186 results about "Geographic entity" patented technology

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 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.

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 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.

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 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 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.

The invention discloses a pipeline geographic entity conversion production method, system, equipment and medium, and the method comprises the steps: S1, designing a pipeline geographic entity model which is used for stipulating a unified format of a converted pipeline geographic entity; s2, the comprehensive pipeline data are expressed as primitives, the comprehensive pipeline primitives comprise pipe point primitives and pipeline primitives, and the pipe point primitives are divided into entity primitives and drawing primitives; primitives of the integrated pipeline data are coded into primitive codes for associating the integrated pipeline primitives with pipeline geographic entities; s3, traversing the pipe point primitives, judging whether the pipe point primitives are entity primitives or not, if yes, generating pipe point entities based on the entity primitives, and if not, not processing in the step and waiting for participating in pipeline entity conversion in the step S4; and S4, traversing the pipeline primitives, and performing pipeline entity conversion. According to the invention, automatic upgrade conversion from the integrated pipeline data to the pipeline geographic entity data is realized.

The embodiment of the invention discloses a virtual area model processing method and a related device, which can be applied to scenes such as the map field, the cloud technology, artificial intelligence, intelligent traffic, auxiliary driving, digital human, virtual human, games, virtual reality, augmented reality and the like. According to the method, the virtual region is constructed by the region image used for describing the geographic entity region, so that the problem of data misalignment is avoided, the accuracy of subsequently constructing the virtual region model is improved, the complexity of the construction process is reduced, the method is not limited by map vector data, and the application range is expanded. In addition, regional images are identified through multiple image identification models, and semantic features describing all the sub-regions are obtained. And performing three-dimensional reconstruction according to the multiple semantic features to obtain a virtual region model for the geographic entity region. Therefore, three-dimensional reconstruction is carried out based on multiple rich semantic features, and the accuracy of the obtained virtual region model is higher.

The invention belongs to the technical field of data processing, and discloses an intelligent geographic information text extraction method based on knowledge perception. Comprising the following steps: constructing a geographic knowledge ontology library, and constructing a corresponding semantic relationship network based on the geographic knowledge ontology library; obtaining a text geographic entity and a text semantic relationship corresponding to the input geographic information text; constructing a corresponding initial tree-shaped tree based on the obtained text geographic entity and the text semantic relationship in combination with a semantic relationship network; performing knowledge perception and semantic understanding on the corresponding initial tree based on the geographic knowledge ontology library to obtain a corresponding semantic tree; performing information extraction based on the obtained semantic tree to obtain required geographic information; by constructing the geographic knowledge ontology library and the semantic relationship network, deep understanding and intelligent extraction of geographic information texts are realized; and the accuracy and efficiency of geographic information extraction are improved.

The invention provides a named entity recognition method, device and equipment based on loop iteration, and relates to the technical field of computers.The named entity recognition method comprises the steps that to-be-processed data is preprocessed, and a first processed text is obtained; the preprocessing comprises text cleaning, truncation and deletion, part-of-speech reduction, unified coding, data resampling and stop word removal; inputting the first processing text into a primary recognition model, and determining a first entity label to which a to-be-recognized entity belongs; performing secondary identification on the to-be-identified entity based on the first entity tag and a secondary identification model, and determining a named entity and a target entity tag corresponding to the to-be-identified entity; the secondary recognition model comprises at least two stages of recognition models, and the granularity of the at least two stages of recognition models is smaller than that of the primary recognition model. According to the method, entity labels of multiple levels are obtained step by step by utilizing a loop iteration method, and the accuracy of geographic entity recognition is improved from a multi-dimensional angle.

The application discloses a low-efficiency land dynamic monitoring management method based on real three-dimensional geographical entities, relates to the technical field of natural resource informatization management, and comprises the following steps: constructing a terrain mutation sensitivity atlas and a vertical risk guide grid, generating a weighted initial value field under the joint driving of an elevation gradient, a reflection intensity and an echo confidence, and forming vertical prior information of geographical entities; based on the vertical prior information of the geographical entities, performing spatiotemporal consistency forced registration, performing local elastic calibration on oblique photography images and laser point cloud data through a multi-scale rigid-flexible mixed constraint mechanism, and generating a vertical error field. Through the construction of the sensitivity atlas, the execution of rigid-flexible registration, the introduction of semantic reconstruction, the implementation of uncertainty propagation and dynamic resampling regulation and control, the vertical error closed-loop correction of the three-dimensional model under complex topography is realized, the accuracy and stability of low-efficiency land identification are improved, and high-credibility technical support is provided for urban space analysis and land decision-making.

