43 results about "Temporal scales" patented technology

A method detects an object left-behind in a scene by updating a set of background models using a sequence of images acquired of the scene by a camera. Each background model is updated at a different temporal scales ranging from short term to long term. A foreground mask is determined from each background model after the updating for a particular image of the sequence. A motion image is updated from the set of foreground masks. In the motion, image, each pixel has an associated evidence value. The evidence values are compared with a evidence threshold to detect and signal an object left behind in the scene.

The invention provides a multiple spatial and temporal scale gradually-advancing load dispatching mode designing method. The method comprises the following steps: extracting the responding characteristics of schedulable load multiple temporal scales; designing a layering load dispatching mode based on load acting; designing the multiple spatial and temporal scale gradually-advancing power load dispatching mode. The multiple spatial and temporal scale gradually-advancing load dispatching mode designing method configures load side sources in day-ahead, intra-day and real time and other temporal scales and promotes the load side sources to participate in the dispatch and the operation of a power grid together with a generating side unit, so as to be favorable to improving the utilization rate of power equipment, promotes a daily load curve to gradually tend to be smooth, reduces the valley-to-peak difference of a system, and has great potentiality in promoting the digesting and saving aspect of large-scale renewable energy sources.

The present invention discloses a rip current forecasting method. The method comprises the following steps of: (1) based on a mesoscale marine weather operational forecasting system, acquiring wind fields and pressure fields of a target sea area and surrounding sea areas of the target sea area; (2) establishing a Delft3D hydrodynamic numerical model, and simulating to obtain tidal current fields of the target sea area and the surrounding sea areas of the target sea area; (3) establishing an SWAN wave spectral model, and simulating to obtain wave fields of the target sea area and the surrounding sea areas of the target sea area; (4) extracting boundary conditions from the wave fields, using the tidal current fields as background current fields, establishing an SWASH wave analysis model, and simulating to obtain a time sequence of a water particle velocity of the target sea area; and (5) accounting intensity of a rip current. In order to consider the dynamic characteristics of coupling of multiple physical mechanisms and coexistence of multiple spatial and temporal scales of the rip current, the method comprehensively utilizes various models to respectively stimulate different physical processes, considers the interactions between various models by using data exchange and coupling, and achieves real-time forecasting of the rip current of a bathing beach so as to provide references for beach rescue.

The invention relates to the technical field of wind/photovoltaic power generation, and discloses a wavelet packet-neural network-based wind/photovoltaic power prediction method. The method comprises the following steps of: firstly analyzing weather forecast data and history data, and assessing the influences, on the photovoltaic power prediction accuracy, of the weather forecast data and the history data; analyzing the influences, on the wind power prediction accuracy, of factors such as temperature, humidity and atmospheric pressure in the natural environment; carrying out wavelet packet decomposition on the data to obtain different frequency component sequences of a meteorological factor sequence and of a wind/photovoltaic active power sequence to ensure that the representation of change characteristics on various spatial and temporal scales is more accurate, wherein the low-frequency components and the high-frequency components respectively map the interior change rules of predicted objects from two scales of profile and detail; aiming at each sequence component, establishing a plurality of BP networks to carry out prediction; and finally reconstructing each network prediction output to obtain a complete prediction result. The method disclosed by the invention has the effects of improving the ability of mapping wind/photovoltaic power change characteristics and effectively improving the wind/photovoltaic power prediction accuracy.

A method and system is provided for obtaining data indicative of at least one characteristic of a continuous medium or at least one localized target located within a predetermined volume of space. The system includes a sensor configuration and a processing circuit. The sensor configuration includes a plurality of sensors for acquiring a plurality of signals from the continuous medium or the target(s). The plurality of sensors have centers spatially separated from each other in at least one spatial dimension. The processing circuit is configured for obtaining data indicative of the characteristic or characteristics of the medium or the target by calculating a plurality of powered weighted increments using the plurality of signals acquired by the sensor configuration and by using a plurality of models for relating the plurality of powered weighted increments to the characteristic or characteristics of the medium or the target. The selected characteristics of the medium or the target can be estimated with better accuracy and temporal and/or spatial resolution than is possible with prior art correlation function, spectra, and structure function-based methods and systems. In contrast to prior art methods and systems, the invention is not sensitive to signal contaminants with large temporal scale such as ground and sea clutter, and is not sensitive to low frequency external interference. The invention is capable of estimating various characteristics of the medium or target, for example, size, shape, visibility, speed, direction of the motion, and rates of changes of the above characteristics.

The invention discloses a GIS platform-based plain river network region pollution load calculation method. Aiming at the characteristics of plain river network region pollution source generation and migration, outputs and river pollutant loads of various pollution sources and various pollutants under the support of a GIS platform; four calculation modes are utilized to respectively calculate pointsource and surface source pollution loads of seven pollution sources such as urban domestic pollution, rural domestic pollution, urban rainfall runoff pollution, dry land rainfall runoff pollution, rice field rainfall runoff pollution, livestock breeding pollution and aquaculture pollution, and finally the pollution load of a whole plain river network region is determined. The method is not onlycapable of reflecting the pollutant migration characteristics of plain river network regions, but also capable of being popularized and applied on a relatively large spatial-temporal scale, so that the simulation precision in the migration process of pollutants from sources to river networks is improved.

