Building identification method, device and system and storage medium

Abstract

The invention discloses a building identification method, device and system, and a storage medium. The building identification method comprises the following steps: S1, obtaining geographic space information of buildings in a plurality of data sources; s2, coding the building according to the geographic space information to obtain a building grid coding set table; wherein the building grid code set table comprises a building ID of each data source and a grid code corresponding to the building ID; s3, according to the building grid coding set table, obtaining a spatial relation data table oriented to a geographic grid; wherein the spatial relation data table comprises all data source building ID sets and the number of corresponding overlapped grids; and S4, according to the spatial relationship data table oriented to the geographic grid, calculating the coincidence condition and degree of the corresponding buildings in the plurality of data sources so as to realize building matching. By adopting the technical scheme of the invention, the contradiction between the accuracy of code identification and the high efficiency of code matching is balanced to the greatest extent.

Description

Technical Field

[0001] This invention belongs to the field of information processing technology, and in particular relates to a building identification method, device, system, and storage medium. Background Technology

[0002] Data synchronization and sharing using buildings as digital space carriers plays a crucial role in communication and energy management. Buildings, as the basic unit, can hold and link multiple datasets. Research shows that linking records within the same dataset or across datasets faces significant challenges in the following three situations: 1) files lack unique identification codes; 2) information is recorded in non-standardized formats; 3) files are massive in size. Therefore, to accurately link building-related data, it is necessary to identify buildings or their parts through foreign keys (i.e., fields that uniquely identify records in relational databases). However, in a globalized context, due to differences in how different institutions, countries, or regions use address codes or local numbering systems (locally) to identify buildings, the integration of non-standardized information across data sources has become a major obstacle to knowledge and experience sharing. Simultaneously, the rapid development of urban construction has led to increasingly faster building updates. To improve data tracking and sharing efficiency, some regions have developed localized land or building identifiers, but their application is limited to specific geographical areas and requires substantial administrative resources for database maintenance.

[0003] Integrating multi-source building data can improve data quality and unlock potential value. Combined with efficient data matching, processing, and analysis capabilities, this will significantly improve market operational efficiency. To meet the needs of current practical business operations, a good building identification system needs to meet the following two conditions: 1) It must possess globally unique identifiers, ensuring that multi-source data using buildings as digital spatial carriers can be correctly associated and retrieved; 2) Based on these globally unique identifiers, the matching rate of building data from different sources must meet business requirements, i.e., high matching accuracy, acceptable matching speed, and a sound matching mechanism. Condition 1) is a prerequisite for condition 2).

[0004] Building identification systems have evolved from systems that do not include geospatial information (address-based) to systems that do include geospatial information. In the early days of human society, before the full development of geographic information science, street addresses were used for centuries as a way for humans to locate places in the physical world. They were the most common building identification system and were often used as foreign keys for matching building data across datasets.

[0005] The fundamental limitation of traditional street address systems stems from the excessive reproducibility of text names and the high complexity of expressing precise information (such as geographical location), leading to complex identification and matching difficulties. However, with the development of geographic information science, it has been discovered that geospatial representation uniquely identifies buildings in physical space and has a natural advantage in managing matching by using space as the primary key for geographical features.

[0006] Geocoding is the process of converting location descriptions (such as coordinates or addresses) into specific locations on the Earth's surface (such as latitude and longitude coordinates). Therefore, establishing a building identification system by directly associating geocoding with building addresses can alleviate the problems of text-based naming systems. However, such geocoding-based building identification systems significantly reduce the matching speed and accuracy of text-based address matching (different buildings do not physically overlap), but similar shortcomings still arise when aiming for more accurate matching. The root cause of these problems is that simple geocoding (such as latitude and longitude coordinates and single-scale geographic grids) does not store the physical form information of buildings. This leads to identification and matching relying solely on simple regular grids or even latitude and longitude point coordinates as a carrier, making it impossible to uniquely identify buildings with diverse shapes and scales.

[0007] Therefore, some strategies have upgraded geocoding-based building identification systems by adding certain building morphology information to increase the high-resolution uniqueness of the identifiers. However, these methods emphasize the local or global uniqueness of building identifiers, neglecting the subsequent matching issues based on building data from different sources and the efficiency of the matching process. Therefore, the concept of a flexible grid, theoretically integrating a flexible structure with a global discrete grid framework, has great potential for improving the global uniqueness of building identification and enhancing matching efficiency.

