54 results about "Spatial aggregation" patented technology

This invention provides a context-aware search engine that communicates with users having a particular location at a particular time within a tessellated network of geographically spaced-apart communication nodes, typically wireless nodes/access points (APs). Based upon the user's location and time, the search engine delivers relevant site-specific information germane to that user's place and time. In an illustrative embodiment, the search engine correlates the address of the node within which the user is located when making a query to the engine via a wireless device such as a laptop computer, PDA or cellular telephone. The time of the query is also accounted for. The query causes the search engine to focus its database search (from a large array of information resources indexed by the search engine and accessible thereby) on those informational items/web sites that fit the appropriate place and time of the user. In particular wireless nodes are viewed as spatial aggregation units (such as polygons) of demarcation in an urban or other densely settled environment (e.g. a university campus, institution, etc.) to identify and characterize the physical environment of the information-seeker.

The invention provides an environmental health risk monitoring and warning system and method based on a spatial Bayesian network. Spatial correlation and spatial aggregation factors are added on the basis of the traditional Bayesian network, optimized machine learning is closely combined with domain knowledge, and therefore, a network model with a more reasonable structure is generated. A spatialcorrelation/aggregation inference link is added on the basis of existing factors, and thus, the ability of Bayesian network environmental health risk monitoring and warning is enhanced. The hand-foot-mouth disease is affected by many environmental factors, which is taken as an example in the invention to explain the principle of the spatial Bayesian network and the advantage of the spatial Bayesian network in environmental health risk assessment and warning. The system and the method of the invention can be applied to the risk monitoring and warning of various diseases or health problems closely related to environmental health.

The invention relates to a detection system and method of a characteristic substance. The method comprises: step 1, analyzing tested samples by mass spectrometry to obtain mass spectrometric data of each sample point and determining areas of interest of tested samples; step 2, for the substances characterized by the same m/z, respectively calculating the total ion strength of all sample points inside and outside the areas of interest, the mean strength of all sample points inside and outside the areas of interest and/or the spatial aggregation of the substance characterized by m/z inside the areas of interest, and judging whether the substance characterized by m/z is the characteristic substance according to pre-set rules. Researchers can accurately and precisely analyze and detect which substances are specific substances of the samples and which substances are background noises inside the areas of interest, relative contents of all characteristic substances and spatial distribution within the samples.

The invention provides a spatial aggregation based trajectory data privacy protection method and system. According to the method, an individual privacy risk metric value is acquired through establishment of a privacy model, the individual privacy risk metric value and the actual condition of urban population risk spatial distribution are compared, privacy risk protection is established, and an effect after privacy protection is evaluated. The method is simple, low in calculation complexity and applicable to spatial data with large data volume.

The invention discloses a geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. Comprising the following steps: aiming at spatial aggregation and dispersion characteristics of historical geological disaster points, respectively proposing a data preprocessing method which uses a clustering algorithm to extract regional clustering attributes as evaluation indexes and is based on a fishing net grid; constructing a model based on a multi-machine learning algorithm of logistic regression (LR), a support vector machine (SVM), a gradient boosting tree (GBDT) and a random forest (RF); determining an optimal model by comparing the model prediction precision with the map evaluation effect, and drawing a dangerous map; meanwhile, providing an experimental scheme for testing the technical reliability. According to the method, the model evaluation precision can be remarkably improved, the model evaluation performance can be enhanced, and the geological disaster risk map with more accurate prediction and better quality can be generated, so that a decision basis conforming to the actual situation is provided for disaster risk prevention and control planning work.

The invention discloses a built-up area boundary identification method and device based on urban building space data. The method comprises the following steps: 1) inputting urban building space vectordata into a geographic information processing platform; 2) determining a critical value for distinguishing whether the building is continuous or not; 3) generating a building continuous area surfacearea; and 4) screening a group of building continuous area surface areas with the highest building area values, and exporting boundary contour lines of the building continuous area surface areas. Thedefects that an existing recognition method is long in time consumption, large in manpower input, large in boundary recognition dependence on human brain judgment randomness, high in image quality requirement and the like are overcome. High-precision identification of the boundary of the urban built-up area is realized through data clustering analysis and spatial aggregation, and various index data and vector analysis requirements required by the urban development and construction current situation assessment and planning field are efficiently and accurately met.