The invention provides a coordinated source grid load control system for access of intermittent renewable energy to the power grid. The system comprises a layered and partitioned grid-connected key influential factor analysis module, a renewable energy set output characteristic representation module of multiple spatial and temporal distribution, a regional power production simulated calculation module based on multiple spatial and temporal scales and an intermittent renewable energy absorption capability measuring module. According to the invention, a renewable energy set output characteristicrepresentation equation of multiple spatial and temporal distribution is established, a power production simulated calculation module based on multiple spatial and temporal scales is established, renewable energy absorption measuring indexes are established, and the effect of a coordinated source grid load control strategy after that high-permeability intermittent renewable energy access the power grid is verified.

An agricultural drought early warning system disclosed by the present invention comprises the steps of calculating an unidirectional agricultural drought index; calculating a comprehensive agricultural drought index, and combining the unidirectional agricultural drought index to determine whether the agricultural drought occurs at a point in a region; calculating a regional agricultural drought index and a time agricultural drought index, determining the occurrence range and time of the agricultural drought determining the development status of the agricultural drought, and sending out the corresponding agricultural drought early warning. The agricultural drought early warning system integrates the more mature theoretical and practical drought indexes to monitor and early warn the regional agricultural drought, comprehensively considers a plurality of factors causing the agricultural drought, improves the agricultural drought monitoring reasonability, expands the spatial and temporal scales of the agricultural drought monitoring, enables the users to compare and analyze the agricultural drought situations and degrees in different regions and at different time periods more flexibly, and improves the applicability.

The invention belongs to the video analysis fields of action recognition, expression analysis, video retrieval, robot navigation, man-machine interaction, target detection and track and the like, and discloses a quick detection method of spatio-temporal interest points with an invariable scale. The method comprises the steps: setting parameters of detection operators of the spatio-temporal interest points under different spatio-temporal scales; calculating an integral video after a continuous video frame is input; calculating a response function of the detection operators of the spatio-temporal interest points of a pixel, and obtaining an intensity response map with multiple spatio-temporal scales; selecting a spatio-temporal scale; obtaining candidate spatio-temporal interest points through non-maximum restrain; and removing candidate spatio-temporal interest points in the edge response, and obtaining a detection result. The quick detection method of the spatio-temporal interest points with the invariable scale can detect the significant and robust local spatio-temporal feature points, is accurate in locating; has strong capacity of resisting noise jamming; has certain abilities of shield-resistance, rotation and spatio-temporal scale change; is good in real-time performance; and can decide to output a sparse or dense point set according to selection of a domain value.

The invention relates to an urban traffic particulate matter emission estimation method. The urban traffic particulate matter emission estimation method includes the following steps that satellite images are selected and processed, an urban road network is processed through remote sensing interpretation, the dust load coefficients of urban roads are measured, a traffic particulate matter emission factor is calculated through simulation, and the traffic particulate matter emission is measured and calculated. The urban traffic particulate matter emission estimation method has the advantages that through the method, the remote sensing technology and the ground on-site monitoring and simulation analysis technology can be effectively combined; through the remote sensing technology, the characteristics of the urban traffic road network under different spatial and temporal scales can be fast and accurately acquired, and the defect that the overall conditions of the particulate matter emission of the road network cannot be reflected at fixed points on the ground can be compensated for. The advantages of high update, wide range, strong objectivity and the like of remote sensing are combined with the ground on-site monitoring and simulation analysis technology, for traffic particulate matter, the urban traffic particulate matter emission can be estimated, and first-hand data can be provided for control over the total amount of air pollutant emission.

The invention discloses an analysis model construction method for typical characteristics of the wind power output. Original data are obtained and data pretreatment is carried out, the data are normalized by using multi-temporal-scale wind power volatility and simultaneous rate valuation indexes, and segmented aggregation is carried out to reduce the dimension; on the basis of an improved adaptivefuzzy clustering algorithm, an adaptive function alpha (c) is calculated and an optimal cluster number c is determined, and wind power output characteristics are classified; a variation dispersion coefficient betai is introduced and wind-power output distortion data are rejected; and a partition weighted median probability estimation method is put forward and a wind-power output daily typical characteristic curve/zone band is identified and extracted. According to the invention, the multi-space-scale large-scale wind power output characteristics and monthly, quarterly or yearly multi-time-scale wind-power output characteristics are analyzed and calculated and the wind-power output daily typical characteristic curve/zone band is extracted, so that the power operation staff can master the wind-power output rule well and a technical reference is provided for real-time scheduling of a power system, scheduling plan arrangement, wind farm planning and system backup configuration.