[0008] In summary, the current building identification system suffers from poor global compatibility and the need to improve high-precision uniqueness. It also suffers from slow matching, poor accuracy, and low efficiency based on building identification, which is a contradiction between the accuracy of the coded identification and the efficiency of the coded matching. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a building identification method, device, system, and storage medium.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: A building identification method, comprising: Step S1: Obtain geospatial information of buildings from multiple data sources; Step S2: Encode buildings based on geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; Step S3: Based on the building grid code set table, obtain the spatial relationship data table oriented towards the geographic grid; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; Step S4: Based on the spatial relationship data table oriented towards the geographic grid, calculate the overlap and degree of corresponding buildings in multiple data sources to achieve building matching.

[0011] Preferably, in step S2, the buildings are GBC-coded based on geospatial information to obtain a flexible GBC code set table for the buildings.

[0012] Preferably, step S3 includes: Based on the building grid code set table, the coding primary key data table is obtained; the coding primary key data table includes: the grid code corresponding to the building ID of each data source and the set of building IDs of all corresponding data sources; Based on the coded primary key data table, a spatial relationship data table oriented towards the geographic grid is obtained.

[0013] Preferably, in step S4, based on the spatial relationship data table oriented towards the geographic grid, the overlap and degree of corresponding buildings in multiple data sources are calculated through the primary key and auxiliary key relationship table to achieve building matching in different data sources.

[0014] The present invention also provides a building identification device, comprising: The first processing module is used to obtain the geospatial information of buildings from multiple data sources; The second processing module is used to encode buildings based on geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; The third processing module is used to obtain a spatial relationship data table oriented towards the geographic grid based on the building grid code set table; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; The fourth processing module is used to calculate the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid, so as to achieve building matching.

[0015] Preferably, the second processing module performs GBC on the building based on geospatial information to obtain a flexible GBC code set table for the building.

[0016] Preferably, the third processing module includes: The first processing unit is used to obtain a coding primary key data table based on the building grid code set table; wherein, the coding primary key data table includes: the grid code corresponding to the building ID of each data source and the corresponding set of building IDs of all data sources; The second processing unit is used to obtain a spatial relationship data table oriented towards the geographic grid based on the coded primary key data table.

[0017] As a preferred option, the fourth processing module calculates the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid, through the primary key and auxiliary key relationship table, so as to achieve building matching in different data sources.

[0018] The present invention also provides a building identification system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program performs a building identification method when executed by the processor.

[0019] The present invention also provides a storage medium storing a computer program that executes a building identification method when running.

[0020] Compared with the prior art, the present invention has the following characteristics: 1) Design a global building coding system based on a flexible grid framework to ensure both the globality of building identification and the simplicity of the coding; 2) The building's shape was described and recorded using an elastic grid structure, enabling it to have better high-precision uniqueness; 3) It achieves both high uniqueness and high matching accuracy and efficiency, thus maximizing the balance between the precision of the encoded identifier and the efficiency of the encoded matching. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a flowchart of a building identification method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the GBC encoding structure; Figure 3 Intended representation of spatial relationship data oriented towards geographic grids; Figure 4Schematic diagram of building misalignment types from different data sources; Figure 5 Matching flowcharts for buildings. Detailed Implementation

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Example 1: like Figure 1 As shown, an embodiment of the present invention provides a building identification method, including: Step S1: Obtain geospatial information of buildings from multiple data sources; Step S2: Encode buildings based on geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; Step S3: Based on the building grid code set table, obtain the spatial relationship data table oriented towards the geographic grid; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; Step S4: Based on the spatial relationship data table oriented towards the geographic grid, calculate the overlap and degree of corresponding buildings in multiple data sources to achieve building matching.

[0026] In one embodiment of the present invention, in step S1, the building is defined as follows: 1. The geospatial information of buildings does not include other types of land features (such as community gardens). The planar building areas under the same data source do not overlap, or in other words, multiple buildings that are close enough to each other are regarded as the same building. In the implementation of this invention, only 2D building data is considered, and their projection on the Earth's surface, also known as building area, do not intersect each other.

[0027] 2. According to the general rule that the building area of ​​a building needs to be greater than 20 square meters, since the implementation of this invention uses a rectangular grid, it is defined that if the minimum outer rectangle of the building's surface geographic information element has one side less than 4m, it is considered a building. In addition, the current largest building in the world has a land area of ​​1.76 million square meters. Therefore, in the implementation of this invention, it is defined that if the minimum outer rectangle of the building's surface geographic information element has one side greater than 2km, it is not considered a building.

