38 results about "Geographic space" patented technology

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a big language model dialogue recommendation method based on multi-modal geographic information fusion and context modulation, which comprises the following steps of: firstly, coding and fusing a text address, geographic coordinates and numerical attributes to obtain a geographic context matrix; then intention modeling is carried out to generate user intention representation, then context feature modulation is utilized, dynamic modulation is carried out on the user intention representation by utilizing an asymmetric fusion mechanism, a context guiding signal containing geographical constraints is generated, and finally, the context guiding signal is spliced before the user intention representation. The big language model input to the parameter freezing obtains user preference vectors and calculates recommendation scores with the candidate positions, the candidate positions are sorted according to the recommendation scores, and the candidate positions with high scores are recommended to the user. According to the method, on the premise that internal parameters of the large language model are not changed, the geospatial reasoning ability is effectively given to the large language model, and the accuracy and interpretability of dialogue recommendation are remarkably improved.

The invention relates to a GIS technology-based geographic information acquisition method, device and equipment and a medium, and the method comprises the steps: obtaining a satellite image, an unmanned aerial vehicle LiDAR point cloud and ground sensor data through multi-source geographic data acquisition, and generating a multi-source geographic data set and an environment parameter matrix; performing dynamic credibility evaluation on the environment parameter matrix to generate a credibility vector; carrying out self-adaptive weight distribution on the credibility vector in combination with an inherent error coefficient of the equipment to generate a dynamic weight vector; performing space-time fusion on the multi-source geographic data set and the dynamic weight vector, and generating a fused geographic information matrix through coordinate registration and weighted fusion; and finally, carrying out closed-loop distortion calibration based on the verification reference point, correcting a system error by using a credibility vector, and generating an optimized geographic information database. According to the method, measurement deviation caused by environmental factors can be effectively overcome, intelligent fusion and quality control of multi-source heterogeneous geographic data are achieved, and the application requirement of high-precision geographic space analysis is met.

The invention, which relates to the technical field of digital interaction, discloses a block-chain-based scenic spot digital creative and creative interaction method comprising the following steps: obtaining tourist block chain identity information and initializing a creative and creative interaction context; carrying out multi-point geographic space binding according to the interaction context to obtain a content triggering set; performing identity binding mapping according to the content triggering set to obtain a to-be-casted collection set; on-chain registration is carried out according to the to-be-cast collection set, and a right-confirmed collection set is obtained; performing multi-dimensional grouping and layering according to the right-confirmed collection set to obtain an associated collection relation graph; and carrying out interactive on-chain combination according to the associated collection relationship graph to obtain the composite cultural assets. According to the method, the distribution accuracy is improved, the exclusiveness and emotion recognition of the generated digital cultural and creative collections are increased, the irreplaceability and identity exclusiveness of the collections are improved, map nesting and layering of multi-dimensional grouping are realized, the structural hierarchy is increased, and the individuation degree of cultural assets of different tourists is improved.

The invention discloses a dynamic shelter restoration method and system based on continuous streetscape panoramic images, and the method comprises the steps: firstly obtaining a to-be-restored target panoramic image A and a to-be-restored reference panoramic image B, and generating an original pixel-level shelter mask; secondly, extracting matching points among the panoramic images, realizing cross-view geometric alignment of the images, and obtaining a target perspective view C and a reference perspective view D through perspective re-projection; and then inputting the target perspective view C and the reference perspective view D into a three-dimensional reconstruction framework, and performing three-dimensional point cloud reimaging under the camera pose of the target perspective view C by using a depth inspection mechanism. And finally, carrying out image restoration on a three-dimensional point cloud re-imaging result, restoring to a panoramic coordinate system, splicing with an original panoramic image, and outputting a shielding-free panoramic image. According to the method, it is ensured that the repairing result conforms to the authenticity and geometric consistency of the geographic space, and the problems of overlapping conflicts and visual tearing during multi-view projection fusion are effectively solved.

