13552results about "Geographical information databases" patented technology

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention provides a meteorological metadata storage method and system based on machine learning, and the method comprises the steps: obtaining a meteorological metadata set of a target region, inputting the meteorological metadata set into a pre-trained time sequence feature extraction model, generating the time sequence distribution features of all meteorological file files, and inputting a spatial topology mapping model, generating spatial correlation topological features of the meteorological elements; based on a cross fusion result of the time sequence distribution features and the space correlation topological features, a hierarchical index structure of meteorological metadata is constructed, a meteorological storage optimization model is called to generate a storage node distribution strategy of a meteorological file, and the meteorological file is stored according to the storage node distribution strategy. And writing each meteorological file file in the meteorological metadata set into the corresponding distributed storage node, and dynamically adjusting the mapping relationship between the physical position and the index of the stored file based on a real-time updating mechanism of the hierarchical index. The meteorological metadata storage efficiency and retrieval accuracy can be improved, and storage resource waste and operation and maintenance cost are reduced.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention provides a gas leakage time-space correlation early warning method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network disposed at a key node of a gas pipe network; according to the real-time concentration data and a historical leakage accident database, a UTM projection coordinate system is adopted to carry out registration on geographic space data, a three-dimensional space mapping model of a pipe network topological structure is established, timestamp alignment is carried out on all dynamic monitoring data streams through an NTP clock synchronization protocol, and a time-space correlation standardized data set is formed. According to the invention, full-chain intelligent management of the gas leakage risk from sensing, prediction to disposal is realized.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention relates to the technical field of data processing, in particular to a power transmission line inspection method combining meteorological data and dynamic confidence weighting, and the method comprises the steps: obtaining multi-source meteorological data, executing time-space reference alignment processing to generate a standardized data set, and carrying out the spatial overlay analysis of the standardized data set and power grid GIS topological data; constructing a meteorological and equipment association topological graph to identify threatened equipment; distributing a data source weight based on the dynamic confidence score table, generating an optimized meteorological prediction result through weighted fusion, and reversely updating a data interface layer; and in combination with field measured data and user correction records, a geofence matching rule and a weight parameter are dynamically adjusted to realize closed-loop calibration. According to the method, accurate association of meteorological data and power grid equipment space risks is realized, the extreme weather prediction reliability is improved through multi-source data fusion and adaptive optimization, the emergency resource allocation efficiency is optimized, the risk that a patrol plan is interfered by weather is reduced, and intelligent decision making of power operation and maintenance business is promoted.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent construction site management method and system based on the Internet of Things. The method comprises the following steps: acquiring construction site environment monitoring data in real time based on multiple Internet of Things sensor nodes; performing real-time environment state change mining on the construction site environment monitoring data, performing global environment perception modeling, and constructing a construction site digital environment perception map; obtaining a construction site omnibearing monitoring video and construction site construction equipment state monitoring data; carrying out target dynamic optical flow tracking on the omnibearing monitoring video of the construction site one by one, carrying out dynamic operation state evolution, and constructing a real-time behavior portrait model of each worker; and performing operation state equipment temperature change trend analysis and dynamic load change evolution according to the state monitoring data of the construction site construction equipment, and generating comprehensive state characteristics of each piece of equipment. The construction site management efficiency and the construction site construction safety are improved.

The invention relates to the technical field of environment monitoring, in particular to an underground water pollution source traceability identification method and system, and the method comprises the following steps: S1, collecting the flow velocity, flow direction and water level change data of underground water in a polluted region in real time; s2, constructing a preliminary underground water flow model; s3, simulating a migration path of pollutants in underground water, and predicting a potential pollution source position; s4, processing the collected underground water flow velocity and pollutant diffusion path data, and removing abnormal data interference; s5, generating a spatial distribution map of the potential pollution source in combination with a geographic information system; and S6, verifying pollutant components and concentration distribution thereof, and confirming the accuracy of a traceability result. According to the invention, by combining a multi-pollutant traceability algorithm, an underground water flow model and a geographic information system technology, a pollutant migration path is accurately simulated and a pollution source spatial distribution map is generated, so that the precision and reliability of underground water pollution source traceability are greatly improved.