[0028] 3. For buildings in multiple data sources, taking two data sources as an example, if the ratio R (defined as the degree of overlap) of the overlapping area of ​​buildings to the area of ​​any building in the two data sources is less than the threshold T (T≥50%), then they are considered as two buildings.

[0029] In one embodiment of the present invention, in step S2, buildings are GBC encoded based on geospatial information to obtain a building GBC set table. The flexible GBC encoding consists of two parts: a reference code selected from the Discrete Global Grid System (DGGS) and a span code. The span code further comprises a span including both radial and latitudinal directions. and Its composition, specifically manifested in binary. The encoding format can also be converted into hexadecimal text format. Specifically, the location code is the global discrete grid code of the building's grid, which contains the lower left corner of the building's smallest outer rectangle. The length of this code is determined by the grid division and encoding rules, as well as the hierarchical range that can cover all buildings in the world. and These represent the number of grid cells (span) occupied by the minimum bounding rectangle of the building's surface feature in the radial and latitudinal directions at a certain discrete grid level, respectively. The maximum number of grid cells is limited to 8, thus each occupies a fixed-length 3-bit binary code. In this embodiment of the invention, the global discrete grid system can use any square grid with multi-scale characteristics. According to the definitions of the largest and smallest buildings, a certain level containing any rectangular unit with a side length of 3-5 meters under the projection plane, or a grid with a side length ranging from 1 / 64″ to 1″ (inclusive) of latitude and longitude, is used as the starting grid level (Base Level). Assuming the grid coding length at this level is Gbit, then the GBC in this embodiment of the invention is a fixed-length... binary encoding, such as Figure 2 As shown in (d).

[0030] The overall encoding process is as follows: Figure 2As shown in (a), (b), (c), and (d), the first step is to determine the encoding of the global multi-scale Base level grid (grid size 4m) where the lower left corner of the building's minimum outer rectangle is located, as the starting location code. Then, obtain the number of cells that the building spans in the longitude direction at the Base Level. The number of grids spanned in the latitudinal direction ,Pick At this time At that time, the building was used As encoding; and when At this point, the grid level is downgraded (scale increases), from Base level to level 1 (grid size is 8m), and the ReferenceCode is updated to... And obtain at this level and ,get If at this time If the value is still greater than or equal to 8, then continue to increase the level. Repeat this process N times until the condition is met. Until then, the final GBC binary form of the building is obtained as follows: It can be converted to hexadecimal. express.

[0031] By combining a partitioned global discrete grid system and a flexible grid structure, the coding of individual buildings incorporates shape features and location information, enabling globally unique coding identification of buildings with minimal memory usage.

[0032] As one embodiment of the present invention, such as Figure 3 As shown, step S3 includes: Based on the building grid code set table, the coding primary key data table is obtained; the coding primary key data table includes: the grid code corresponding to the building ID of each data source and the set of building IDs of all corresponding data sources; Based on the coded primary key data table, a spatial relationship data table oriented towards the geographic grid is obtained.

[0033] Furthermore, the building grid code set table includes: building IDs a1 and a1 of data source A and their corresponding grid codes {GCode}. n1 GCode n2 GCode n3}、{GCode m1 GCode m2 GCode m3}, Building IDs b1 and b1 and their corresponding grid codes {GCode} of data source A n2 GCoden3 GCode n4}、{GCode m1 GCode m3 Matching buildings from multiple data sources requires determining their spatial relationships, i.e., whether they intersect or are disjoint (connected areas are considered intersecting). If they intersect, the degree of intersection, i.e., the area of ​​overlapping polygon features, needs to be calculated. If an object-oriented matching method is used, common spatial indexing methods are needed to quickly determine the surrounding polygon features, and then vector equation solving methods are used to refine the determination of whether they intersect and the area of ​​overlap. However, after discretizing the vector polygon features using a fixed-size geographic grid, the data representation of each building is described as a set containing several finite grid codes, such as... Figure 3 As shown in (a), the determination of spatial relationships between building elements can then be transformed into a calculation of whether there is an intersection in the grid sets they represent. This involves reconstructing the building codes into a pairing relationship, that is, extracting information on overlapping relationships from the grid codes of all buildings across multiple data sources, and constructing a spatial relationship data table, such as... Figure 3 As shown in (b) and (c). This relationship table not only records whether buildings in multiple data sources intersect (right column of the table), but also directly obtains their intersecting area (grid area is known and consistent) by statistically analyzing this intersection relationship. This process does not require looping, which greatly shortens the matching time. For buildings a and b in two data sources A and B, the number of overlapping grids obtained from the spatial relationship data table is O, and the number of grid code sets corresponding to a and b obtained from the building grid code set table is the total number of grids for that building, P and Q. The grid area is calculated as follows: , , GS represents the area of ​​a DGGS grid cell at a certain level.

