216 results about "Geographic entity" patented technology

The invention provides a land geographic surveying and mapping model generation method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a geographic surveying and mapping data set containing a satellite remote sensing image, surface elevation and the like in a target region, then carrying out the topographic feature interpretation, and generating a topographic semantic, surface coverage and surveying and mapping task correlation feature set; then calling a geographic space encoder to carry out multi-scale space modeling on the related feature set to obtain a geographic space representation vector set, fusing the geographic space representation vector set with a preset geographic entity topological graph dynamic feature to generate a geographic entity related feature set, and carrying out multi-scale space modeling on the geographic entity related feature set; and finally, training a geographic surveying and mapping analysis model according to the geographic entity associated feature set and the surveying and mapping task associated feature set, generating a geographic surveying and mapping optimization strategy, and feeding back the geographic surveying and mapping optimization strategy to a surveying and mapping terminal to trigger surveying and mapping parameter dynamic adjustment operation so as to realize more optimized land geographic surveying and mapping model generation.

The invention discloses a real-time place name and address multi-language translation system and method based on a neural network model, and relates to the technical field of multi-language translations, and the method comprises the steps: firstly extracting a topological relation of address components through structured analysis, and then carrying out the deep modeling of cross-component context dependence through employing a deep learning-based neural network model, deep semantic representation of the address text is mined through cross-component semantic association learning, and place name disambiguation based on dynamic semantic reasoning is realized based on the deep semantic representation; according to the method, the problem of semantic deviation in complex scenes such as homonymy, different places and different names is solved, the accuracy of judging the ambiguity of geographic entities is remarkably improved, the translation result can dynamically adapt to place name expression habits of different languages, translation ambiguity caused by limitation of a static knowledge base or imperfect rules is effectively eliminated, and the translation efficiency is improved. And a more accurate multi-language address conversion effect is achieved.

The embodiment of the invention provides a processing method and device of a geographic language model, a program product and a storage medium. The method comprises the steps that geographic information of the following three types of geographic entities in a preset geographic range is obtained, wherein the three types of geographic entities include a point of interest (POI), an area (AOI) and a road Road; constructing data representation of each POI according to geographic information of each geographic entity; wherein the data representation of the POI comprises an address description text, longitudes and latitudes, and association information for describing an AOI and / or a Road having an association relationship with the POI; the association relationship comprises an inclusion relationship representing that the AOI contains the POI and a proximity relationship representing that the AOI is close to the POI or the Road is close to the POI; and training a preset geographic language model based on the data representation of each POI.

The invention provides a method and system for constructing a geographic entity based on a DLG topographic map, and the method comprises the steps: taking the DLG topographic map as a data basis, and carrying out the objectification processing of basic geographic information elements; the method comprises the following steps: constructing a data graph based on an association relationship between geographic information elements and a target geographic entity in dimensions such as spatial scale, geometric morphology, geographic features, attribute expression and the like, belonging to a mapping conversion standard, and extracting an intermediate entity library from an objectified result library; entity construction operators are assembled according to needs to complete entity production scheme configuration, processing such as assembly and fusion is carried out on primitive objects in the middle entity library based on an entity production scheme, and rapid production and construction of geographic entity achievements are achieved.

The invention belongs to the technical field of natural language processing and geographic information systems, and particularly relates to a region address standardization method based on knowledge graph enhanced retrieval. Cleaning and preprocessing the original address text input by the user; utilizing a fine-tuned large language model to identify a geographic entity and performing standardized expansion on variant expression to generate a query candidate set; entity linking and context retrieval are carried out based on the knowledge graph, and attributes, hierarchy and spatial topology information of associated entities are obtained; in combination with the original input and the map context, an enhanced retrieval query text is generated through large language model reconstruction; vectorized semantic retrieval is carried out through the fine-tuned embedding model, and a preliminary candidate address set is obtained; carrying out multi-dimensional refined sorting by adopting a resorting model; and generating a structured standard address by using a large language model, and outputting the structured standard address after multi-level verification. According to the method, the problems of ambiguity resolution, alias recognition and context understanding in address processing are solved, and the accuracy and robustness of address standardization are improved.

