TWI683321B - Data processing method and system using binary space division - Google Patents

Abstract

In a data processing method and system, the processing unit divides the predetermined geographic range into multiple geographic areas based on multiple geographic location data related to reference division objects contained in the predetermined geographic range and uses a binary space segmentation method; The geographical location data and the indexes assigned to these geographical areas are stored in the binary tree; based on the geographical coordinates contained in the binary tree and the historical transaction data related to the buildings within the predetermined geographic range, the historical transactions The data is divided into multiple groups that are related to these geographic areas; and based on the construction data contained in each group's historical transaction data, reference data related to the corresponding geographic area is generated, and the reference data is stored in the storage module and located In a storage location corresponding to the index assigned to the corresponding geographic area.

Description

Data processing method and system using binary space division

The present invention relates to data processing, and in particular to a data processing method and system using binary space division.

At present, banking institutions have used the existing online mortgage loan trial calculation method to provide mortgage loan trial calculation service. This online mortgage loan trial calculation method can be implemented by, for example, a back-end server provided by a banking institution. This background server can be based on the input information related to the mortgaged building (for example, it can include the building address, building type (apartment, Huaxia), etc.), and can use, for example, the existing real estate automatic valuation model (Automated Valuation Models, AVM) to execute For example, multiple regression algorithm, spatial regression algorithm, etc. to process data to estimate the value of the mortgaged building. Therefore, the estimated value of the mortgaged building can be used as an evaluation of whether to apply for mortgage with this mortgaged building. However, the complex regression algorithm and spatial regression algorithm used in existing real estate automatic valuation models are relatively time-consuming in processing data.

In order to solve the above time-consuming problem, we came up with an estimate of the value of the mortgaged building based on a large amount of historical building transaction data, such as the transaction log-in log-in data. However, how to process such huge historical building transaction data in an efficient and relatively accurate manner so as to obtain reference materials that can be quickly applied to building valuation will become an important issue.

Therefore, an object of the present invention is to provide a data processing method that can process historical transaction data of transaction buildings in an efficient and relatively accurate manner.

，m _i代表該M筆歷史交易資料其中被分到第i組的歷史交易資料的筆數，且該m _i筆歷史交易資料其中每一者所含的該地理座標位於第i個地理區域；及(E)根據第i組的m _i筆歷史交易資料所含的建物資料，產生相關於該第i個地理區域的參考資料，並將該參考資料儲存於該儲存模組且位在一唯一對應於指派給該第i個地理區域的索引的儲存位置，其中i=1,2,…,Q。 Therefore, the present invention provides a data processing method for processing M transaction historical transaction data related to all buildings that have been traded within a predetermined geographic range, and through a processing unit and a storage module Computer execution, where M≧2 and each historical transaction data includes the geographic coordinates corresponding to the address of one of the buildings, and the building indicating the total area, building type, age of the building, and transaction price of the corresponding building data. The data processing method includes the following steps: (A) Obtaining P geographic location data related to P different reference division objects contained in the predetermined geographic range and geographic range data of the predetermined geographic range, where P≧1 (B) According to the P geographic location data and the geographic range data and using the binary space division method, the predetermined geographic range is divided into Q geographic regions, where Q≧2 and the Q geographic regions are composed of the P geographic regions Refer to the divided objects; (C) store the P geographical location data and Q indexes that are different from each other and are assigned to the Q geographical regions, respectively, in a binary tree with P non-terminal nodes and Q leaf nodes , Where the binary tree is stored in the storage module, and the P non-terminal nodes store the P geographic location data and the Q leaf nodes store the Q indexes, respectively; (D) according to the storage in the The binary tree of the storage module and the M geographic coordinates of the M historical transaction data divide the M historical transaction data into Q groups respectively related to the Q geographic regions, where M=

, M _i represents the number of historical transaction data in the M group of historical transaction data, and the geographical coordinates contained in each of the _mi historical transaction data are located in the i-th geographical region; And (E) generate reference data related to the i-th geographical area based on the building data contained in the _mi historical transaction data of the i group, and store the reference data in the storage module in a unique Corresponds to the storage location of the index assigned to the i-th geographic area, where i = 1, 2, ..., Q.