The invention relates to the technical field of data processing, and discloses a sky-ground integrated geological environment monitoring and evaluation method and system. The method comprises the following steps: fusing sky remote sensing, surface monitoring and underground detection data into a monitoring matrix, and processing to generate an original database; constructing a space-time standard field based on the database, and fusing to generate an associated pedigree; decomposing the associated pedigree to obtain an element feature chain; constructing a coupling dynamic field based on the feature chain, and analyzing to obtain an evolution parameter set; establishing a risk assessment matrix according to the parameter set, and analyzing and generating an early warning index system; and combining an early warning system and historical cases to design a treatment scheme. According to the method, the unified space-time standard field, the multilevel feature transfer network and the dynamic coupling model are constructed, deep fusion of the space-based remote sensing data, the surface monitoring data and the underground detection data is realized, and the accuracy and reliability of geological environment monitoring and evaluation are improved.

The invention discloses a composite interference-oriented cultivated land system toughness evaluation and optimization method, and relates to the technical field of cultivated land toughness optimization, and the method comprises the steps: collecting and preprocessing multi-source cultivated land data, constructing a toughness evaluation index system, and calculating the weight of each index; and calculating a comprehensive toughness score based on weight distribution, drawing a time sequence change curve based on the score, and performing region division to generate a toughness distribution diagram. According to the method, the single-factor pollution index, the Nemo comprehensive index and the interaction effect index of heavy metal pollution are combined, the soil pollution condition is accurately quantified through a weighted summation method, a scientific and reliable basis is provided for toughness evaluation, the dynamic toughness distribution diagram is generated in combination with the LSTM model and the GIS technology, the spatio-temporal dynamic characteristics of a cultivated land system under composite interference are revealed, and the method has the advantages of being high in reliability and high in reliability. According to the method, the defects of an existing model in space-time analysis are overcome, differentiated optimization strategies are formulated based on region division and weak region labeling, and the resource allocation efficiency and the pertinence of an optimization scheme are remarkably improved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention belongs to the crossing field of hydrology and water resource scheduling and water conservancy informatization, and discloses a basin complex water engineering system real-time scheduling visualization dynamic modeling method and system, and the method comprises the steps: constructing a real-time scheduling topology visualization graph of a basin water engineering system; based on the real-time scheduling topology visualization graph, performing fusion configuration on water engineering node characteristic parameters and river section node characteristic parameters; constructing a composite flood routing model based on the node characteristic parameters of the fusion configuration; and constructing an intelligent scheduling model based on the composite flood routing model. According to the invention, through combination of a computer graphical interaction technology and a multi-model coupling algorithm, full-process digital management of water engineering scheduling is realized.

The embodiment of the invention discloses a bus travel visual management method and system combined with a positioning technology, and the method comprises the steps: obtaining the vehicle real-time positioning data of a target vehicle, extracting the task execution information associated with the vehicle real-time positioning data, carrying out the dynamic track fitting of the vehicle real-time positioning data, and obtaining the task execution information of the target vehicle; generating a driving track map matched with the task execution information, performing multi-dimensional analysis on the driving track map based on a preset task rule, and determining a driving state feature and a task completion index of the target vehicle; performing space-time correlation mapping on the driving state characteristics, the task completion index and the driving track map to generate a visual dynamic management interface; and in a running state of the visual dynamic management interface, in response to a tracing instruction triggered by the management terminal, calling the historical track database to carry out backtracking optimization processing on the driving track map, and outputting management feedback data containing an abnormal event label and a path comparison result.