The invention provides a geographic knowledge complex question-answering method based on a space-time agent architecture. The geographic knowledge complex question-answering method comprises the following contents: natural language analysis: accurately understanding intentions of users and information contained in questions; multi-modal geographic entity recognition: processing non-text data contained in user input; generating a WKT format and injecting a prompt; knowledge graph retrieval enhancement; in the spatial computing tool chain arrangement, an LLM generates a problem solving scheme after combining multi-modal information, WKT geometry and related data resources associated with a knowledge graph, tools or functions needing to be called and an execution sequence of the tools or the functions are listed, and the series of tools adopt a model context protocol; in answer generation and response, the method can effectively solve the problems that in the prior art, when complex geographic questions and answers are processed, question input recognition is incomplete, reasoning steps are not accurate, and the answer basis is not real, and more intelligent and reliable question and answer support is provided for the field of natural resource planning and the like.

The invention discloses a geographic information data fusion system, and relates to the technical field of geographic information data processing, the system comprises the following components: a data acquisition and preprocessing unit, a deep learning semantic analysis model construction unit, a semantic level data fusion unit and a data application and visualization unit; according to the method, semantic relationship reasoning is carried out on geographic entities by using a Geo-SDNN model through the deep learning semantic analysis model building unit, the complex semantic relationship between the geographic entities can be accurately captured, and meanwhile, the semantic level data fusion unit adopts a fusion algorithm based on semantic hierarchy, so that the data fusion efficiency is improved. In addition, a multi-source data acquisition interface and a data quality evaluation module of the data acquisition and preprocessing unit effectively improve the quality of basic data, the accuracy of data fusion is further improved, the data processing flow is optimized through the overall design of the system, and the data fusion efficiency is improved. And the efficiency of geographic information data fusion is obviously improved.

The invention discloses a multi-source heterogeneous geographic data processing method based on a deep fusion algorithm, and the method comprises the steps: collecting multi-source heterogeneous geographic data, and writing the multi-source heterogeneous geographic data into a unified index database; performing coordinate reference normalization, time sequence completion and integrity-correctness joint verification to obtain a standardized geographic data block; semantic-space joint matching is carried out, and candidate cross-modal entity pairs are generated; constructing a cross-modal attention network, extracting fusion features and calculating a posterior fusion confidence coefficient; selecting a fusion result with the highest confidence coefficient from each candidate cross-modal entity pair as a target geographic entity, and generating a unique fusion identifier; and writing the target geographic entity with the unique fusion identifier into a fusion database to obtain a unified geographic entity data set. According to the method, the problems of low data fusion quality, insufficient credibility in the fusion process and difficulty in cross-modal feature matching in the prior art are solved, and the fusion precision, efficiency and credibility are improved.

The invention discloses a geographic entity knowledge graph generation method considering spatio-temporal characteristics and a related device, and relates to the technical field of knowledge engineering.According to the method, a multi-layer geographic entity knowledge graph structure model comprising a concept layer, an entity layer and a relation layer is established, and a'code-scale-entity-relation 'knowledge representation method is integrated in the model; a unique spatial identity code of a geographic entity is introduced to support invariable identification of any geographic entity under scale or time sequence change; by expanding the same geographic entity node into geographic entity sub-nodes in different states, formalized description of the geographic entity scale and process features is ingeniously realized, and specific spatial-temporal features and geoscience mechanisms of geographic entity knowledge can be reflected.

The invention belongs to the technical field of surveying and mapping and geographic information processing, and discloses a geographic entity intelligent identification and reconstruction system based on multi-source surveying and mapping data. The system is composed of a multi-source data acquisition and preprocessing module, a cross-modal feature coding and fusion module, a structural atlas construction and spatial logical reasoning module, a deformable neural modeling module and a physical prior guided collaborative prediction and closed-loop optimization module. According to the method, a cross-modal feature coding and fusion module is arranged, and a modal attention mechanism is introduced to dynamically weight multi-source data, so that heterogeneous information such as a laser point cloud, an inclined image and a multispectral image is fused into a unified coding vector in a high-dimensional space; compared with feature extraction performed by using a static deep network in a comparison file, the method of the invention adopts a minimum residual function to perform modal weight training, has an adaptive feature integration capability, and effectively improves the accuracy of geographic entity recognition and the robustness of boundary segmentation in different scenes.