Therefore, another object of the present invention is to provide a data processing system that can process historical transaction data of transaction buildings in an efficient and relatively accurate manner.

Therefore, the present invention provides a data processing system for processing M pieces of historical transaction data related to all buildings that have been traded within a predetermined geographic range, where M≧2 and each piece of historical transaction data contains data corresponding to the The geographical coordinates of one of the corresponding buildings and the address of the building, and the building data indicating the total area, building type, age of the building and transaction price of the corresponding building. The data processing system includes a storage module and a processing unit electrically connected to the storage module.

，m _i代表該M筆歷史交易資料其中被分到第i組的歷史交易資料的筆數，且該m _i筆歷史交易資料其中每一者所含的該地理座標位於第i個地理區域；及該處理單元根據第i組的m _i筆歷史交易資料所含的建物資料，產生相關於該第i個地理區域的參考資料，並將該參考資料儲存於該儲存模組且位在一唯一對應於指派給該第i個地理區域的索引的儲存位置，其中i=1,2,…,Q。 When the processing unit receives the M pieces of historical transaction data and P pieces of geographical location data related to P different reference partitioned objects contained in the predetermined geographical range, and the geographical range data of the predetermined geographical range, of which P≧1, the processing unit divides the predetermined geographic range into Q geographic regions based on the P geographic location data and the geographic range data and using a binary space segmentation method, where Q≧2 and the Q geographic regions are composed of The P reference division objects are divided; the processing unit stores the P geographical location data and Q indexes that are different from each other and are assigned to the Q geographical regions, respectively, in an index with P non-terminal nodes and Q leaf nodes Binary tree, wherein the binary tree is stored in the storage module, and the P non-terminal nodes store the P geographical location data and the Q leaf nodes store the Q indexes, respectively; the processing unit is based on The binary tree stored in the storage module and the M geographic coordinates of the M historical transaction data divide the M historical transaction data into Q groups respectively related to the Q geographic regions, where M=

, M _i represents the number of historical transaction data in the M group of historical transaction data, and the geographical coordinates contained in each of the _mi historical transaction data are located in the i-th geographical region; And the processing unit generates reference data related to the i-th geographical area according to the building data contained in the _{i i} historical transaction data of the i group, and stores the reference data in the storage module in a unique Corresponds to the storage location of the index assigned to the i-th geographic area, where i = 1, 2, ..., Q.

The effect of the present invention is that the processing unit can use the stored binary tree to group a large number of historical transaction data in a highly efficient and relatively accurate manner, and generate correlations based on the building material data contained in each group of historical transaction data. References for geographic areas. In the evaluation of a target building, the processing unit can quickly obtain the index assigned to the geographical area where the target building is located by using the stored binary tree, and then read from the storage module corresponding to the index Reference for storage location.

Before the present invention is described in detail, it should be noted that in the following description, similar elements are denoted by the same numbers.

Referring to FIG. 1, an embodiment of the data processing system 1 of the present invention is suitable for processing M (M≧2) historical transaction data related to all buildings that have been traded within a predetermined geographic range. In this embodiment, the predetermined geographic range may preferably be, for example, an urban area, but not within this limit, and in fact, M may represent a large number, but not within this limit. In other embodiments, the predetermined geographic range may be determined according to actual applications. In addition, each historical transaction data includes, for example, the geographic coordinates corresponding to the address of one of the buildings, and indicates the total area of the corresponding building, the type of the building (such as the sky, building, or apartment, but not here Limit), building age and transaction price of building materials, where the geographic coordinates are a two-dimensional geographic coordinates, such as latitude and longitude coordinates. The data processing system 1 may be implemented as a networkable computer, for example, and includes a storage module 11 and a processing unit 12 electrically connected to the storage module, but not limited to this. After processing the M historical transaction data by executing, for example, a program (not shown) stored in the storage module 11 and related to a data processing method, the processing unit 12 can obtain and store a binary tree 111 and Multiple references 112.

Hereinafter, referring to FIG. 1 and FIG. 2, an example will be described to explain how the data processing system 1 executes the data processing method to process the M historical transaction data. The data processing method includes the following steps S21-S26.