The invention discloses a spatial autocorrelation region-based spatial-temporal difference detection method for vegetation net primary productivity. . The method comprises the following steps: S1, obtaining MOD17A3 data of research years of a research area; S2, extracting a vegetation NPP graph of a research time period of the research area; S3, calculating the vegetation NPP variable quantity inthe research time period of the research area; S4, calculating a global Moran index, a high/low value clustering index and related statistics of the vegetation NPP in the research period of the research area; S5, judging the overall clustering characteristics of the research area data samples; S6, calculating a hot spot analysis index of the vegetation NPP in the research time period of the research area; S7, judging hot spot distribution characteristics of the vegetation NPP in the research time period of the research area; S8, calculating a local Moran index of annual vegetation NPP variation in the research period of the research area; S9, judging spatial aggregation characteristics of annual vegetation NPP variations in the research period of the research area. The overall clustering characteristics of the vegetation NPP can be accurately expressed, and the local spatial characteristics can be detected.

The invention discloses a method for detecting a coupling relation between land utilization and surface temperature based on urban agglomeration scale. The method comprises the following steps: (1), obtaining land utilization data of a to-be-researched region, and analyzing landscape pattern features; (2) acquiring remote sensing image data of a to-be-studied region and performing surface temperature inversion to obtain surface temperature; (3) performing global Moran index calculation and local Moran index calculation based on the surface temperature, and then discussing the spatial aggregation characteristic of the surface average temperature of the to-be-studied region; and (4) obtaining the average surface temperature in each grid unit and the utilization ratio of each type of land ineach grid unit, and analyzing the coupling relationship and the spatial instability between the regional land utilization and the surface temperature by adopting a geographically weighted regression model. According to the method, the landscape pattern index, the global Moran index, the LISA clustering graph and the GWR model are combined, the local coupling relation between land utilization and surface temperature of different regions in the urbanization process is effectively disclosed, and breakthrough in precision is achieved.

The embodiment of the invention provides an object feature information acquisition method and device, object classification method and device and information pushing method and device. When the feature information of an object is determined, a plurality of neighbor nodes of a first node are respectively determined from N relational networks at N moments, so that N neighbor node groups can be obtained; spatial aggregation features of the first node at each moment is determined based on the neighbor node group corresponding to each moment and the node feature of the first node; the N spatial aggregation features at the N moments are inputted into a sequence neural network sequentially according to a time sequence, N spatial-temporal expressions of the first node at the N moments can be obtained respectively; and the N space-time expressions are aggregated, so that the space-time aggregation feature of the first node can be obtained and is adopted as the feature information of a first object represented by the first node.

The invention discloses a space-time evolution analysis method for a regional water conservation function. The method comprises the following steps: carrying out grid processing on collected data through the collected rainfall data, temperature data, DEM data, soil composition data and soil type data of a meteorological station, and carrying out normalization and ArcGIS software by adopting a maximum and minimum value method to obtain grid data of each factor; according to the grid data of each factor, on the basis of an NPP quantitative index method, calculating by combining ArcGIS software to obtain annual ecosystem water conservation service capability index grid data; analyzing according to the annual ecosystem water conservation service capability index grid data to obtain a time change trend of the water conservation service capability in the whole region space; and the time variation degree of the water conservation service capability in the regional space, the spatial aggregation characteristic of the water conservation function in the regional time and the time variation characteristic of the water conservation function in the different soil types of regions.

