avoid confusion of multiple components, and to avoid further affecting the customer type and the customer’s house-buying ability level. Identify. in this way As a result, when the broker obtains the list of clients, he no longer needs to go through telephone interviews and personal visits one by one, and he can start to have a preliminary understanding, generalization and classification of the clients he contacts, and he can also know how to serve different types of clients.

The processing module 212 calculates a list of customer house purchase requirements based on a plurality of constituent features in the first search condition issued by the customer code. More specifically, the processing module calculates the customer's house purchase demand list based on the comparison table of the tag category and the corresponding theme as shown in Figure 5.

In order to achieve this goal, the system needs to first establish an object database, and each object data in the object database has an object index code and a plurality of composition characteristics to which the object index code belongs.

The object database 211 stores plural label category data, and each label category data has an object index code and the object; the index code belongs to plural component features, and each component feature is mainly a single component feature or multiple components Defined by characteristics. For example, the composition feature is three generations in the same house, and the relative composition feature is to change a large room (more than 3 rooms and more than 30 pings), and the remaining corresponding relationships are shown in Figure 5.

The processing module 212 uses a weight calculation formula to obtain the main influencing object and its relative label category based on the multiple component features in the first search condition issued by the customer code, and then according to the component of the label category The characteristics are defined by a single component feature or multiple component features, find out the multiple real estate information that meets the conditions, and generate a list of customer home purchase needs containing a specific number of multiple real estate information. For example, if the main impact tag category is three generations in the same house (for example, an object suitable for three generations to go together, such as an IKEA furniture store), the processing module 212 Then search the object database 101 with composition characteristics of 3 rooms or more and 30 pings or more. In this way, the user simply selects a specific object, and the system can search for the corresponding object for selection.

In order to collect more information about the activities of the user 91, the system can also place a special device in a specific physical location, and it can find the existence of the device used by the user 91, for example, it records once it detects the WIFI signal of the mobile phone. In other words, the user 91 may also issue the second search condition S12 in a passive state. The second search condition S12 indicates the customer code and a second geographic location of the customer code (that is, the location of the above-mentioned special device).

In some embodiments, the object display system 10 further includes an activity range R1. The activity range R1 is centered on the customer code 15 and has an activity range radius L1 calculated from the customer code 15 as the center. The activity range radius L1 indicates the activity range R1 related to the customer code 15.

In addition, the actual distance L between the customer code 15 and the plurality of objects 111, 112, 113, and 114 can also be represented by the total walking distance based on the distance information of streets and lanes.

As shown in Figures 1 and 3, the object database in the customer database 26 includes geographic information D5 and a plurality of object tags D111, D112, D113 representing the plurality of objects 111, 112, 113, and 114, respectively. And D114, may further include at least one additional object tag D121 representing the at least one additional object 121, may further include a recommended object tag D131 representing the recommended object 131, and may further include the customer code 15 A real estate information unit D15.

In Figure 3, the plurality of object tags D111, D112, D113, and D114 respectively include the first plurality of position information D111P, D112P, D113P, and D114P representing the composition features 111P, 112P, 113P, and 114P, respectively indicating the plurality of A plurality of tag category indicators D111H, D112H, D113H, and D114H of the tag categories H111, H112, H113, and H114, and a plurality of activity range data units D111A, D112A representing the plurality of interaction relationships 111A, 112A, 113A, and 114A, respectively , D113A and D114A.

The additional object tag D121 includes an additional location information D121P representing the additional component feature 121P, an additional object category indicator D121H indicating the additional object category HB1, and an additional activity range data unit D121A representing the additional location 121A. The recommended object tag D131 includes a recommended location information D131P representing the recommended composition feature 131P, a recommended tag category indicator D131H indicating the recommended object category HC1, and an actual distance data unit D131A indicating the location 131A. The real estate data unit D15 includes a real estate location information D15P representing the frequently located location 15P of the real estate customer, and a real estate category indicator D15H indicating the real estate category HD1.

As shown in Figures 2 and 3, the plurality of interaction relationships 111A, 112A, 113A, and 114A in Figure 2 are represented by a plurality of attribute words D111A1, D112A1, D113A1, and D114A1, respectively, and are formed to form and The plurality of interactive relationships 111A, 112A, 113A, and 114A respectively correspond to the plurality of quantities, and respectively correspond to the plurality of interactive relationships Plural interactive content of 111A, 112A, 113A and 114A.

