44 results about "Spatiotemporal Analysis" patented technology

The invention discloses a land coverage change algorithm based on time-space analysis. The method comprises the following steps: S1, performing remote sensing data preprocessing; S2, performing NDVI distribution map calculation; S3, performing regression simulation on NDVI values in different land coverage type scopes; S4, performing threshold determining and land coverage type distribution map extraction; and S5, performing land coverage type change statistics. According to the invention, by taking remote sensing images of different time phases and land data (such as second national land data employed by the embodiments of the invention) of field investigation as a basis, image preprocessing is performed on the remote sensing images, and plantation normalization exponents of the different time phases are obtained. Model analyzing is performed on NDVI data of the different time phases and corresponding land field investigation data, an NDVI threshold scope corresponding to each type of land is determined, a quite clear land use type graph is obtained, and changing land coverage change data statistics is carried out.

The invention discloses a fault cause determination method and system based on a space-time analysis log. The method comprises the following steps: step 1) collecting syslog logs generated by all network equipmentin a network system; 2) monitoring syslog logs generated by all equipment in the network and periodically performing feature analysis on the logs based on time space in real time; 3) performing time-based feature extraction and analysis on the running state of the network equipment, and checking hidden dangers and fault time points of the network equipment; (4) obtaining space topology information, (5) extracting related equipment logs in combination with the space topology information to construct a feature matrix of abnormal events, and (6) obtaining a fault propagation chain and root causes through an intelligent decision algorithm, and displaying detailed information of a fault influence surface and the root causes.

The invention relates to a topology discovery method of space geological data based on an unstructured mode, mainly comprising three-level discovery containing topology core relationship generation, topology dynamic relationship search and topology error relationship correction. The topology discovery method based on the unstructured mode aims at different manners of obtaining the space geological data, space data distribution features and required functions and application of a model, and is designed by analysis of such complex geophysical phenomenon as fault, wrinkle, lenticle, intrusive rock and on the like, so that correlations of the space, the time and a structure among large and complex space geophysical data accumulated for a long period during the mining area geological exploration are determined, and the uniformity and the authenticity of the space, the time and the structure can be maintained; and therefore, the space expression ability and the time-space analysis efficiency of three-dimensional geophysical data can be strengthened, and the advanced science means for analyzing, forecasting and evaluating geological resources can be provided for geologists.

The invention provides an operator data-based public security case time-space analysis method and apparatus. The method comprises the steps of obtaining occurrence information of serial cases, whereinthe occurrence information comprises occurrence time, occurrence places, occurrence time error ranges and occurrence place error ranges of the cases; according to a sequence of the occurrence time ofthe serial cases, sequentially connecting the occurrence places into a case action track on a map; and screening out operator clients occurring on the case action track in the occurrence time, and classifying the operator clients as suspects, thereby obtaining a suspect list. The method is based on operator data, so that the coverage range is wide and the blind area is small; the operator clientssatisfying the case action track are screened and classified as the suspects, so that the suspect check range is effectively narrowed, the case solving time is shortened, and the case solving efficiency and quality of public security organs are improved.

The invention provides a single motor imagery electroencephalogram signal recognition method based on multi-linear principal component analysis. According to the method, a projection matrix can be found from each of a time domain, a frequency domain and a space domain to project the three-dimensional EEG tensor data, so that dimensionality reduction of the original EEG tensor data is realized, andthen classification is carried out by combining a linear classification method. Compared with a traditional principal component analysis method, according to the multi-linear principal component analysis method provided by the invention, dimension reduction is directly carried out from multiple dimensions in the multi-dimensional tensor; spatial structure information of the signals is reserved, the signals are expanded into a one-dimensional vector form for classification after dimension reduction, and therefore compared with a traditional method based on principal component analysis, the method provided by the invention reserves the spatial characteristics of the EEG signals; compared with EEG time-domain analysis, frequency-domain analysis, time-frequency analysis or space-time analysis, the EEG signals are subjected to multi-modal analysis from the time domain, the frequency domain and the space domain, more comprehensive features can be extracted, and the electroencephalogram recognition effect is still high under the condition of small samples.