The invention discloses a method for constructing a three-dimensional geographic entity and a three-dimensional electronic map, which comprises the following steps of: firstly, classifying acquired basic surveying and mapping data based on a constructed multi-level classification framework, and determining an entity category to which each element in the basic surveying and mapping data belongs; then, according to entity category design mapping data corresponding to the elements, converting the mapping data into a geometric mapping relation and an attribute mapping relation of a two-dimensional geographic entity, and through the geometric mapping relation and the attribute mapping relation, converting the mapping data and the attribute information corresponding to the elements into geographic entity geometry and geographic entity attributes; semantized geographic two-dimensional entities are generated to effectively inherit and structurally reuse stock data, and massive waste and repeated construction of data resources are avoided. And then, selecting a corresponding three-dimensional conversion method according to the type of the two-dimensional geographic entity, and converting the two-dimensional geographic entity into a three-dimensional geographic entity. And finally, constructing a corresponding three-dimensional electronic map according to the three-dimensional geographic entities corresponding to all the elements.

According to the efficient matching method for the GIS multi-scale basic geographic space data, a multi-scale matching scheme based on geographic space database optimization is improved, firstly, a spatial database model based on a relational database and a spatial database engine is used as a method for storing the multi-scale basic geographic space data, and the method is high in storage speed and flexible in application; 2, establishing a new object selection method for multi-scale geographic space data matching, organizing a basic geographic entity in a data structure form of a multi-way tree based on a multi-scale semantic change rule of geographic space data, and establishing a category matching method for semantic change of the same element category of different proportional scales; a category matching relationship under a multi-scale condition is effectively constructed; and thirdly, an optimization scheme for multi-scale basic geographic space data matching is established, the matching effect is good, and the operation efficiency is high. Spatial data fusion can be efficiently realized, the spatial data quality is improved, and the map database updating efficiency is improved.

The invention relates to the technical field of image processing, and discloses a house quality detection method and system based on a live-action three-dimensional model. The method comprises the following steps: converting an OSGB format live-action three-dimensional building model into a body coordinate system and carrying out quality evaluation, carrying out semantic segmentation on a standardized model to obtain a component geographic entity set, establishing an independent local coordinate system of each component to form a single three-dimensional model set, generating a multi-view projection feature image set based on a normal vector distribution planning view angle, and carrying out multi-view projection on the single three-dimensional model set. And detecting defects by adopting a 3D-YOLO-BuildingDefect algorithm, and carrying out inverse transformation to obtain three-dimensional space defect distribution and a quality grade evaluation result. The technical problems that a traditional two-dimensional detection method is insufficient in spatial positioning precision, incomplete in detection coverage rate and insufficient in geometric information utilization are solved, and the spatial positioning accuracy of house quality detection and the comprehensiveness of defect recognition are improved.

The invention discloses a three-dimensional semantic knowledge graph construction method for geographic entity space-time relationship, and relates to the technical field of knowledge graph construction, comprising: acquiring a unified reference geographic entity stream, selecting a mutually covered geographic entity pair set based on XY axis projection overlapping detection, and reading a sensor precision file; determining an up-and-down orientation relation based on a bounding box vertical extreme value; querying a preset layer thickness mapping table to determine a coarse-grained sectioning layer thickness, calculating coarse-grained vertical coverage in the Z-axis direction, defining a crossing depth interval as a coarse-grained vertical index, and comparing the coarse-grained vertical index with a first threshold library to generate a preliminary up-down relationship state tag; and for the ambiguous or envelope state entity pair, determining a local depth range based on a coarse-grained vertical index, performing heavy new sampling by using a fixed fine-grained sectioning layer, generating a second threshold library for rejudgment by revising a threshold, and forming a revised map version. The vertical semantic ambiguity can be eliminated, and the accuracy of judging the upper and lower relationships of the geographic entities is improved.

The invention provides a GIS buffer generation method and system based on fuzzy semantics and geographic constraints, and belongs to the technical field of geographic information systems.The method comprises the steps that fuzzy descriptors, membership values and geographic entity types are input into a pre-trained language-space mapping model, and basic distance parameters are obtained; querying a spatial database based on the geographic entity keywords, obtaining geometric features associated with the geographic entities, and calculating feature lengths corresponding to the geometric features; correcting the basic distance parameter based on the intention direction, the membership value, the geographic entity type and the feature length expressed by the fuzzy description word to obtain a geographic distance threshold; generating an initial two-dimensional buffer polygon based on the geographic distance threshold and the spatial geometric features of the geographic entity; and querying a digital elevation model to obtain topographic data based on the initial two-dimensional buffer region polygon, and generating a three-dimensional buffer region curved surface through a gradient sensitive interpolation algorithm. According to the invention, the accuracy of generating the GIS buffer area is improved.