The invention belongs to the technical field of geographic information science space optimization, and particularly discloses a low-altitude vertical take-off and landing field site selection method fusing mobile phone signaling big data dynamic maximum coverage, which comprises the steps of fusing multi-source geographic space big data, and constructing an urban low-altitude vertical take-off and landing field candidate position set; defining a multi-time-sequence take-off and landing field maximum coverage site selection model, and determining an optimization objective function, constraint conditions and a decision-making method; a genetic algorithm is used as a basic algorithm model, and three limiting conditions of a multi-time-sequence influence factor, total quantity constraint and integer programming are added to form a multi-time-sequence integer constraint spatial evolution algorithm; based on MP-ICSEA, an MP-MCLP problem is solved, an optimal solution of service facility configuration is obtained, the effectiveness of vertical take-off and landing field site selection and vertical take-off and landing aircraft redeployment along with the change of the flow of people is verified, and therefore the reasonability of vertical take-off and landing field site selection and the efficiency of vertical take-off and landing aircraft redeployment according to the time sequence are greatly improved.

The invention discloses a method for intelligently evaluating the susceptibility of a landslide disaster in combination with deep learning, and relates to the technical field of geological disaster evaluation. According to the method, data processing is more accurate, standardized processing of multi-source geographic space data is achieved through the GIS technology, coordinate deviation, resolution difference and magnitude influence are eliminated, a high-quality and high-consistency data basis is provided for model training, and interference of data noise on an evaluation result is effectively reduced; model fusion is efficient, the advantages of RF and KNN basic models (the RF is high in anti-overfitting capacity, and the KNN is sensitive to local features) are fused, the prediction result serves as the input feature of the deep learning model, cooperation of machine learning and deep learning is achieved, the utilization efficiency of the model on the landslide influence factors is remarkably improved, and the method is suitable for the landslide prediction of the landslide. The evaluation precision is improved by 15%-20% compared with that of a single model (AUC value verification shows that the AUC value of the model is larger than 0.85).

The invention is suitable for the technical field of geographic element data analysis, and provides a geographic spatiotemporal weighted vector autoregression-based multi-element collaborative change modeling method, which comprises the following steps of: constructing a GTWR-VAR model, and combining a multivariable feedback mechanism of vector autoregression with locality of geographic spatiotemporal weighted regression to obtain a GTWR-VAR model; the method comprises the following steps of: quantifying spatial-temporal heterogeneity of mutual influence among geographic elements, introducing a Bayesian optimization mechanism, adaptively determining an optimal lag order, a spatial bandwidth and a time bandwidth by taking verification set mean square error minimization as a target, and finally adopting Monte Carlo block permutation test to determine the optimal lag order, the spatial bandwidth and the time bandwidth on the premise of retaining inherent time autocorrelation of data. And evaluating and eliminating non-significant items in the local coefficient to ensure the statistical effectiveness of the result. According to the method, the continuous change rule of the influence mechanism between the geographic elements in time and space is disclosed, and a more accurate and reliable basis is provided for regional response research under fine management, prediction and climate change.

The invention relates to the technical field of geographic space information, and discloses a rural cadastre dynamic updating and property management method and system based on remote sensing and a GIS (Geographic Information System). A multi-source data acquisition module, a data arrangement module, a dynamic updating module, a property management module, a data analysis module, a three-dimensional visualization module, a quality inspection scale module, a public participation module, a dispute mediation module, a resource sharing module and an operation and maintenance security module are deployed, and initialization configuration and linkage testing of each module are completed. The multi-source data acquisition module is used for integrating multi-source data, the dynamic updating module is used for detecting land type changes through a CNN model, the data analysis module is used for quantifying conflict risks and cultivated land / development trends, and all the modules cooperatively support application, so that the system has the advantages of real-time monitoring, intelligent early warning, accurate metering and dynamic optimization. The problems that in traditional rural cadastral management, land utilization change perception is lagged, ownership disputes are passively handled, cultivated land protection quantification is absent, and development intensity regulation and control are not accurate are solved.

The embodiment of the invention provides a satellite false fire point data elimination method, device and equipment and a storage medium, and the method comprises the steps: collecting a satellite fire point product and auxiliary geographic space data, determining a fire point label map, obtaining original wave band data, determining an original observation time sequence tensor according to the original wave band data, and obtaining an observation time sequence tensor according to the original observation time sequence tensor; building a deep learning model based on the LSTM network and the supplementary structure, dividing the fire point label map and the original observation time sequence tensor into a training set and a test set by using a preset date strategy, training and testing the deep learning model to obtain a prediction model, and inputting spatio-temporal information of each target fire point in the target satellite fire point product into the prediction model to obtain a prediction result. According to the method, the category of the target fire point is determined, and the data corresponding to the target fire point of which the category is the preset false fire point category is eliminated, so that accurate identification and automatic elimination of the false fire point caused by the non-combustion heat source are realized, and the false alarm rate of the satellite fire point product is remarkably reduced.