The invention relates to a short-time traffic flow prediction method based on dynamic space-time analysis, and the method specifically comprises the steps: system modeling: providing a dynamic traffic flow space-time state matrix for representing the traffic state of each road section of a regional road network, and precisely and quantitatively representing the traffic state of the road section; proposing a traffic flow dynamic space-time weight matrix used for calculating the similarity between traffic flow space-time state matrixes, dynamically allocating a weight to each data element of the space-time state matrixes so as to construct a dynamic space-time weighted Euclidean distance, and optimizing a neighbor search mechanism of a K neighbor model; and providing a weighted prediction method based on a similarity ratio, and carrying out empirical research by using a real traffic flow data set of an American California highway management system PeMS. The precision performance of three prediction functions including mean value prediction, inverse distance weighted prediction and grade weighted prediction is discussed. Compared with an existing statistical theory and an artificial neural network model, the model provided by the invention is verified to have better prediction precision in short-time traffic flow prediction.

The invention provides a navigation environment visualization representation method for ship visual navigation. The method includes the following steps that: a geometric layer is constructed, wherein the geometric layer includes a geometric model of navigation environment and thematic elements, initial positions and statuses, ship dynamic data and movement navigation point position information; a physical layer is constructed, wherein the physical layer is used for representing the physical characteristics of the thematic elements in the navigation environment and laws of physics which the thematic elements should follow in the navigation environment; a temporal layer is constructed, wherein the temporal layer concludes the temporal characteristics of the navigation environment and thematic elements to states and processes and specifies a researched time domain as a continuous model based on time density; and a semantic layer is constructed, wherein the semantic layer includes topic semantic, space semantic and process semantic and reflects the physical natures of the dynamic change processes of the navigation environment elements and the influences of the navigation environment elements on ship visual navigation. With the method of the invention adopted, the navigation environment and thematic elements can be integrated under a unified time-space frame, and centralized and unified representation of navigable environment information can be realized, and the accuracy and efficiency of visual time-space analysis can be improved.

The invention discloses an urban road network short-term traffic flow prediction method based on a space-time residual hybrid model, and the method comprises the steps: collecting the current latitude and longitude data of a vehicle, storing the data in a big data cluster database, and carrying out the data preprocessing of the original data; dividing the urban road network into a traffic grid network according to longitude and latitude, mapping the longitude and latitude data of the vehicle into the traffic grid network, and generating traffic grid data; performing standardization processing on the traffic raster data, and constructing a training set and a test set; constructing an urban road network short-term traffic flow prediction model based on the space-time residual hybrid model; and training the constructed urban road network short-time traffic flow prediction model based on the space-time residual hybrid model to predict traffic raster data at the next moment. According to the method, the residual hybrid model is introduced under space-time analysis of the road network, and the capability of capturing the traffic flow time and space of the road network is improved, so that the relative error when the space-time residual hybrid model predicts the traffic flow is reduced, and the prediction precision is improved.

The invention discloses a traffic speed prediction method based on traffic flow data fusion. The method comprises the following steps of: (1) data preprocessing, namely performing data preprocessing on original traffic data, including historical traffic speed data and traffic flow data; (2) designing a traffic speed prediction model based on traffic flow data fusion, wherein the traffic speed prediction model comprises a flow prediction layer, a data fusion layer, a space-time analysis layer and a prediction output layer; (3) generating a sample data set and training the traffic speed prediction model, specifically splitting the traffic data obtained based on the step (2), generating a training data set, a verification data set and a test data set, and training the traffic speed prediction model; and (4) predicting a traffic speed in a future time period, namely based on the prediction model obtained by training in the step (3), predicting the traffic speed in the future time period according to the collected traffic data. The invention further comprises a system for implementing the traffic speed prediction method based on traffic flow data fusion.

The invention relates to a data and model coupling analysis method oriented to marine drift trajectory prediction. The method comprises the following steps: actually measured drifting track data collection and marine environment data preparation, drifting track prediction numerical simulation experiments, different marine environment data set space-time analysis, multi-dimensional drifting track prediction precision evaluation, precision evaluation result statistical analysis and FSLE analysis based on a prediction track and an actually measured track, multi-factor sensitivity analysis and applicability evaluation of the environmental data and the prediction model in the region. According to the method, the environment data and physical model coupling analysis evaluation capability in drift track prediction is improved, the application of different modes of data in the drift track prediction field is promoted, and the further regional data refinement work is guided. The analysis and evaluation method provided by the invention can more comprehensively verify the real performance of the related prediction mode in the region, and is beneficial to improving the overall reliability of trajectory prediction.

The field of the invention encompasses synthetic chromosome compositions and methods that allow single cell spatiotemporal analysis in response to differentiation cues and labeling of transplanted cells to monitor the fate and function of such cells in the patient recipient.