First, in step S21, the processing unit obtains P (P≧1) pens generated from outside or through human input operations, which are respectively related to the geographic location data of P different reference partitioned objects contained in the predetermined geographic range And the geographical range information of the predetermined geographical range. In this embodiment, each reference division object may be a geographical feature object or a geographic division line selected from at least railways, streets, elevated roads, rivers, parks, and green spaces, and each geographic location data includes, for example, a group Or more sets of two-dimensional coordinate vectors indicating the contour of one of the P geographic feature objects. Similarly, the geographic range data may include multiple sets of two-dimensional coordinate vectors that define the predetermined geographic range.

Next, in step S22, the processing unit 12 divides the predetermined geographic range into Q (Q≧2) using the Binary Space Partitioning (BSP) method based on the P geographic location data and the geographic range data Geographic regions. That is, the Q geographic regions are divided by the P reference division objects.

For example, in the example shown in FIG. 3, the predetermined geographic range contains, for example, 9 (P=9) reference division objects L ₁ ~L ₉ , where the reference division objects L ₁ ~L ₃ , L _{8 are,} for example, respectively These are the geographical features of railways, rivers, rivers and green areas, and the other reference divisions L ₄ ~ L ₇ , L _{9 are,} for example, the desired geographic division lines. It is worth noting that these geographical division lines are not limited to straight lines and horizontal lines, but may be diagonal lines in other forms. In more detail, the processing unit 12 first divides the predetermined geographic range into a first sub-range on the left and a third on the right based on the geographic location data D1 and the geographic range data corresponding to the reference division object L ₁ (railway) Two sub-ranges. Then, the processing unit 12 divides the first sub-range into the lower left third sub-range and the first sub-range according to the geographical position data D2, the geographical range data and the geographical position data D1 corresponding to the reference division object L ₂ (river) The fourth sub-range on the upper left; and the second sub-range is divided into the fifth on the lower right based on the geographic location data D3, the geographic range data and the geographic location data D1 corresponding to the reference division object L ₃ (river) The sub-range and the sixth sub-range on the upper right. Next, the processing unit 12 corresponding to the divided object reference data D4 ₄ L of the location, the geographical range information, and the location of such data D1, D2 the third sub-range is divided into a seventh sub-range to the left And the geographic area 33 on the right; the fourth sub-range is divided into the geographic area 34 on the left according to the geographic location data D5 corresponding to the reference division object L ₅ , the geographic range data, and the geographic location data D1, D2 And the geographic area 35 on the right; according to the geographic location data D6 corresponding to the reference division object L ₆ , the geographic range data, and the geographic location data D1, D3 divide the fifth sub-range into the eighth sub-unit on the left The range and the geographical area 38 on the right; and the sixth sub-range is divided into the geographical area on the left according to the geographical location data D7 corresponding to the reference division object L ₇ , the geographical range data, and the geographical location data D1, D3 Area 39 and the geographic area 40 on the right. Finally, the processing unit 12 divides the seventh sub-range into the lower side according to the geographic location data D8 corresponding to the reference division object L ₈ (green space), the geographic range data, and the geographic location data D2, D4 The geographic area 31 and the upper geographic area 32 (in this example, the geographic area 32 and the reference division object L ₈ have the same area range, that is, the slash area range in FIG. 3); and the object is divided according to the reference the geographic data D9 ₉ L, which is the geographical range information, and the location of such data D3, D6 eighth sub range is divided into the geographic region of the lower side 36 and upper side 37 of the geographical area. Therefore, the processing unit 12 divides the predetermined geographic range into the geographic regions 31-40 (ie, Q=10). Hereinafter, these geographic areas 31 to 40 are referred to as the first to tenth geographic areas, respectively.

Then, in step S23, the processing unit 12 stores the P geographical location data and Q indexes different from each other and assigned to the Q geographical regions, respectively, in a binary tree with P non-terminal nodes and Q leaf nodes 111, and the binary tree 111 is stored in the storage module 2. It is worth noting that the P non-terminal nodes store the P geographic location data and the Q leaf nodes store the Q indexes, respectively.