The invention relates to the technical field of geographic information systems, in particular to a GIS buffer zone parameter inference method based on deep learning, and the method comprises the following steps: obtaining a natural language instruction input by a user; analyzing the natural language instruction to extract a geographic entity keyword and a fuzzy description word; based on the fuzzy description word, determining an initial distance parameter through a pre-trained language-space mapping model; querying a spatial database based on the geographic entity keyword to obtain geographic entity attributes; according to the geographic entity attribute, correcting the initial distance parameter to obtain a corrected distance parameter; a field programmable gate array (FPGA) is used for carrying out parallel calculation on the spatial relation matrix to generate an optimized buffer area; and outputting the optimized buffer area. According to the method, the FPGA is introduced to carry out parallel calculation of the spatial relation matrix, so that the data processing speed and the energy efficiency ratio are remarkably improved, and the real-time generation of the buffer area is realized.

The invention relates to a vector geographic spatial data relation description method, and belongs to the technical field of spatial data management. According to the method, a relation description data structure comprising relation coding values, relation coordinate values, at least two members and role attributes of each member is constructed, relation data is described by utilizing the structure, and the relation coordinate values are used for describing coordinates displayed in a map or positions where text letters appear. The role attributes are used for describing spatial semantics or other business attribute relations of members in the relations, and the members are point elements, line elements, surface elements or relation data in the vector geographic space data. It can be seen that by means of the constructed relation description data structure, the relation between the geographic entities can be described, the vector geographic space data are made to carry semantic information, the relevance between the vector geographic space data can be described more clearly, and relevance analysis of the geographic space data is facilitated.

The invention provides a geographic spatial data management method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a geographic spatial data set of a target region, geographic entity observation records containing time sequence changes, and corresponding spatial position identifiers, and then carrying out the feature extraction of the geographic spatial data set, generating time sequence correlation features and spatial topological features, then calling a pre-trained geographic space analysis model to perform dynamic feature fusion processing on the time sequence correlation features and the spatial topological features, and generating a fused feature set; and generating a geographic space optimization strategy containing a geographic entity state prediction result and resource allocation priority configuration based on the fused feature set, and finally triggering a geographic information system updating operation of the target area according to the geographic space optimization strategy, thereby realizing dynamic management and timely updating of geographic space data. The accuracy, timeliness and effectiveness of geographic space data management are improved.

The invention discloses a real-scene three-dimensional system geographic entity space-time coding method. According to the method, the dynamic sensing capability of the geographic entity state is remarkably improved through an evolution prediction mechanism. Traditional coding mostly focuses on current state records, but the method depends on a state transition probability model of time decay weighting, can accurately capture recent change trends of entities, generate evolution prediction codes and dynamically bind the evolution prediction codes with space codes and time codes, so that the coding can not only reflect current positions, time and attributes of the entities, but also can reflect current state records of the entities. And the state trend of 1-3 months in the future can be pre-expressed. According to the method, the subsequent stage of a construction road can be pre-judged in advance, an emergency department can quickly recognize potential risk entities, the problem that geographic information lags behind actual change is effectively solved, more prospective data support is provided for collaborative decision making of multiple departments, the suitability and long-term reliability of coding in multiple scenes are enhanced through full-process closed-loop design, and the method is suitable for popularization and application. And a stable basic support is provided for efficient application of live-action three-dimensional geographic information.

The invention relates to the technical field of surveying and mapping geographic information science, in particular to a method for quickly constructing a knowledge graph based on structured spatial data, which comprises the following specific steps of: classifying according to expressed entity objects to obtain structured spatial data corresponding to each type of entities, and uniquely coding the structured spatial data of each type of geographic entities to obtain a unique coding result; connecting attribute information corresponding to each type of entity objects according to the codes; extracting different types of entity types, attributes and relation constraints, and analyzing a topological relation between the entities by utilizing mapping of keywords and codes of an attribute table; discovering an implicit relationship between entities through path reasoning or correlation analysis, and storing the mined topological relationship and implicit relationship in CSV; the method comprises the following steps: importing CSV data into a graph database in batches through a Cypher statement to form a structured spatial data RDF triple; and on the basis of the characteristics of strong interactivity, clear entities and relationships and the like of the knowledge graph, rapid retrieval and query of massive entities can be well supported.