The invention relates to the technical field of marine monitoring, and discloses a seawater environment monitoring data statistical system, which comprises a user login module, a region division module, a data retrieval and query module, a data space-time analysis module, an environment quality evaluation module, a data input and output module, a comprehensive database module, an early warning and alarm module and an information issuing module, the data space-time analysis module is composed of a data time analysis unit and a data space analysis unit; the data time analysis unit is used for estimating and researching statistical regularity existing in the monitoring element change process of a certain time sequence so as to reflect the change track of the monitoring element; and the spatial analysis unit is used for describing a spatial distribution rule of the monitoring data and representing the spatial distribution rule in a map form. According to the method, the abnormal conditions are shown in the form of maps and charts, so that the method is convenient, simple and visual, a user can be effectively helped to make accurate judgment in time, and marine pollution is reduced.

According to the urban air quality monitoring method based on mobile multi-source perception, in the stage of obtaining mobile air quality parameters, an environment monitoring sensor and a combined positioning module are used for recording the time position of a mobile carrier (a pedestrian wearable type, a shared bicycle, public transport and an unmanned aerial vehicle) at each collection pointand the environment parameters of the position; in the multi-source data processing stage, a data processing and storing module is used for carrying out heterogeneous fusion, denoising and filtering,space-time interpolation and database construction on the mobile air quality data; in the environment space-time analysis and evaluation stage, a clustering algorithm and thermodynamic diagram visualization are used for carrying out data modeling and space-time analysis on multi-source air data. Compared with traditional environment fixed monitoring, the method has the advantages that the number and cost of fixed monitoring nodes are greatly reduced in a mobile and multi-carrier data acquisition mode, noise is reduced through Kalman filtering, discrete points are interpolated to uniform grid space discrete points through a weighted K-nearest neighbor interpolation algorithm to draw a curved surface, and visualization is achieved.

The invention discloses a motor vehicle path fitting algorithm based on low sampling data. The method comprises steps of S1, reading the configuration item information, and obtaining track data and road network data which need to be processed; S4, filtering the road network data, determining a road type field needing to be reserved according to the actual situation of a city, and storing each roadaccording to the geographic information; S7, for the whole track, constructing a migration probability matrix for adjacent track points, calculating the comprehensive probability between adjacent projection points, and finally obtaining a probability matrix between projection points corresponding to each track point in the whole track; and S8, obtaining a relation graph of the whole track according to the comprehensive probability matrix of the whole track calculated in the S7, and sequentially selecting the projection point with the maximum value as the optimal fitting path of the track. Themethod is advantaged in that the information such as spatial distribution and time consumption is comprehensively considered, the migration probability between adjacent mapping points is calculated on the basis of space analysis at the moment, local optimal probability path selection is carried out, and progressive high-precision path fitting is achieved.

The invention relates to the technical field of smart traffic, and particularly discloses a smart travel traffic data fusion method, wherein the method comprises the following steps: acquiring traffic data acquired in various ways including urban road checkpoints, vehicle satellite positioning and a floating vehicle platform; analyzing the traffic data according to a space-time analysis method to obtain a traffic data analysis result in a region; performing space-time expansion analysis on the traffic data analysis result in the region to obtain an urban road overall traffic data analysis result; and outputting the urban road overall traffic data analysis result as urban smart travel data. The invention further discloses a smart travel traffic data fusion device. According to the smart travel traffic data fusion method provided by the invention, the convergence and management of cross-platform and cross-network traffic travel data are realized, so that the problems of fusion calculation and self-expansion application of urban traffic smart travel data are solved.

The invention provides a marine environmental element statistical prediction method based on a space-time experience orthogonal function, and the method comprises the steps: constructing a day-by-day marine dynamic environmental element space-time sample matrix over the years of a to-be-analyzed space based on the reanalysis data of a to-be-analyzed and predicted sea area; performing multi-time scale analysis on the space-time sample matrix by using a wavelet packet decomposition method to obtain space-time sample matrixes of different time scales; performing spatio-temporal empirical function decomposition on the spatio-temporal sample matrixes of different time scales, and respectively constructing orthogonal spatio-temporal bases corresponding to different time scales; adopting an orthogonal space-time base to construct a space-time statistical prediction model oriented to ocean dynamic environment elements; and performing reconstruction by using the medium-and-long-term space-time analysis prediction results of different time scales to obtain the medium-and-long-term space-time analysis prediction results of the ocean dynamic environment elements of the to-be-analyzed space. The invention develops a medium and long term statistical prediction model of the ocean dynamic environment elements based on a space-time experience orthogonal function, and the analysis and prediction capability of the ocean dynamic environment elements is improved.