The embodiment of the invention discloses a point cloud classification method and device. A specific embodiment of the method comprises: obtaining initial point cloud data; randomly extracting first point cloud data from the initial point cloud data based on a preset first downsampling rate; randomly extracting second point cloud data from the first point cloud data based on a preset second downsampling rate; respectively inputting the first point cloud data and the second point cloud data into a pre-trained spatial aggregation network to obtain first feature data and second feature data; extracting feature data corresponding to the second point cloud data from the first feature data, and aggregating the extracted feature data and the second feature data into third feature data; and inputting the third feature data into a pre-trained point cloud classification network to obtain category information corresponding to each point in the second point cloud data. According to the embodiment,uniform sampling with different densities is carried out on the point cloud data, the richness of extracted features is improved, and the performance of carrying out small target segmentation on thepoint cloud is improved.

The invention relates to a spatio-temporal scanning clustering method for irregular shapes based on maximum correlation and risk deviation, belonging to the field of spatio-temporal event clustering analysis method. The method comprises the steps of firstly obtaining the graphic structure of the research area according to the case onset location and traffic route in the research area; then, according to the maximum correlation (mlink) algorithm,determining the scan window shape and searching the potential aggregation region; then calculating the relative risk and risk deviation of the aggregation to determine whether the relative risk of the aggregation is accurate or not. Compared with the prior art, the method of the invention changes the scanning window of the conventional cylindrical spatio-temporal scanning method, and the spatial base thereof can be irregular in shape, thereby facilitating the discovery of the spatial aggregation of unconventional shapes. And the relative risk bias assessment is introduced to assess the risk of aggregation. If the deviation is too large, the region can be further observed to avoid causing unnecessary loss of early warning and increase the reliability of disease prevention and early warning in the region.

The invention discloses a sagger crack detection method based on a fan-shaped neighborhood difference histogram. The method includes: acquiring a surface image of a to-be-detected sagger; roughly extracting a crack binary image with a lot of noise through detail extraction and threshold segmentation; constructing a fan-shaped filter according to characteristics of spatial aggregation features anddirection features of crack pixels in the neighborhood, and convolving the fan-shaped filter with the image to obtain the fan-shaped neighborhood difference histogram; analyzing the crack distributionprobability feature difference between the crack pixels and non-crack pixels by using the fan-shaped neighborhood difference histogram, and separating the crack pixels from the non-crack pixels, andrealizing extraction of cracks; and obtaining an evaluation factor reflecting the degree of cracking by extracting global and local length and area features of the cracks, and evaluating the crackingdegree level. The invention can realize accurate and complete detection effect on various types of cracks on a bottom surface of the complex sagger, and can be applied to the automatic detection of the cracks of the sagger.

The invention discloses an infrared weak and small moving target detection method based on track point space-time consistency. The infrared weak and small moving target detection method comprises thesteps: firstly extracting dense tracks with different lengths for continuous video frame images, and generating a dynamically changing track list; secondly, detecting candidate targets in the single-frame image by using a track point space aggregation model, and eliminating pseudo targets by using motion characteristics; providing a contour number coding mechanism for aggregation representation oftrack points; and finally, for continuous video frames, proposing a backward target detection algorithm based on contour code word consistency. According to the infrared weak and small moving targetdetection method, the spatial aggregation and the time consistency of the target track points are considered at the same time, so that the maximization of the detection rate and the minimization of the false alarm rate are solved.

The invention discloses a spatial aggregation scale selection method for geographic big data. The spatial aggregation scale selection method comprises the steps of performing equidistant grid divisionon a research area under different grid sizes; generating scale snapshots of different scales; establishing point clouds for snapshots of different scales; carrying out topological feature extractionon the point cloud of each scale snapshot; performing scale effect analysis on each scale snapshot to obtain a key scale; and selecting a required spatial aggregation scale according to the key scale. According to the method, a topology analysis method is introduced; clustering modes, annular modes and inter-class modes of data under different aggregation scales are described through multi-ordertopology invariants, the influence of the scale effect is quantified through evolution of the modes, then scale mutation points are found to judge a proper research scale range, and therefore the selected spatial aggregation scale better meets actual requirements.