The invention provides an adaptive multi-modal feature fusion well logging interpretation method based on multi-task joint learning, and the model comprehensively employs a graph convolution network, a multi-head attention mechanism, a gating mechanism and an adaptive loss weight adjustment algorithm, and effectively integrates multi-well group well logging data and geological structure information. And the complex spatial relationship and geological similarity between the wells are fully captured. The model can process multi-source data in a unified manner, extracts high-level feature representation containing spatio-temporal correlation, avoids repeated feature learning, reduces calculation complexity, and ensures the consistency and complementarity of prediction task features at the same time. The self-adaptive loss weight adjustment mechanism dynamically optimizes weight distribution according to the loss change of each task, realizes balance optimization among different tasks, and comprehensively improves the prediction precision of each task. The physical constraint regularization module ensures that the prediction result not only has statistical accuracy, but also conforms to the actual geological physical law, so that the reasonability and reliability of the prediction result are improved.

The invention discloses a grading, zoning and differentiating risk early warning method for mountain torrent disasters in medium and small watershed. Firstly, a drainage basin risk area is determined through mountain torrent disaster data analysis; the drainage basin delay time is calculated, the section water level and the drainage basin submerging range are simulated through the simplified hydrodynamic model, and quantitative analysis is conducted on the model; according to the method, a high-precision grid database is formed by combining unstructured grid division and risk source identification, and then clustering grading is performed on multi-dimensional characteristics of grids through a Gaussian mixture model (GMM) to obtain risk levels of the grids in a drainage basin. Finally, a differential early warning threshold value and a response strategy are formulated according to a grid partition grading result, and dynamic updating and linkage issuing are achieved on a GIS platform in combination with actual measurement data. According to the method, limited real conditions of small and medium-sized basin data and precision requirements of model simulation are taken into consideration, fine setting of the early warning threshold value is realized on the basis of grading and partitioning, and the accuracy of mountain torrent risk identification is also remarkably improved.

The invention relates to an intelligent detection and evaluation method and system for underground pipeline defects, and the method comprises the following steps: S1, collecting multi-modal data of an underground pipeline based on an inspection robot, and transmitting the multi-modal data to an intelligent detection and evaluation platform; s2, the intelligent detection and evaluation platform preprocesses the collected multi-modal data; s3, performing defect detection on the underground pipeline based on the preprocessed multi-modal data; s4, constructing a three-dimensional model of the underground pipeline, and superposing a defect detection result into the three-dimensional model; and S5, based on the three-dimensional model of the underground pipeline, performing intelligent evaluation on the underground pipeline, and generating a pipeline condition evaluation report. According to the invention, the efficiency and reliability of underground pipeline defect detection are effectively improved.

The present invention relates to the field of big data technology, and discloses a task scheduling method, device and storage medium for spatiotemporal big data, wherein the method comprises: performing ETL cleaning and standardization processing on the original spatiotemporal data to obtain spatiotemporal data in a unified format that complies with the OGC specification; performing spatial dimension reduction operations on the spatiotemporal data in the unified format and constructing a spatiotemporal index to obtain a spatiotemporal index data set; distributively storing and compressing the spatiotemporal data in the unified format according to spatiotemporal characteristics to obtain partitioned compressed data; establishing a multi-dimensional query index for the partitioned compressed data to obtain a task allocation scheme; performing distributed data retrieval based on the task allocation scheme to obtain the original query result; performing data integrity verification and spatial relationship reconstruction on the original query result to obtain the target query data. The method realizes the reasonable allocation of computing resources, effectively reduces the performance pressure of each node, and improves the availability and interoperability of data.