The invention discloses an integrated pipeline space-time analysis and risk estimation method based on a 4D-BIM technology, and belongs to the technical field of calculation, reckoning or counting. Generating a comprehensive pipeline three-dimensional model in Microstation software according to a construction drawing, and endowing each component with a code; the method comprises the following steps: converting a three-dimensional model of the comprehensive pipeline into a. Dgn.spx format which can be identified by Synchro software by using a plug-in SynchroPlug in the Microstation software; the method comprises the following steps: exporting coding information of a three-dimensional model in Microstation software, and importing the coding information into Project software; in Project software, according to the coding information and the construction organization scheme, a construction progress schedule is formulated and converted into a. Xml format; and importing the three-dimensional model and the construction progress schedule into Synchro software, simulating the construction progress and carrying out space-time analysis and risk estimation, if no potential risk exists, determining the final comprehensive pipeline construction organization scheme, otherwise, circulating the optimization steps until the requirements are met. According to the invention, possible problems and time-space risks in the construction process are predicted, and the construction management efficiency and the construction safety are effectively improved.

The invention discloses a multi-source space-time analysis algorithm for faults of high-voltage switchgear, which comprises the following steps: firstly, acquiring various fault characterization quantity data of the high-voltage switchgear, and storing the fault characterization quantity data in a database; and then obtaining fault characteristics of the high-voltage switchgear. According to the multi-source space-time analysis algorithm for faults of high-voltage switchgear, through multi-dimensional analysis of the incidence relation between various fault characterization quantities and the relation between the incidence relation and the fault characteristics, theoretical support is provided for multi-dimensional comprehensive analysis of the high-voltage switchgear, meanwhile, analysis of the fault characterization quantity is not limited to comparison of absolute threshold values, comparison of transverse data and longitudinal data is carried out at the same time, so that fault judgment is more three-dimensional and accurate, and finally an algorithm model between the fault characterization quantity and the fault characteristics of the high-voltage switchgear is established, a multi-source space-time analysis algorithm for the temperature and insulation defects of the switch cabinet is formed, so that the accuracy of defect alarm is improved, false alarms and missing alarms are reduced, the operation and maintenance efficiency is improved, and the safety risk is reduced.

The invention relates to the field of ecological civilization, and provides a method for constructing an ecological conservation geographic knowledge graph, which comprises the following steps of: constructing an ecological conservation geographic science knowledge base by taking geographic features and an ecological civilization mode as a core, and constructing an ontology base by taking the ecological conservation geographic science knowledge base as a data basis; adopting a distributed network data acquisition technology to obtain multi-source heterogeneous network data for text space-time analysis, extracting ecological conservation geographical space-time scene events and corresponding time and position attributes, and creating an ecological conservation geographical space-time event database; and constructing an ecological conservation geographic knowledge graph according to the ecological conservation geographic ontology library and the spatio-temporal event database. By constructing and applying the ecological conservation geography knowledge graph based on the ubiquitous network, semantic association, intelligent retrieval and reasoning among different knowledge of ecological conservation geography are achieved, the capacity of a search engine is improved, and the search quality and search experience of a user are enhanced.

The invention discloses a spatio-temporal analysis method based on massive position trajectory data. The spatio-temporal analysis method comprises the steps of storing spatial position index information in an HDFS in an ORC file format; converting the spatial position index information Spatial Object into a set memory format by utilizing an S2 geometric library of Google; realizing a spatial function Spatial UDF on the basis of an OGC OpenGis standard; spark SQL being used to describe the operation of space-time analysis, spatial position indexing and filtering push-down being used to extract trajectory information, and a spatial function and a spark operator being used to perform query analysis calculation. Compared with a traditional scheme and an existing scheme in the industry, the technical scheme has obvious advantages in the aspects of the storage compression ratio, the space-time analysis performance, the assembly operation and maintenance cost and the like, and the feasibility and effectiveness of the method are explained.

The invention provides an intersection traffic state recognition method based on spatio-temporal analysis, including data collection, preprocessing steps of feature vector selection and definition steps, and classifier design. Firstly, the eigenvectors of traffic state recognition are obtained through spatio-temporal analysis technology, that is, the spatial traffic flow data and time real-time signal data are refined and deeply fused in stages to obtain value parameters that are more suitable for the traffic state, and then the obtained eigenvectors are analyzed. Carry out learning and training to automatically identify traffic conditions.