Please refer to Fig. 4, the second search condition S12 indicates that a second icon KA2 (such as the icon K112) among the plurality of icons K111, K112, K113, and K114 is selected, and the second icon KA2 is located in the A first marked position MA1 (for example, marked position M112P) in the second map M2, and corresponds to a first object label DA1 (for example, an object label D112) among the plurality of object labels D111, D112, D113, and D114, And the second map M2 has a second marked position MA2 corresponding to the first marked position MA1.

The system can selectively display related information between the user 91 and the object based on the user's selection. Please refer to FIG. 1, FIG. 3, and FIG. 4. The client device 22 is operated by the user 91 to generate interactive operations at a first time and at a second time after the first time. The interactive operation is transmitted to the data processing device 21 at different times. For example, the data processing device 21 can calculate an actual distance between the plurality of objects and the location where the customer is often located. By receiving a user input from the user 91, when the second icon KA2 displayed on the display screen 221 is selected, the actual distance in the first object tag DA1 is displayed in the second The symbol near KA2.

Many modifications and other embodiments of the present disclosure presented here will be understood by those who are familiar with the art to have the benefits of the teachings presented in the above description and related drawings. Therefore, we should understand that the present disclosure is not limited to the specific embodiments disclosed, and the modifications and other embodiments are intended to be included in the scope of the following patent applications.

20: display system

21: Data processing device

211: Object Database

212: Processing Module

2121: Search component

2122: Map Generator Component

2123: recommended components

213: Data Collection Module

22: client device

221: display screen

26: Customer database

28: Identification device based on big data for home purchase demand

31: Transmission module

91: User

D111, D112, D113, D114: object label

D121: Extra Object Label

D131: Recommended item label

D15: Real Estate Information Unit

D1A: Scope of Activities Data Unit

D5: Geographic Information

D53: The second map data block

D54: The third map data block

D55: The fourth map data block

D61: Object attribute image data block

D62: Object attribute image data block

DA1: First object label

DHA1, DHA2: Object type indicator

DL1: Activity range value

M2: second map

M3: Third map

M4: Fourth map

M5: Fifth Map

S11: First search condition

S12: Second search condition

S13: Third search condition

S14: Fourth search condition

U1: Screen

Claims (4)

A device for identifying housing needs based on big data, including: An object database, the object database including geographic information and a plurality of object tags respectively representing a plurality of objects, each of the plurality of objects has a composition characteristic, and the plurality of objects belong to one of the plurality of tag categories; A customer database, the customer database is used to store a plurality of customer data, each customer data mainly includes a customer code, a customer type, a customer home purchase ability level and a first search condition, where the customer type is at least divided into For first purchase, house swap, and property purchase, the first search condition includes the customer code and multiple component characteristics relative to the customer code; A data collection module, coupled to the customer database, receives the first search condition from a plurality of client devices through a communication module, and stores the first search condition in the customer database; A processing module, coupled with the data collection module, the object database, and the customer database, and based on a plurality of constituent features in the first search condition issued by each customer code, summarizes the relative tag of the object, and According to a customer type induction principle, each customer code is classified into the relative customer type, and according to a customer home purchase ability level principle, the multiple component characteristics of multiple customer codes are estimated as the customer purchase budget interval. Ability level to update the customer database, and at the same time calculate a customer home purchase demand list, where the customer home purchase demand list is composed of a plurality of customer codes corresponding to the customer type, the customer home purchase ability level, and the customer type summarized The principle is that the customer type of first purchase, home exchange, and property purchase defined by a plurality of object tags and a plurality of composition characteristics. The principle of the customer's home purchase ability level is related to a home purchase budget interval and divided into multiple levels; and A transmission module, coupled with the data collection module, the object database, and the customer database, is used to transmit the customer's house purchase demand list to the client device. The device for identifying home purchase needs based on big data according to claim 1, wherein the processing module calculates the difference between the plurality of objects that fall within the issued first search condition and the location where a customer is often located An actual distance; Wherein, when a second icon displayed on a display screen is selected, the actual distance is presented near the second icon. The device for identifying home purchase needs based on big data according to claim 2, wherein the processing module first eliminates the location of the plurality of objects in the first search condition issued by the customer code that exceeds a predetermined deviation value , Calculate the customer type and the customer's house purchase ability level relative to the customer code. According to claim 3, the big data-based home purchase demand identification device, wherein the processing module first confirms that the plurality of objects in the first search condition issued by the customer code exceeds a predetermined quantity before calculating the customer Type and level of the customer’s ability to buy a house.

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