The invention relates to the technical field of hydraulic engineering intelligent operation and maintenance, and discloses an AI unmanned aerial vehicle-based auxiliary decision-making platform for hydraulic engineering operation and maintenance, and the platform comprises a data collection module which is used for obtaining multi-modal data of a water conservancy facility through an AI unmanned aerial vehicle, the multi-modal data comprises a visible light image, thermal imaging data, multispectral data and laser radar data; the data processing and fusion module is used for carrying out formatting processing, space-time alignment and feature fusion on the multi-modal data; the health assessment and prediction module is used for assessing the health state of the water conservancy facility based on the processed data and predicting the future change trend of the facility state; and the auxiliary decision-making module is used for generating inspection priority planning of the water conservancy facilities. According to the invention, through multi-source data fusion, dynamic risk assessment and intelligent optimization distribution, the effects of accurate monitoring, efficient operation and maintenance and risk visual management of the whole life cycle of the water conservancy facilities are realized.

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention relates to the technical field of geographic information surveying and mapping, in particular to a city-level surveying and mapping geographic information standard system fusion construction method and system, and the method comprises the following steps: obtaining surveying and mapping data space coordinates, extracting terrain boundaries, ground feature distribution and road intersection features, calculating boundary point spacing, and calling a database to divide a topological structure. And detecting precision, spatial offset and overlapping conditions, and generating a topological correlation result. According to the method, by deeply analyzing the space coordinates, the time sequence, the geographic boundary and the classification information of the surveying and mapping data, the organizability and logicality of the data are improved, the tracking and updating capabilities of the data are enhanced through the refinement of the topological structure, the dynamic monitoring of the ground feature change is realized, the regional conflict is reduced through the optimization of the boundary adjustment mechanism, and the accuracy and the reliability of the surveying and mapping data are improved. The accuracy and adaptability of boundary data are improved, the consistency and application value of surveying and mapping data are enhanced through accurate matching of classification information and adaptive weight adjustment of data fusion, and accurate fusion of the data in a multi-source heterogeneous environment is ensured.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention provides a precise interference method and system for pilot signals of a remote controller of an unmanned aerial vehicle, and the method comprises the steps: receiving and processing an image transmission signal and a remote control signal of the unmanned aerial vehicle, and analyzing a starting time, a transmission period and different types of image transmission protocol classification results of the image transmission signal; extracting the time period of the remote control signal and the occurrence time of a pilot symbol in the remote control signal; generating optimized interference signal parameter configuration by utilizing a reinforcement learning algorithm, analyzing historical data by utilizing a long short-term memory network to obtain a historical analysis result, and predicting an optimal interference opportunity; monitoring a wireless communication environment around the unmanned aerial vehicle by using a real-time spectrum sensing technology so as to identify a potential conflict frequency band, and selecting a target interference frequency band by using a frequency agility technology; determining a precise interference method based on the historical analysis result, the optimal interference opportunity and the target interference frequency band; according to the invention, the interference precision and effect on the pilot signal of the remote controller of the unmanned aerial vehicle are improved.

The invention discloses an AR navigation system and method based on a visual language model, and the method comprises the steps: firstly carrying out video data collection, and constructing a memory database; secondly, querying a navigation target most related to a natural language request of a user in the constructed memory database to obtain a current AR equipment pose and a target pose; and then solving the shortest path from the current pose to the target pose by using the current AR equipment pose and the target pose according to the point cloud map, and optimizing the path direction to finally obtain an optimized path. And finally, guiding a user to move along the planned path through view superposition path indication and voice prompt in AR equipment by utilizing the optimized path, and updating the point cloud map and the memory database. According to the invention, high-precision real-time positioning and sparse point cloud map construction can be realized only by camera input in indoor and outdoor complex environments with low GPS precision, accurate navigation is carried out, and the flexibility and intelligent level of navigation are improved.

The invention relates to the technical field of data evaluation processing, in particular to a rural waterlogging area waterlogging risk grade evaluation and early warning method based on multi-source data. The method comprises the following steps: acquiring geographic information, hydrological and flood data and flood control and drainage engineering data of a rural waterlogging area, and performing multi-source data fusion to obtain rural flood multi-source data; and performing river flood overflow calculation on the rural flood multi-source data to obtain river flood overflow data. According to the invention, through a data processing technology, a mode identification technology and a deep learning technology, rural multi-source feature data is subjected to fusion analysis, and risk level evaluation of a waterlogging area and a waterlogging sheet in a rural area is realized; a flood real-time forecasting and early warning model is constructed, when rainstorm occurs or a meteorological department issues rainstorm early warning, the rural flood flooding range is quickly predicted, and a risk avoiding transfer route is planned.