The invention relates to a spatio-temporal analysis method of pavement damage data based on multi-source feature fusion. Pavement damage detection adopts a semantic classification network to capture image data of local abnormalities and diseases of the pavement, and detect classification results of pavement abnormalities; GPS of damage data Positioning and video positioning space fusion, through the multi-section image mosaic algorithm, through the realization of multi-source image data fusion, establish a global road surface coordinate system; based on the damage data time series traceability based on shape matching, according to the road surface damage feature extraction under the time series, use space Based on the rough matching of location features, the matching of high-frequency and multiple-acquisition images is carried out at the same time; the construction of high-frequency data sets of natural road surface conditions, including high-frequency lightweight road surface state data collection, information extraction and fusion technology of multi-source data, and The data set interface is established. Compared with the prior art, the invention has the advantages of strong anti-interference ability, improved timeliness of road surface maintenance and management, and the like.

The embodiment of the invention provides a non-uniform space-time analysis method and system for surface variables. The method comprises the steps of obtaining to-be-analyzed surface variable data; carrying out non-uniform space analysis based on information entropy on the to-be-analyzed surface variable data to obtain a non-uniform space quantitative analysis result; and performing non-uniformitytime analysis based on grey relational degree on the to-be-analyzed surface variable data to obtain a non-uniformity time quantitative analysis result. According to the non-uniformity space-time analysis method for the surface variables, the spatial analysis method uses the information entropy as an index for quantitatively describing the non-uniformity of the surface variables, dimensions can beunified, and the invention is suitable for remote sensing observation and also suitable for observation of ground experiments; according to the time analysis method, a grey correlation analysis method is used for time change analysis of non-uniformity among different variables.

An arrangement planning apparatus includes a demand prediction unit which sequentially predicts an occurrence time point and an occurrence spot of demand for a moving resource along a time course, a first data conversion unit which analyzes each prediction result by the demand prediction unit based on a spatiotemporal division system, in which a size of an area where the moving resource is movable and a length of a time range in which the moving resource moves are defined, and converts each prediction result by the demand prediction unit into plural groups of first moving resource management data including the areas and the time ranges, a spatiotemporal division unit which extracts a specific group among combinations of the areas and the time ranges in the plural groups of first moving resource management data, changes the area and the time range which belong to the extracted specific group, and updates the spatiotemporal division system with this change, and a second data conversion unit which applies the spatiotemporal division system updated by the spatiotemporal division unit to each prediction result by the demand prediction unit and converts each prediction result by the demand prediction unit into plural groups of second moving resource management data including the areas and the time ranges.

The invention discloses a long-distance gas transmission pipeline spatio-temporal data fusion system and method based on dynamic segmentation, and relates to the field of underground pipeline data fusion, data organization models and spatio-temporal indexing. The method comprises the following steps: acquiring gas transmission pipeline data; extracting the data of the gas transmission pipeline to obtain sample data of the gas transmission pipeline; standardizing the sample data of the gas transmission pipeline to obtain standardized sample data; performing data conversion on the standardized sample data to obtain converted sample data; performing data fusion on the converted sample data, and establishing a gas transmission pipeline space-time database. According to the invention, long-distance gas transmission pipeline spatio-temporal data fusion can be carried out, and a spatio-temporal data index model is established to efficiently manage data, so that integrated organization and management of long-distance gas transmission pipeline body, operation and maintenance and environment data are realized; and a solid data support is provided for safety space-time analysis of the long-distance gas transmission pipeline and accurate acquisition of potential safety hazards of pipeline operation.

The invention discloses a method for analyzing the real license plate of a vehicle with a license plate based on the bayonet passing data, comprising the following steps: step 1, establishing a vehicle and driver information database; Human face information, based on the time-space analysis of the vehicle, determine the suspected vehicle with the license plate; Step 3, obtain the driver information of the suspected vehicle with the license plate, and find out the same characteristics or similarity with the suspect vehicle with the license plate from the driver-associated vehicle Vehicles greater than the threshold value and obtain the license plate number, which is the real license plate corresponding to the suspect vehicle of the set. The invention has the beneficial effects of narrowing the query range of the vehicle characteristic information, accelerating the recognition speed, improving the recognition efficiency, and being able to effectively curb the behavior of the false-plate vehicle.

The invention discloses a spatio-temporal analysis method based on massive position trajectory data, which includes: saving the spatial position index information in an ORC file format in HDFS; using Google's S2 geometry library to convert the spatial position index information Spatial Object into a set An in-memory format; based on the OGC Open-Gis standard to implement Spatial UDF for spatial functions; Spark SQL is used to describe the operations of spatiotemporal analysis, use spatial location index and filter push-down to extract trajectory information, and use spatial functions and spark operators for query analysis and calculation . The technical solution of the present invention has obvious advantages over traditional solutions and existing solutions in the industry in terms of storage compression ratio, space-time analysis performance, component operation and maintenance cost, etc., which illustrates the feasibility and effectiveness of the method.