For example, following the example of FIG. 3, the processing unit 12 has first assigned 10 (Q=10) indexes P1 to P10 to the first to tenth geographic regions 31 to 40, respectively. Therefore, referring to FIG. 4, the binary tree 111 stored by the processing unit 12 has 9 (P=9) non-terminal nodes 411 to 419 and 10 leaf nodes 421 to 430, and the non-terminal nodes 411 to 419 are stored respectively There are the first to ninth geographic data D1~D9

，m _i代表該M筆歷史交易資料其中被分到第i組的歷史交易資料的筆數，且該m _i筆歷史交易資料其中每一者所含的該地理座標位於第i個地理區域。更明確地說，該處理單元12藉由將每筆歷史交易資料所含的該二維地理座標以逐層方式與該儲存模組11所儲存的該二元樹111的該P個非終端節點所儲存的該P筆地理位置資料其中的部份相關者比對，如此可經由該二分樹111追蹤至其中一個葉節點(及該Q個葉節點其中的一者)，以致該筆歷史交易資料會被分到該Q組中相關於指派有該葉節點所儲存的該索引的該地理區域的一組。 Afterwards, in step S24, the processing unit 12 obtains the M historical transaction data through, for example, downloading from the Internet, according to the binary tree 111 stored in the storage module 11 and the M historical transaction data M Geographic coordinates, divide the M historical transaction data into Q groups related to the Q geographic regions, where M=

, M _i represents the number of historical transaction data in the M group of historical transaction data, and the geographical coordinates contained in each of the _mi historical transaction data are located in the i-th geographical area. More specifically, the processing unit 12 determines the two non-terminal nodes of the binary tree 111 stored in the storage module 11 by layer-by-layer with the two-dimensional geographic coordinates contained in each historical transaction data. Some of the related data in the P geographical location data stored are compared, so that it can be traced to one of the leaf nodes (and one of the Q leaf nodes) through the binary tree 111, so that the historical transaction data will be A group in the Q group that is related to the geographic area to which the index stored by the leaf node is assigned.

For example, following the example of FIG. 3 and FIG. 4, if the geographical coordinates contained in a piece of historical transaction data are successively layered with the non-terminal nodes 411, 413, 417, the first, third, and seventh When the geographic location data D1, D3, and D7 are compared and tracked to the leaf node 429, the historical transaction data will be assigned to the ninth related to the index P ₉ assigned to the leaf node 429. A group of geographical areas 39.

Finally, in step S25, the processing unit 12 according to the constructional information m _i pen historical transaction data contained in the group i, to generate a correlation of the i-th reference geographic area 112, and 112 stored in the reference The storage module 11 is located at a storage location uniquely corresponding to the index assigned to the i-th geographic area, where i = 1, 2, ..., Q. Therefore, a total of Q pens corresponding to the reference data 112 of the Q geographic regions are stored by the processing unit 12 in the storage module 11 and in Q different storage locations. It is worth noting that, in this embodiment, each reference material 112 includes one or more reference building types corresponding to one or more different reference buildings (e.g. One, but not limited to) the reference price per unit area (for example, the price per ping, but not limited to this), and a number of reference depreciation coefficients that are related to a number of different housing age ranges. For example, for each reference data 112, the reference price per unit area of each reference building type is, for example, the price per unit area related to the reference building type in the historical transaction data of the corresponding group ( Transaction price/total area), but not limited to this; and these reference depreciation coefficients can be generated based on, for example, different first and second reference depreciation coefficients and using interpolation, but not as limit. For example, the first reference depreciation coefficient can be obtained by dividing the average value of the sum of all unit prices in the corresponding group's historical transactions data whose house age is less than 5 years by the corresponding group's historical transaction data The average value of all unit area prices in is obtained, and the second reference depreciation coefficient can be obtained by dividing the average value of all unit area prices in the corresponding group of historical transaction data that are older than 30 years by the corresponding The average value of all unit area prices in the historical transaction data of the group is obtained, but not limited to this.