The embodiment of the invention provides a city emergency command and dispatch system based on Internet of Things perception, which comprises a data acquisition module, a data processing and analysis module, an emergency resource management module, a command decision module, a communication dispatch module and a visual display module, the data acquisition module acquires and preprocesses multi-source heterogeneous data through various sensors and an intelligent terminal; the data processing and analysis module analyzes data by using technologies such as big data, predicts the trend of an emergency, identifies risks, and generates a report and an early warning; the command decision-making module generates and optimizes a command decision-making scheme according to the early warning and the pre-arranged plan; the emergency resource management module manages resources and deploys the resources according to a scheme; the communication scheduling module realizes high-speed transmission and equipment interconnection based on various communication technologies; and the visual display module is used for visually displaying related information by virtue of a GIS (Geographic Information System) and a three-dimensional modeling technology. Through cooperation of all the modules, the urban emergency processing capacity can be improved.

The invention discloses a tunnel blasting quality evaluation and optimization method based on multi-source data fusion, and belongs to the field of tunnel blasting quality evaluation, and the method comprises the steps: collecting and preprocessing multi-source data of a tunnel blasting region; based on the preprocessed multi-source data, performing blasting quality evaluation according to local back break, a blasting contour line, average linear back break and point cloud extraction to obtain a blasting quality evaluation result; according to the blasting quality evaluation result, the blasting quality is graded, and a comprehensive blasting quality score is calculated and graded; and establishing a database containing geological parameters, surrounding rock response parameters and blasting process parameters, training through a convolutional neural network model to generate a blasting parameter optimization scheme, and dynamically adjusting blasting parameters of the next cycle according to the comprehensive blasting quality score. According to the method, the blasting parameter optimization and the quality evaluation process are closely combined to form a closed-loop system, the specific situation in the construction can be reflected in real time, and the accuracy of the blasting effect is ensured.

The invention relates to the technical field of big data, and discloses a space-time big data abnormal event detection method and device and a storage medium. The method comprises the following steps: performing timestamp unification and space coordinate standardization processing on urban space-time big data to obtain a standardized space-time data set; feature vector cosine similarity between each data point in the standardized spatio-temporal data set and other data points is calculated to obtain a target similarity value, and an initial neighborhood data set is selected; performing space-time density distribution calculation on the initial neighborhood data set to obtain a core neighborhood data set; performing time sequence feature and spatial distribution feature extraction on the core neighborhood data set to obtain a fusion feature weight matrix; and inputting the fusion feature weight matrix into a support vector machine classifier to perform abnormal event recognition to obtain an abnormal event detection result, thereby improving the classification capability of high-dimensional nonlinear features, and simultaneously realizing all-directional feature description and detection of abnormal events.

The invention relates to a road collapse risk comprehensive assessment method based on multi-source data fusion, and the method comprises the following steps: collecting and preprocessing multi-source data related to road collapse, the multi-source data related to road collapse comprising remote sensing image data, underground water level monitoring data, geological exploration data and surface deformation monitoring data; spatio-temporal feature fusion and multi-level data fusion are carried out on the preprocessed multi-source data, the spatio-temporal feature fusion comprises spatial registration and time alignment, and the multi-level data fusion comprises low-level data fusion and high-level data fusion; and performing feature extraction on the fused multi-level data, inputting a feature extraction result into a road collapse risk assessment model constructed based on a machine learning model, outputting a road collapse risk occurrence probability, and generating a road collapse risk level according to the road collapse risk occurrence probability. Compared with the prior art, the method can comprehensively and accurately predict and evaluate the risk of road collapse.