[0034] In one embodiment of the present invention, in step S4, the overlap and degree of corresponding buildings in multiple data sources are calculated based on the spatial relationship data table oriented towards the geographic grid, thereby obtaining the matching relationship of buildings in multiple data sources. The present invention first defines that if the area T (%) of building X in one data source overlaps with building Y in another data source, then X largely overlaps with Y; if less than T (%) of the area overlaps with it, then X partially overlaps with Y. Based on this, the geographic misalignment of building data from different data sources is summarized and categorized into 6 types (taking two data sources as an example, such as...). Figure 4(As shown). Type 1 is divided into two subtypes. Subtype 1 is the case where two buildings overlap well. Theoretically, in this case, they can be assigned the same GBC code in both systems during the first step of matching. However, due to the rigidity of the global discrete grid itself, it is inevitable that some building elements with good overlap but assigned different GBCs will be missed. This definition of good overlap is based on definition 3 of buildings, that is, the overlap degree R must be higher than the assumed threshold T (%) to be considered the same building, but subtype 2 is an exception: even if the overlap degree of two buildings is lower than T (%), if there is no other second building overlapping with it in this data source, it is still considered the same building. Therefore, Type 1 is actually a supplement and extension to the case where the same GBC is not assigned in the first step of the matching process in both data sources. When it is determined to be Type 1, this invention selects the GBC of any one of the data sources as GBC_Base and corrects the corresponding building code of the other data source to GBC_Base. Type 2 is divided into three subtypes: the case of partial overlap and non-unique matching, the case of separation, and the case of no nearby matching. This type of relationship is defined arbitrarily, and in practice, the assumed threshold T(%) can be adjusted based on specific situations and experience. Type 3 is a one-to-many relationship, and its algorithm design is not significantly different from that of Type 4 (many-to-one). Type 5 is a more complex many-to-many relationship, which in a sense overlaps with Type 3 and Type 4, but the presentation is different. Type 6 involves situations where X largely overlaps with Y, but Y partially overlaps with X. This could be due to different data sources (e.g., half of the buildings are damaged) or severe sampling errors, making it impossible to determine whether they truly represent the same building at a low cost. Therefore, this paper adopts a conservative matching principle: it also associates corresponding data from another data source that does not share the same GBC but has a high degree of overlap. Whether the association is meaningful is then determined manually in the specific scenario.

[0035] like Figure 5 As shown in (a), in step S4, taking two data sources as an example, building a1 is taken from data source A and building b1 is taken from data source B; the building matching process includes: Calculate the area S of a1 and b1 based on the building grid code set table and spatial relationship data table. a1 、S b1 and their overlapping area ; if and (Condition 1) then the matching type is subtype 1 of type 1, that is, buildings a1 and b match and have the same GBC primary key, set as aGBC1, with no auxiliary key; If condition 1 is not met, but condition 2 is met or (Condition 2) then buildings a1 and b match, but their primary keys are different; If neither condition 1 nor condition 2 is met, but a building b2 exists that belongs to data source B, (Condition 3) then the matching type is type 2, that is, buildings a1 and b do not match, their primary keys are aGBC1 and bGBC1 respectively, and their auxiliary keys do not contain the primary key of the other. If conditions 1, 2, and 3 are not met, then the subtype 2 of type 1 is: buildings a1 and b are uniquely matched, and their primary key is set to aGBC1. If condition 1 is not met, but condition 2 is met, and there exists a building b2 belonging to data source B, (Condition 4) then the matching type is type 3, that is, a1 auxiliary code: bGBC1, bGBC2, ...; If condition 1 is not met, but condition 2 is met, and condition 4 is not met, but there exists a building a2 belonging to data source A, (Condition 5) then the matching type is type 6, that is, buildings a1 and b match but have different GBC primary keys, denoted as aGBC1 and bGBC1, which are auxiliary keys to each other; If condition 1 is not met, but condition 2 is met, and conditions 4 and 5 are not met, then the matching type is type 4, that is, b1 auxiliary code: aGBC1, aGBC2, …….

[0036] Type 5 is obtained by combining the judgments of Type 3 and Type 4.