The invention relates to the technical field of remote sensing vision, and particularly discloses a remote sensing multi-modal reasoning method and system based on a geospatial thinking chain, and the method comprises the steps: constructing a geospatial thinking chain data set which comprises structured reasoning data organized according to a planning-positioning-synthesizing three-section cognitive architecture, the association module is used for establishing verifiable association between visual evidence in a remote sensing image and a text conclusion; constructing a remote sensing visual language basic model, wherein the model comprises a visual encoder, a language decoder and a visual projection layer connecting the visual encoder and the language decoder; according to the method, remote sensing analysis is modeled into a verifiable multi-step reasoning process, so that the model provides a verifiable analysis track while outputting a final answer, the problem that output cannot be verified due to traditional end-to-end training is solved, the performance of a complex analysis task is remarkably improved, and meanwhile, the accuracy of the result is improved. And the conversion from opaque perception to structured and verifiable reasoning is realized.

This invention relates to the field of agricultural information technology and discloses a method for constructing an innovative platform for cultural tourism to support agricultural industrialization based on historical resources. The method includes the following steps: S1: Multi-dimensional deconstruction and annotation of historical resources: Historical resources are collected, and spatiotemporal positioning, thematic hierarchical classification, and semantic attribute annotation are performed to construct a knowledge graph containing multi-dimensional associations of "historical events - geographic space - agricultural production - folk culture." The spatiotemporal positioning is accurate to the year, month, latitude, longitude, or administrative code. Through multi-dimensional deconstruction (spatiotemporal positioning, thematic classification, and semantic annotation) and knowledge graph construction, the originally isolated historical materials are transformed into machine-understandable and reasonable digital assets, supporting cross-temporal and cross-category related queries (such as "what crops were planted in a certain place under what background in a certain year"), facilitating in-depth exploration of potential points of integration between cultural tourism and agriculture.

The invention provides a zoonosis risk prediction method coupling climatic factor time sequence characteristics and land utilization space landscape characteristics under the background of climate and land utilization change, and relates to the technical field of zoonosis space epidemiology. According to the method, land utilization types such as buildings, roads, greenbelts and water bodies are identified through high-resolution remote sensing, landscape features are constructed, weather time sequences such as air temperature, rainfall and humidity are obtained by combining a geographic space big data cloud computing platform, and climate features are extracted by adopting a long-short-term memory network; and meanwhile, spatial population distribution characteristics are formed by utilizing the population data set. And based on the multi-source spatial-temporal characteristics, constructing a multilayer perceptron to simulate and predict the propagation rate. A true propagation rate is calculated based on the case matrix and the susceptible-infected-restorer epidemiological model. And calculating a loss function of the predicted propagation rate and the real propagation rate to optimize deep learning network parameters, and realizing risk dynamic assessment of the coupling space-time propagation mechanism.

The application discloses a kind of dynamic shelter repair method and system based on continuous street view panoramic image, the method is first obtained target panoramic image A and reference panoramic image B to be repaired, generate original pixel-level shelter mask.Secondly, the matching points between panoramic images are extracted, the geometric alignment of image across view angle is realized, and the target perspective C and reference perspective D are obtained by perspective re-projection.Then, the target perspective C and reference perspective D are input into the three-dimensional reconstruction framework, and the three-dimensional point cloud is re-imaged using the depth checking mechanism under the camera pose of the target perspective C.Finally, the image restoration result of three-dimensional point cloud re-imaging is restored to the panoramic coordinate system, and spliced with the original panoramic image, and the de-shelter panoramic image is output.The application ensures that the repair result conforms to the reality and geometric consistency of geographic space, effectively solves the overlapping conflict and visual tearing problem when multi-view projection fusion.