The invention provides a three-dimensional geographic entity space-time identification coding method based on a Beidou grid code, and belongs to the field of live-action three-dimensional, and the method employs a classification identification + space identification + time identification structure, and rapidly calculates an outsourcing 3D convex hull of a three-dimensional geographic entity through KD tree pruning and principal component analysis. The method comprises the following steps of: determining a subdivision hierarchy of a maximum inner packet grid by using neighborhood seed growth, calculating an entity space identifier which takes a Beidou three-dimensional grid position code as description by using a six-point method, further calculating an entity time identifier in a form of geographic entity ID + timestamp, realizing accurate identification of a geographic entity three-dimensional space position and time information, and realizing accurate identification of the geographic entity three-dimensional space position and time information. The problems that traditional geographic entity coding is insufficient in identification precision in three-dimensional space and time dimension, data integration is difficult and the like are solved, and the method has extremely high application value in the fields of live-action three dimensions, smart cities, low-altitude economy and the like.

The invention relates to the technical field of geographical name address translation, and particularly discloses a geographical name address translation system and matching method based on artificial intelligence and a special name library. Based on a to-be-translated geographical name address text input by a user, multiple candidate geographical entity information descriptions are extracted from a geographical entity information library; and performing deep context semantic interaction matching analysis on the to-be-translated geographical name address text and the information description of each candidate geographical entity by utilizing a natural language processing technology, and performing translation on the to-be-translated geographical name address text by taking the matched standard geographical name address description of the candidate geographical entity as a basis and combining translation knowledge in a special name library. And performing target language translation matching and standardization processing on the standard place name address description to generate a final target language translation place name address. According to the application, the geographic entity information base is utilized to assist in identifying the fuzzy or non-standard place name address input by the user, so that reliable geographic entity anchoring can be provided for the translation process, and the accuracy and consistency of translation are improved.

The invention discloses a method and a device for generating a movement track of a crowd in a disaster and a medium, and the method comprises the steps: firstly obtaining a geographic entity set, current disaster point information, current shelter information, current position information of residents and the like in a region, and inputting the information and the geographic entity set into a pre-trained track generation model; then, a travel log of the resident is generated according to the identity type of the resident, then the travel log is mapped to a geographic entity set, and an initial target location set can be obtained; on the basis, the preliminary target location set is further screened, and a more accurate travel target location set can be obtained. By integrating the current disaster point information, the shelter information, the current position of the resident and the travel target location set, the movement track of the resident in the disaster period can be obtained. According to the embodiment of the invention, accurate prediction of crowd flow in a disaster scene can be realized, so that the quality and efficiency of emergency response in a disaster are improved.

In order to improve the efficiency and accuracy of place name address search in a complex semantic environment, the invention discloses an address search method for enhancing LLM based on a spatial relation knowledge graph. According to the method, place name addresses and related information contained in a Chinese natural language under complex semantics (nested spatial relationship semantics) can be converted into spatial positions of the addresses or geographic entities related to the positions. According to the method, geographical name and address information extraction in a Chinese natural language is focused on, and complex spatial relationship semantic geographical name and address contained in the Chinese natural language are analyzed by utilizing the analysis capability of a large language model on the Chinese natural language and are organized into structured parameters; then converting the structured parameters into a retrieval instruction stream of spatial data through a spatial relationship knowledge graph; therefore, the corresponding geographic position and the geographic object associated with the position are obtained. According to the method, a Chinese natural language geographic information retrieval function module is constructed based on the method, and the practicability of the process is verified by utilizing a query example.

The invention discloses a multi-task learning method and system based on knowledge enhancement, and the system comprises a basic coding module which is used for coding traffic event text information into text coding representation; the knowledge graph construction module is used for constructing a traffic field knowledge graph; the knowledge enhancement module is used for identifying the geographic entities in the text coding representation, linking the geographic entities to the traffic domain knowledge graph, and deeply fusing the geographic knowledge of the screened knowledge base entities in the traffic domain knowledge graph with the text coding representation; the event extraction module is used for extracting related elements in the text coding representation according to the text coding representation after deep fusion, and preliminarily constructing an event framework; and the multi-task learning module is used for coordinating combined learning and reasoning of entity identification, relation extraction, event type classification and knowledge fusion related tasks, identifying and extracting traffic event text information to be processed, and outputting a final prediction result. According to the method, the geographic entity in the text and the geographic knowledge in the knowledge graph are effectively linked and fused, information sharing and complementary enhancement can be realized, and the recognition accuracy and robustness of the model are improved.