[0037] Regarding the final data presentation, considering the existence of six possible scenarios, if we use the building data source from one of the data sources as the base reference data source, we can use the GBCs of all buildings in that data source as the primary key to match the relationships between buildings in other data sources. The GBCs of buildings in the base reference data source are already globally unique during encoding. However, due to the prevalence of one-to-many, many-to-one, and many-to-many relationships among buildings based on the six matching types, the same base reference building may have more than one corresponding building (i.e., matched) in another data source. Therefore, the concepts of Primary GBC and Auxiliary GBC are introduced to express this relationship, such as... Figure 5As shown in (b), the primary key is a unique, non-repeating key in this data structure, while auxiliary keys can be repeated, and there may be more than one. Thus, by establishing a relationship table between primary and auxiliary keys, the matching of buildings based on GBC encoding from different data sources is finally completed. This invention is applicable to multiple data sources. When there are more than two data sources, it is still essential to determine one as the basic reference, and then perform pairwise matching with the others. This relationship table can be expanded to include multiple auxiliary key types to correspond to other data sources.

[0038] Example 2: This invention also provides a building identification device, comprising: The first processing module is used to obtain the geospatial information of buildings from multiple data sources; The second processing module is used to encode buildings based on geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; The third processing module is used to obtain a spatial relationship data table oriented towards the geographic grid based on the building grid code set table; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; The fourth processing module is used to calculate the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid, so as to achieve building matching.

[0039] As one embodiment of the present invention, the second processing module performs GBC on the building based on geospatial information to obtain a flexible GBC code set table for the building.

[0040] As one embodiment of the present invention, the third processing module includes: The first processing unit is used to obtain a coding primary key data table based on the building grid code set table; wherein, the coding primary key data table includes: the grid code corresponding to the building ID of each data source and the corresponding set of building IDs of all data sources; The second processing unit is used to obtain a spatial relationship data table oriented towards the geographic grid based on the coded primary key data table.

[0041] As one embodiment of the present invention, the fourth processing module calculates the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid and through the primary key and auxiliary key relationship table, so as to achieve building matching in different data sources.

[0042] Example 3: This invention also provides a building identification system, including: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a building identification method when run by the processor.

[0043] Example 4: This invention also provides a storage medium storing a computer program that executes a building identification method during runtime.

[0044] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for identifying buildings, characterized in that, include: Step S1: Obtain geospatial information of buildings from multiple data sources; Step S2: Encode buildings based on geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; Step S3: Based on the building grid code set table, obtain the spatial relationship data table oriented towards the geographic grid; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; Step S4: Based on the spatial relationship data table oriented towards the geographic grid, calculate the overlap and degree of corresponding buildings in multiple data sources to achieve building matching.

2. The building identification method as described in claim 1, characterized in that, In step S2, GBC is performed on the buildings based on geospatial information to obtain a flexible GBC code set table for the buildings.

3. The building identification method as described in claim 1 or 2, characterized in that, Step S3 includes: Based on the building grid code set table, the coding primary key data table is obtained; the coding primary key data table includes: the grid code corresponding to the building ID of each data source and the set of building IDs of all corresponding data sources; Based on the coded primary key data table, a spatial relationship data table oriented towards the geographic grid is obtained.

4. The building identification method as described in claim 3, characterized in that, In step S4, based on the spatial relationship data table oriented towards the geographic grid, the overlap and degree of corresponding buildings in multiple data sources are calculated through the primary key and auxiliary key relationship table to achieve building matching in different data sources.

5. A building identification device, characterized in that, include: The first processing module is used to obtain the geospatial information of buildings from multiple data sources; The second processing module is used to encode buildings based on geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; The third processing module is used to obtain a spatial relationship data table oriented towards the geographic grid based on the building grid code set table; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; The fourth processing module is used to calculate the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid, so as to achieve building matching.

6. The building identification device as described in claim 5, characterized in that, The second processing module performs GBC on the buildings based on geospatial information to obtain a flexible GBC code set table for the buildings.

7. The building identification device as described in claim 5 or 6, characterized in that, The third processing module includes: The first processing unit is used to obtain a coding primary key data table based on the building grid code set table; wherein, the coding primary key data table includes: the grid code corresponding to the building ID of each data source and the corresponding set of building IDs of all data sources; The second processing unit is used to obtain a spatial relationship data table oriented towards the geographic grid based on the coded primary key data table.

8. The building identification device as described in claim 7, characterized in that, The fourth processing module calculates the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid, through the relationship table of primary and secondary keys, so as to achieve building matching in different data sources.

9. A building signage system, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the building identification method as described in any one of claims 1 to 4 when executed by the processor.

10. A storage medium, characterized in that, The storage medium stores a computer program that, when executed, performs the building identification method as described in any one of claims 1 to 4.

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