The invention discloses a customized bus route design method and system based on precise OD big data, and the method comprises the steps: obtaining precise passenger flow OD data of a recent period oftime of an urban bus whole-line network, carrying out the aggregation of the OD data according to time, carrying out the spatial aggregation of the OD data of a starting station and a terminal station which meet the conditions, and planning head and tail stations of a customized bus route and the route of the customized bus route; according to the method, the reasonable customized bus route is designed on the basis of passenger flow rules of the accurate OD big data, and the rationality of customized buses is improved.

According to the positioning method and device for the user occupational and residential places, the control equipment and the storage medium provided by the invention, the positioning data of the user within the preset duration is acquired, and the positioning data is used for indicating the position of the user at different time; spatial aggregation processing is carried out on the positioning data of the user to generate aggregation points, and the aggregation points have aggregation point appearing time; the aggregation point of which the aggregation point appearing time meets a preset time condition is determined as an effective aggregation point; and according to the effective aggregation point, user job and residence place information is positioned and output, that is to say, the positioning data of the user is analyzed from two dimensions of space and time, so that a more accurate user occupational and residential place is positioned.

The invention discloses a virtual space system based on geographic information, and relates to the technical field of oil production engineering, and the virtual space system comprises a system layer which is composed of a data layer, an engine layer, an operation and maintenance layer, a service layer and an application layer, and also comprises a function module, the function module is composed of a spatial data information base construction part, a spatial data information and spatial data processing and storage part, a spatial system basic publishing engine part and a web-based three-dimensional visualization control part. According to the method, the PostGIS is adopted to provide support for complex space objects and support space indexes, in the aspect of space operation, the PostGIS supports space operators and provides a space analysis function, a space aggregation function and a space maintenance function, in addition, in the aspect of space operation, the PostGIS provides the functions of space data coordinate projection system conversion, sphere length calculation and the like, and the space data coordinate projection system conversion function, the sphere length calculation function and the like are integrated. According to the method, space transportation is more accurate, and space management and analysis functions such as storage and management of space geographic data and geometric topology analysis can be provided for the PostGIS by expanding the PostgreSQL database through the PostGIS.

The present invention discloses a high-resolution image building area extraction method based on multi-scale texture spatial clustering. The method comprises: performing multi-scale wavelet decomposition on an input image to obtain a low-frequency approximate coefficient image and a multi-directional high-frequency detail coefficient image; in each decomposition layer, performing feature integration on the multi-directional high-frequency detail coefficient image to obtain a texture intensity image; performing spatial autocorrelation statistical analysis to obtain a spatial distribution pattern image of each layer texture intensity; performing cross-scale feature fusion; and performing spatial clustering detection, and obtaining a building area through threshold segmentation. According tothe method provided by the present invention, that the texture features of the building area in the high-resolution image have the characteristics of multi-scale and spatial aggregation distribution are fully considered; and not only automatic extraction of the high-resolution image building area can be effectively implemented, but also complete contour boundaries can be maintained for the extracted building area, and further vectorization processing and application can be facilitated.

The invention discloses an atmospheric pollution source map construction method and device, a storage medium and electronic equipment, and belongs to the field of atmospheric monitoring. According to the method, mutual verification and supplement among databases are carried out in combination with pollution source data of multiple sources, mutual evidence is carried out according to pollution source positions, spatial aggregation degrees, atmospheric pollutant emission and the like in the pollution source data, and a pollution source credibility evaluation system is established; according to the pollutant concentration inversion result, the pollution source with high reliability is reserved, and the pollution source with low reliability is filtered out; dividing the reserved pollution sources into a plurality of classes, performing grid division on the to-be-constructed region according to a certain size, and performing analysis to obtain information of various pollution sources of each grid; in each grid, the color of the grid is rendered according to the influence of various pollution sources on the pollutant concentration, and an atmospheric pollution source map is obtained. According to the invention, a set of refined atmospheric pollution source atlas with data fusion is established, and a basis is provided for scientization and precision of atmospheric pollution management.