The invention belongs to the technical field of natural language processing, and particularly discloses a method for constructing a geographic knowledge graph alignment model, which comprises the following steps of: constructing a geographic knowledge graph structure containing multi-dimensional spatial information; creating a corresponding reverse link for the relation link between each geographic entity by using a reverse link generation strategy; distributing corresponding weights to different neighbor entities of each geographic entity by using a spatial perception attention mechanism, and calculating an output feature of each geographic entity by combining an orthogonal transformation matrix of the relationship and the spatial distance; and generating a negative sample through the adversarial network, training a pre-constructed deep learning network by using the negative sample, performing error calculation by using a combination of vector loss and geographic loss as total loss in the training process, and updating a corresponding weight parameter according to an error result until network convergence reaches an expected target, thereby completing training. According to the method and the device, the accuracy of geographic entity alignment in the geographic knowledge graph can be improved.

The application belongs to the technical field of data processing, and discloses a geographic information text intelligent extraction method based on knowledge perception; comprising: constructing a geographic knowledge ontology library, and constructing a corresponding semantic relationship network based on the same; obtaining text geographic entities and text semantic relationships corresponding to input geographic information text; constructing a corresponding initial tree based on the obtained text geographic entities and text semantic relationships, and combining a semantic relationship network; performing knowledge perception and semantic understanding on the corresponding initial tree based on the geographic knowledge ontology library, and obtaining a corresponding semantic tree; performing information extraction based on the obtained semantic tree, and obtaining required geographic information; the application realizes in-depth understanding and intelligent extraction of geographic information text by constructing a geographic knowledge ontology library and a semantic relationship network, and improves the accuracy and efficiency of geographic information extraction.

The present application relates to the technical field of geographic information display, in particular to a geographic entity information display method based on real scene three-dimensional modeling, comprising: acquiring multi-angle remote sensing images of the geographic entity region, performing target detection on the multi-angle remote sensing images to obtain each geographic entity type in the multi-angle remote sensing images; matching the geographic entity type with a preset geographic knowledge graph to obtain geographic information corresponding to the geographic entity type. Through processing of the multi-angle remote sensing images, the present application can effectively identify geographic entities, and extract more accurate geographic information according to different perspectives. Through the process, the accuracy of geographic entity classification and identification can be improved, and the geographic entity type is matched with the geographic knowledge graph, so that each geographic entity not only has a description of the physical space position, but also can be combined with rich semantic information.

The present invention discloses an automatic construction method for a multi-source geographic information knowledge graph based on deep learning, including: designing the storage structures of the geographic knowledge graph, geographic entities, and geographic corpora; constructing the data sources of the geographic knowledge graph through web encyclopedias and geographic databases. Extract the information from the web encyclopedia and / or geographic database and store it in the geographic entity structure. If the entity structures of the web encyclopedia and the geographic database are both described, but the information recorded content is not completely consistent, it is considered that the information in the geographic database is more accurate, and the information in the geographic database is used as the standard to align and form geographic entity nodes. The model predicts whether the geographic nodes and candidate geographic entities represent the same real-world entity and provides a confidence score for the prediction. Finally, the node selects the geographic entity with the highest confidence from the positive classification candidate pairs to establish the correct identity link. The advantages of the present invention are: improving the accuracy of information and saving labor costs.

The present invention relates to the field of data processing technology, and provides a method, apparatus, device, and storage medium for generating a geographic entity singularization model. The method comprises: determining missing areas and key areas based on an oblique photography model of a target area and existing singularization results of the target area; singularizing and reconstructing geographic entities in the missing areas based on the oblique photography model of the target area and point cloud data of the target area, and singularizing and reconstructing geographic entities in the key areas based on field supplementary photography of the key areas and the oblique photography model of the key areas; determining a target singularization result of the target area based on a first singularization result, a second singularization result, and existing singularization results; and fusing the target singularization result and the oblique photography model after scene decoration to obtain a geographic entity singularization model of the target area. The present invention improves the efficiency of singularization model generation.

This invention relates to a method and system for improving the quality and efficiency of map element production, comprising the following steps: Step 1: Providing a configuration management database, which is used to manage and maintain a geographic entity document library. Existing paper or electronic map element production standard documents are disassembled, index rules are established, and stored in the configuration management database; Step 2: Establishing basic files for data production personnel, connecting to the personnel system database, and automatically matching the work experience of production personnel. This invention, through the interaction between data production personnel and the system, integrates rich map data production cases with traditional production standard documents, effectively improving the skill proficiency of production personnel. Furthermore, by combining big data analysis, it accurately targets production bottlenecks and problematic production personnel, forming bottleneck solutions and pushing them to relevant data production personnel, effectively improving the quality and production efficiency of map data products.