The invention discloses a residential quarter livability evaluation method considering different crowd preferences, and particularly relates to the technical field of human settlement environment evaluation, which comprises the steps of obtaining vector and attribute data of residential quarters in a city, constructing an evaluation basic data set, and constructing an evaluation system containing five-dimensional and three-level indexes such as living safety. And performing quantitative statistics and geographic space indexes, grouping questionnaires according to ages to obtain index weights, and performing weighted summation to obtain a residential quarter livability evaluation result considering crowd preferences. The method focuses on smaller spatial scale to carry out evaluation, gives consideration to objective environmental factors and subjective preferences of different crowds, adopts an adaptive quantification mode for different types of data, accurately measures the influence of various geographic space factors, fully distinguishes the demand differences of different age groups, enables the evaluation result to better fit an actual living scene, and improves the evaluation efficiency. The accuracy and rationality of human settlement environment evaluation are improved, a reliable basis is provided for urban planning and human settlement environment improvement, and ordered promotion of differentiated community construction is supported.

The application discloses a social interaction method and system based on GIS space-time base and digital twin simulation, relates to the field of digital twin and GIS fusion, and comprises the following steps: collecting multi-source heterogeneous data of a target geographic area through sparse sensing nodes, and encoding the data into implicit space-time tokens by using a Bit space-time variational autoencoder; inputting the tokens into a generative world model based on a diffusion transformer as a unique condition, and reconstructing a four-dimensional space-time field; converting the four-dimensional space-time field into a three-dimensional Gaussian sputtering representation, and rendering the representation into a two-dimensional view in real time by using a progressive resolution strategy; updating Gaussian sputtering parameters asynchronously through a time series prediction network in response to newly-arrived sparse sensing data; constructing a social interaction layer on the rendered view, and supporting multi-user distributed access; and predicting a future video frame sequence by using a random mask strategy in response to an interaction instruction. The application realizes the construction of a full-scale, high-fidelity and interactive digital twin world from a macro geographic space to a micro device state.

The application relates to a sample dynamic supplement method, system, device and medium based on environmental covariates. The method calculates the similarity between unknown points and the remaining sample point set and quantifies the uncertainty, then constructs a hierarchical sampling framework according to the uncertainty mean, dynamically allocates the supplement sample points, and finally combines the supplement points with the original sample set to form an updated sample set. Based on the full use of historical sample data, the method can significantly improve the representativeness and statistical balance of the updated sample set in geographical space, so that when compared with the traditional hierarchical sampling method, the map product precision evaluation result can obtain a better and more accurate performance, and the evaluation deviation problem caused by the non-representativeness of the sample set can be effectively overcome.

The application discloses a garden landscape design method based on digital twinning, and belongs to the technical field of landscape garden design. The method collects target site environment data sets, generates a site thermal distribution cloud map through a geographic space grid processing unit; extracts a site spatial pattern feature matrix based on the same; constructs a landscape effect distribution model, decomposes the feature matrix into terrain undulation, seasonal color and illumination reflection parameters, drives the model to generate a three-dimensional scene rendering sequence, embeds the model into a three-dimensional digital twinning scene of a garden site, and associates the parameters with physical properties of the twinning scene in real time; performs dynamic optimization of a landscape scheme, generates element replacement instructions, and updates a spatial topological relation network; outputs a site design scheme atlas, adjusts facility layout coordinates, and generates a final scheme data packet in combination with path connectivity, so that the accuracy and adaptability of garden landscape design can be improved.

The invention discloses a prediction uncertainty adaptive guide vector supplementary sampling method and system based on geographic optimal similarity, terminal equipment and a medium, and relates to the technical field of geographic space sampling, and the method comprises the steps: calculating the prediction uncertainty of all unknown points based on the geographic optimal similarity of sample points and the unknown points, and calculating the prediction uncertainty of the unknown points; the prediction uncertainty aims at vector sampling indexes; constructing a dual-objective function, and taking minimization of the dual-objective function as a target, selecting unknown points of which the prediction uncertainty is higher than a prediction uncertainty threshold value for supplementary sampling; and in a supplementary sampling process, adaptively adjusting the weight parameter of the dual-objective function and the prediction uncertainty threshold. According to the method, prediction uncertainty of local and overall areas is balanced, vector distribution prediction precision and supplementary sampling efficiency are improved with a small number of newly added samples, the method is suitable for disease vector monitoring scenes such as aedes albopictus, and support is provided for public health prevention and control decisions.