So far, the processing unit 12 has finished executing the data processing method. It is worth noting that, since the geographical features included in the predetermined geographical range have been taken into consideration when dividing the geographical areas, the reference data 112 generated by the data processing system 1 can relatively accurately reflect the geographical areas The characteristics of the price of buildings, for example, the price of buildings adjacent to railways is relatively low, and the price of buildings adjacent to parks or green spaces is relatively high.

When the data processing system 3 is applied to the valuation of a target building falling within the predetermined geographic range, after the processing unit 12 obtains the data of the target building's address, building type, total area and age of the house, etc., The address is first converted into a target two-dimensional geographic coordinate, and according to the target two-dimensional geographic coordinate, a target leaf node can be quickly tracked through the binary tree 111, and then from the target leaf node according to the stored index from the The storage module 11 obtains the reference data 112 stored in the storage location corresponding to the index, so that the reference price per unit area matching the building type and the reference discount coefficient matching the age of the house can be obtained from the reference data 112 , And estimate the price of the target building based on the unit area reference price, total area and the reference discount factor. Since the details of how to calculate the price of the target building is not a feature of the present invention, it will not be repeated here.

In summary, the data processing system 1 of the present invention can use the stored binary tree 111 to group a large number of historical transaction data in an efficient and relatively accurate manner, and according to the buildings contained in each group of historical transaction data The data generates reference material 112 related to the corresponding geographic area. In particular, since the geographical features included in the predetermined geographical range have been taken into consideration when dividing the geographical areas, each reference material 112 can fully reflect the characteristics of the relevant geographical areas in terms of building prices. On the other hand, when evaluating the target building, the data processing system 1 can quickly obtain the index assigned to the geographical area where the target building is located by using the stored binary tree 111, and then from the storage module 11 The reference data 112 located at the storage location corresponding to the index is read. Therefore, the price of the target building can be quickly estimated based on the read reference data 112 and related data of the target building. This contrasts with the relatively time-consuming complex regression algorithm and spatial regression algorithm used by the above-mentioned conventional techniques to perform the estimation process. Therefore, the purpose of cost invention can indeed be achieved.

However, the above are only examples of the present invention, and the scope of implementation of the present invention cannot be limited by this, any simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the content of the patent specification are still classified as Within the scope of the invention patent.

1‧‧‧Data processing system

11‧‧‧Storage module

111‧‧‧ Binary tree

112‧‧‧Reference

12‧‧‧Processing unit

L ₁ ~L ₉ ‧‧‧Reference division object

31~40‧‧‧ (first to tenth) geographic area

411~419‧‧‧non-terminal node

421~430‧‧‧leaf node

P ₁ ~P ₁₀ ‧‧‧Index

Other features and functions of the present invention will be clearly presented in the embodiment with reference to the drawings, in which: FIG. 1 is a block diagram illustrating an embodiment of the building evaluation system of the present invention; FIG. 2 is a flowchart , Exemplarily illustrates how this embodiment performs a data processing method on multiple historical transaction data related to all buildings that have been traded within a predetermined geographic range; FIG. 3 is a schematic diagram that exemplarily shows how this embodiment will An example of dividing the predetermined geographic range into ten geographic regions; and FIG. 4 is a schematic diagram illustrating a binary tree stored in this embodiment and corresponding to the ten geographic regions divided as shown in FIG. 3.

1‧‧‧Data processing system

11‧‧‧Storage module

111‧‧‧ Binary tree

112‧‧‧Reference

12‧‧‧Processing unit

Claims (6)

A data processing method for processing M historical transaction data related to all buildings that have been traded within a predetermined geographic range, and is executed by a computer including a processing unit and a storage module, where M≧2 And each historical transaction data includes the geographical coordinates corresponding to the address of one of the buildings, and the building data indicating the total area, building type, house age, and transaction price of the corresponding building. The data processing method includes The following steps: (A) Obtain the P geographic location data related to P different reference division objects contained in the predetermined geographic range and the geographic range data of the predetermined geographic range, where P≧1; (B) According to The P geographic location data and the geographic range data are divided into Q geographic regions using a binary space division method, where Q≧2 and the Q geographic regions are divided by the P reference division objects Out; (C) store the P geographical location data and Q indexes that are different from each other and are assigned to the Q geographical regions, respectively, in a binary tree with P non-terminal nodes and Q leaf nodes, where the binary The tree is stored in the storage module, and the P non-terminal nodes store the P geographic location data and the Q leaf nodes store the Q indexes, respectively; (D) according to the stored in the storage module The binary tree and the M geographic coordinates of the M historical transaction data divide the M historical transaction data into Q groups that are related to the Q geographic regions, where