The invention discloses a method for positioning a social media user based on a heterogeneous graph, which comprises the following steps of: mining and acquiring a behavior or a relationship of the user which is most likely to generate geographical location information through an association rule, and adding the behavior or the relationship generated by the relationship of the user as a node into a heterogeneous network which takes the user as a center by using the heterogeneous graph; according to the method, the content, relation and context information in the social network are comprehensively utilized, the feature vectors of the users are formed through the text information of the users and the extracted position information, the user group with the highest similarity with the to-be-predicted user is obtained by screening the users, and the position prediction precision is improved by utilizing structured processing on the geographic entity. According to the method, the precision and accuracy of social media user prediction are improved, a heterogeneous graph structure is simplified through association rule mining, excessive redundant information is prevented from being added into the graph, intimacy screening is carried out on the users in combination with hierarchical clustering, the intimacy screening granularity is controlled, and the accuracy of user position reasoning is improved.

The invention discloses a loss function optimization method based on a graph neural network in spatial data processing, and relates to the technical field of geographic information system and artificial intelligence cross fusion. A spatial graph structure is constructed, the spatial graph structure comprises nodes and edges, the nodes represent geographic entities, the edges represent spatial topological relations, and node features comprise spatial coordinate information and digital elevation model elevation values; a graph neural network model is designed, the graph neural network model comprises an input layer, a multi-layer message passing layer and an output layer, the input layer receives node features and edge features, and the multi-layer message passing layer aggregates feature information of node neighborhoods layer by layer; the output layer generates a spatial embedding vector reflecting a node spatial feature and a neighborhood topological relation; and defining a loss function, and training the graph neural network model by using the loss function. The problems of huge data scale, low processing efficiency, insufficient spatial relationship modeling precision, difficulty in fusion with an intelligent analysis tool and the like are solved.

The invention provides an urban inland inundation simulation method and system based on a geographic entity. The method comprises the following steps: constructing a library template of geographic entity data related to urban inland inundation simulation; establishing an urban inland inundation simulation database based on the design specification of the database template; collecting data required for processing waterlogging, and performing simulation calculation in combination with a one-dimensional pipe network and a two-dimensional hydrodynamic model; a 3D live-action map is used as a data base, and a user use interface is designed to provide a Web end for urban waterlogging simulation. The 3D live-action map platform is used as a data base support, a simulation system which is more appropriate in scene simulation, more accurate in data analysis and more scientific in emergency decision support is constructed based on the urban inland inundation geographic entity, and geographic entity semantization is provided for scene simulation so as to facilitate management of entities such as buildings, roads and pipelines in a city. In the aspect of waterlogging safety emergency rescue, a plurality of sub-tracks outside a rainfall main track are supported to compare effects of different emergency measure schemes in parallel, and more accurate decision support is provided for safety emergency.

The invention discloses a geographic entity geometric center point extraction method and device, and relates to the field of geographic entity geometric center point extraction, and the method comprises the steps: calculating the gravity center of a two-dimensional geometric figure of a geographic entity through employing a cut complement method; judging whether the gravity center is in the two-dimensional geometric figure of the geographic entity by using a ray method; if yes, regarding the gravity center as the geometric center of the geographic entity; and if not, constructing a Delaunay triangulation network in the geometric figure boundary region of the geographic entity, and extracting the geometric center of the geographic entity according to the Delaunay triangulation network. According to the method, the geometric center of the two-dimensional geometric figure of the geographic entity in any shape can be accurately calculated.

The invention provides a multi-level geographic entity data consistency determination method and device, equipment and a storage medium, and relates to the technical field of surveying and mapping geographic information. The method comprises the steps of performing fusion processing on geographic entity data of different spatial scale hierarchies to form a unified database; based on geometric, semantic and attribute features, a differentiation matching model is adopted to carry out automatic identity determination on point-shaped, linear and planar entities of different hierarchies, and comprehensive judgment is carried out by utilizing multiple geometric quantities such as distance, buffer area overlapping proportion, area overlapping degree, curvature, trend and the like; and performing unified coding and association relationship construction on the entities according to an affirmation result. According to the method, the problems that the same entity in the multi-level geographic entity data is inconsistent in affirmation and non-uniform in coding are solved, automatic and high-efficiency consistency affirmation and integrated management are realized, and the data processing accuracy and efficiency are remarkably improved.