The application discloses a hydrological and hydrodynamic coupling alpine mountain flood simulation and prediction method, and belongs to the technical field of disaster prediction. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: for the target alpine mountain basin, collect multi-type and multi-scale basic data; S2, deep learning correction and fusion of satellite precipitation data: for the local overestimation and underestimation problems of satellite precipitation data, a deep learning algorithm is used for hour-scale correction and fusion. By collecting satellite remote sensing, reanalysis, ground observation and geographic space four kinds of core data, covering all factors of 'precipitation-runoff-terrain-underlying surface' required for alpine mountain flood simulation, avoiding the one-sidedness of traditional modeling caused by the lack of data types, using 3 sigma criterion to remove precipitation and runoff outliers, unifying data format and space-time scale, forming a standardized data set, avoiding the interference of abnormal values, incompatible format and space-time mismatch on subsequent model input.

The invention relates to a sample dynamic supplement method, system and device based on environment covariables and a medium. According to the method, the similarity between an unknown point and a remaining sample point set is calculated, the uncertainty of the unknown point and the remaining sample point set is quantified, then a stratified sampling framework is constructed according to an uncertainty mean value, supplementation sample points are dynamically distributed according to the stratified sampling framework, and finally the supplementation points and an original sample set are combined to form an updated sample set. According to the method, on the basis of fully utilizing historical sample data, the representativeness and statistical balance of the updated sample set in geographic space can be remarkably improved, so that compared with a traditional stratified sampling method, a map product precision evaluation result is more excellent and closer to the real precision, and the accuracy of the map product is improved. The problem of evaluation deviation caused by the fact that the sample set is not representative is effectively solved.

The invention provides a method and system for screening available land parcels of reserve land, and the method comprises the steps: constructing a remote sensing land database based on ArcGIS, integrating multi-source heterogeneous geographic space data, and carrying out the preprocessing of original remote sensing land data; a plot type deduction system is established, and deducted land is obtained; and constructing an artificial intelligence large model training data set and optimizing a large model system, and carrying out land type classification identification on the deducted land by adopting a large model. According to the invention, automatic and intelligent screening and identification of available land are realized, efficient utilization and dynamic supervision of land development are realized, and a method and system support is provided for promoting land utilization and development, layout optimization and system governance.

The invention provides a map scene semantic text generation method based on a progressive learning framework, and relates to the technical field of geographic space visual semantic paragraption.The method comprises the steps that a geographic space image-text data set is constructed according to a multi-mode geographic image and corresponding text description of the multi-mode geographic image, and a sample is obtained; optimizing a pre-trained CLIP vision-language model in combination with a sample on the basis of the difficulty of sample division in dynamic binning course learning; obtaining a to-be-processed multi-modal geographic image; and inputting a to-be-processed multi-modal geographic image into the optimized CLIP vision-language model, and outputting a structured description text of the multi-modal geographic image. According to the technical scheme, lightweight cross-modal semantic alignment is realized through dynamic course learning and biological heuristic feature decoupling.

The invention provides a wounded post-delivery decision-making method, device and equipment and a storage medium thereof, and relates to the technical field of medical informationization, and the method comprises the steps: obtaining environment state information at a current moment; performing feature vectorization processing to obtain a medical institution embedded vector and a transportation tool embedded vector; calculating through an attention mechanism to obtain an initial attention preference matrix; correcting the initial attention preference matrix; and performing action decoding to generate a joint scheduling action of the transportation tool. According to the method, a variable-length sequence is processed by using a Transform structure, and global adaptive modeling of dynamic medical resources is realized. By introducing a distance attenuation item to correct an attention matrix and explicitly introducing geographic space constraints, invalid long-distance pairing is effectively inhibited. The mechanism gives consideration to timeliness and resource utilization rate, and the decision-making efficiency and robustness of the system in a complex environment are remarkably improved.