, M _i represents the number of historical transaction data in the M group of historical transaction data, and the geographical coordinates contained in each of the _mi historical transaction data are located in the i-th geographical region; and (E) were built according to the historical transaction data T _i m i-th group of data included in generating the i-th related to the geographic region and comprising indicating one or more or a plurality respectively corresponding to each other is different from the reference buildings Type of unit area reference price, and multiple reference data related to multiple reference depreciation coefficients of different house age ranges, and the reference data is stored in the storage module and is uniquely assigned to the first The storage location of the index of i geographic regions, where i=1,2,...,Q. According to the data processing method of claim 1, each reference division object is a geographical feature object or a geographic division line selected from at least a railway, a street, an elevated road, a river, a park, and a green space, wherein, in step (A) In, each geographic location data includes at least one set of two-dimensional coordinate vectors indicating the contour of one of the P geographic feature objects. According to the data processing method described in claim 2, the geographical coordinates contained in each historical transaction data are a two-dimensional geographical coordinate, wherein, in step (D), the processing unit is based on the data contained in each historical transaction data The two-dimensional geographic coordinates are tracked to one of the leaf nodes through the binary tree stored by the storage module, so that the historical transaction data is divided into the Q group related to the index assigned to the index stored by the leaf node A group of geographic areas. A data processing system for processing M historical data related to all buildings that have been traded within a predetermined geographic range, where M≧2 and each historical transaction data includes an address corresponding to one of the buildings The geographic coordinates of and the building data indicating the total area of the corresponding building, the type of building, the age of the house and the transaction price. The data processing system includes: a storage module; and a processing unit electrically connected to the storage module; Wherein, when the processing unit receives the M historical transaction data and P pen related geographic location data of P different reference division objects contained in the predetermined geographic range, and geographic range data of the predetermined geographic range , Where P≧1, the processing unit divides the predetermined geographic range into Q geographic regions based on the P geographic location data and the geographic range data and using a binary space segmentation method, where Q≧2 and the Q geographic regions Is divided by the P reference division objects, and the P geographical location data and the Q indexes that are different from each other and assigned to the Q geographical regions are stored in a two with P non-terminal nodes and Q leaf nodes Meta tree, wherein the binary tree is stored in the storage module, and the P non-terminal nodes store the P geographical location data and the Q leaf nodes store the Q indexes, respectively, according to the storage in the storage The binary tree of the module and the M geographic coordinates of the M historical transaction data divide the M historical transaction data into Q groups that are respectively related to the Q geographic regions, where

, M _i represents the number of historical transaction data in the M group of historical transaction data, and the geographical coordinates contained in each of the _mi historical transaction data are located in the i-th geographical region, And based on the building data contained in the i-th group of m _i historical transaction data, generate the one corresponding to the i-th geographical area and containing one or more reference building types corresponding to one or more different reference building types respectively Reference price per unit area, and multiple reference data related to multiple reference depreciation coefficients of different age ranges, and store the reference data in the storage module in a unique correspondence to the i-th geographic location The storage location of the index of the area, where i=1,2,...,Q. According to the data processing system described in claim 4, each reference division object is a geographic feature object or a geographic division line selected from at least railways, streets, elevated roads, rivers, parks and green spaces, where each geographic location data Contains at least one set of two-dimensional coordinate vectors indicating the contour of one of the P geographical feature objects. According to the data processing system of claim 5, the geographic coordinates contained in each historical transaction data are a two-dimensional geographic coordinate, wherein the processing unit passes the two-dimensional geographic coordinates included in each historical transaction data via the The binary tree stored by the storage module is tracked to one of the leaf nodes, so that the historical transaction data is divided into a group in the Q group related to the geographic area assigned with the index stored by the leaf node.

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