The invention relates to an urban green space functional area automatic division method based on deep learning and crowdsourcing geographic space data. According to the method, an innovative solution is provided for the problems of efficiency and accuracy in urban functional area demarcation. The method comprises the following steps: firstly, integrating remote sensing image data and crowdsourcing geographic space data, including point of interest (POI) data, open street map road network (OpenStreet Map, OSM), Baidu map boundary and the like, and carrying out accurate mapping on social functional areas of urban green space (Urban Green Space, UGS); then, a deep learning algorithm, especially a U-Net model, is adopted to process the remote sensing image so as to extract vegetation distribution information; and then, analyzing POI data by utilizing a natural language processing technology, establishing a semantic relationship between the UGS social function and the POI, and further realizing automatic classification of the social function area. Besides, through accuracy evaluation, high efficiency and accuracy of UGS social function area division are ensured, and powerful data support is provided for urban planning, sustainable development, government decision making and urban geographical research. The application of the method significantly improves the automation level of urban green space functional area division, and provides an important technical means for rapid monitoring, effective management and scientific planning of the urban green space.

The invention discloses a population flow coupled urban vertical take-off and landing field network site selection method, and belongs to the field of urban air traffic infrastructure planning. Multi-source geographic space big data are fused, and a vertical take-off and landing field candidate point set conforming to urban built-up area environmental constraints is constructed; constructing a population flow coupled low-altitude aircraft vertical take-off and landing field network location model CPF-VNLM, taking the candidate point set as a starting and destination vertical take-off and landing field of the model, and determining an objective function, a multi-constraint condition and a binary decision variable which take maximum air-ground combined travel cost saving as a core; designing a model reduction pruning strategy; and solving the model by combining an integer programming solver to obtain an optimal layout scheme of the vertical take-off and landing field network. According to the invention, through coupling the population space-time distribution data and the urban physical space constraint, the vertical take-off and landing field network layout is optimized, and the operation efficiency and the service quality of the urban air traffic system are significantly improved.

The application discloses a continuous interest point recommendation method and system based on social space-time information and user preferences. The method comprises the following steps: obtaining long-term preference features and short-term preference features of a user in a user check-in sequence; obtaining geographical space features of each location in the user check-in sequence; obtaining user social information features of each location in the user check-in sequence; adding and fusing the long-term preference features, the short-term preference features, the geographical space features and the user social information features into multi-semantics features, and inputting the multi-semantics features into a recommendation model to generate an interest point recommendation list meeting user preferences. The technical problems of check-in data sparsity, insufficient utilization of meta-information and inaccurate recommendation for cold users are solved, hierarchical feature extraction is realized for the user check-in sequence, the geographical space features are increased, the user social information features are enhanced, the behavior characteristics of the user are comprehensively learned, the next interest point is accurately recommended, and the use experience of the user is optimized.

The invention relates to the technical field of digital village and cultural heritage protection, and discloses a village and town cultural space digital design method, which comprises the following steps of: constructing a multi-dimensional information acquisition and modeling unit to form comprehensive acquisition and digital modeling integrating geographic space, architectural form and intangible cultural information; designing a lightweight immersive interaction unit, and superposing a virtual culture information layer in an entity space by means of augmented reality (AR) and mixed reality (MR) technologies; developing a cloud collaborative creation and dynamic updating unit, and providing a visual tool to support residents and cultural institutions to jointly participate in content editing; and an intelligent guide and propagation enabling unit is established, personalized path recommendation is provided based on user behaviors and a positioning technology, and a propagation product is automatically generated through a cross-platform engine. According to the method, the integrity and authenticity of the digital record of the cultural heritage are ensured, the problems of homogenized cultural expression and single transmission channel are solved, and the public influence and social vitality of the cultural space are improved.

The application discloses a kind of based on the method for quickly finding city infrastructure ownership of big data analysis, specifically related to data analysis technical field;Collect GIS, property registration database, internet of things sensor, remote sensing image and historical archives data, use natural language processing and optical character recognition technology to parse unstructured text, compare the spatial information of GIS database and property registration database, mark geographic space inconsistency data, and combine property unit change trend analysis, identify property correlation instability data, train machine learning model, build infrastructure ownership prediction rule, improve the credibility evaluation ability of ownership informationThe application significantly improves the accuracy and response speed of infrastructure ownership query, especially in the scene of emergency repair and accident disposal, the property unit can be quickly identified, the repair delay caused by information lag or wrong matching is avoided, and the city management efficiency and public safety guarantee ability are improved.