The invention discloses a multisource sea surface temperature (SST) remote sensing product fusion method based on a robust fixed order filtering model. The method synthesizes a scale transformation problem in a time-space fusion process of SST remote sensing products, an uncertainty expression problem of the products and complicated space structure and time structure of an SST time-space process, fully utilizes the complementary characteristics of the remote sensing products in the aspects of spatial resolution, time-space integrity and accuracy characteristics and adopts hierarchical Bayes frame based on the robust fixed order filtering model to fuse infrared SST and microwave SST remote sensing products to obtain fine scale fusion data with the high accuracy, the complete space and the rich local spatial models. The method has more reasonable simulation of SST space trend, the seamless spatial-temporal scale transformation between the multisource remote sensing products and the quantification of the uncertainty of the remote sensing products are realized, and the method is suitable for high efficient calculation of remote sensing large data volume.

The invention belongs to the field of wind power climbing prediction, and relates to a wind power climbing prediction model switching method based on gale weather classification. The method includes the following steps of firstly, exacting characteristic quantities which can obviously represent the gale weather and characteristic indexes which can obviously represent the gale weather according to historical gale fluctuation weather in an assigned geographical range and forming a distinguishing expression; secondly, calculating a prediction range, the weighting coefficient of the characteristic quantities and the weighting coefficient of the characteristic indexes through the distinguishing criterion in the Fisher distinguishing method, obtaining a distinguishing result and conducting verification and analysis through a statistic test; finally, further obtaining a gale weather classification result through the distinguishing method, and forming a switching mechanism for different statistical prediction models according to the characteristics of the spatial and temporal scales of different types of gale weather. According to the method, the climbing weather distinguishing research is conducted on a result of numerical weather prediction, and the method provides the more accurate switching mechanism for different statistical prediction methods in the aspect of comprehensive prediction methods of wind power climbing.

The invention discloses a remote sensing image data-based watershed multi-scale landscape pattern and water quality index relationship research method. The method includes the following steps that: 1)multi-spectral remote sensing images and high-resolution remote sensing images are utilized, and image spectra features are fused, and watershed landscape source and sink pattern information is extracted; 2) on the basis of digital elevation model data (DEM) and land use classification data, an improved landscape space load comparison index is calculated after minimum hydrological response unitsare obtained through division; 3) the Spearman correlation coefficient of an landscape pattern index and a water quality index is calculated, a comprehensive and low-redundancy landscape pattern indexsystem and water quality index system are constructed; and 4) a relation between the landscape pattern index and the water quality index is researched under a plurality of spatial and temporal scalesthrough using a multivariate analysis and canonical correspondence analysis method. According to the remote sensing image data-based watershed multi-scale landscape pattern and water quality index relationship research method of the invention, the landscape index is improved, the influence of the spatial and temporal scales on a relationship between a landscape pattern and non-point source pollution is considered in subsequent analysis, and therefore, an analysis result has a better ecological effect, and a problem that the optimal research scale of a traditional analysis method is uncertainin researching the relationship between the landscape pattern and non-point source pollution can be solved.

The invention relates to the field of ecological environment, and discloses a land automatic classification method based on multisource and multi-temporal satellite image data. The method comprises the following steps: A, multi-temporal data from the satellite are used for calculating a normalized differential vegetation index (NDVI) value of the land; B, according to the NDVI value, land classification baseline data are predicted to divide and classify the land, and a land initial classification table is acquired; and C, a gravity model and the land initial classification table are used for precisely classifying the land, and a land classification result is acquired. As the multi-temporal data serving as a data source has an exact time point, when the land automatic classification method in the embodiment of the invention is used, land automatic classification complete data in a specific temporal scale are acquired through processing the multi-temporal data at different time points, and evolution analysis of the land is facilitated.

The invention discloses an estimation method and device of an vegetation leaf area index. The method comprises the steps of building a correspondence relation of the leaf area index and atmospherical data, wherein the correspondence relation comprises a parameter to be determined; calibrating the parameter to be determined according to the correspondence relation, a leaf area index sample, and corresponding existing atmospherical data; determining the leaf area index according to the correspondence relation, the calibrated parameter to be confirmed, and forecast atmospherical data. According to the estimation method and device of the vegetation leaf area index, the leaf area index can be estimated through the forecast atmospherical data in future period, the change of the vegetation leaf area index can be simulated in a good spatial-temporal scale when the forecast atmospherical data is high in spatiotemporal precision, methods and evidence are provided for study of a rainfall runoff process without leaf area index observation data in future, and thus the estimation method and device of the vegetation leaf area index have important scientific value and application value.

The invention provides a basin erosion sediment production predicting method coupling with different spatial and temporal scale models. The method comprises the following steps: dividing a to-be-detected drainage basin into m sub drainage basins according to DEM topographic data and a remote-sensing image of the to-be-detected drainage basin; carrying out statistics on rainfall times n of each subdrainage basins every year, calculating the erosion sediment production and spatial distribution thereof of each sub drainage basin after rainfall every time and updating the DEM data of each sub drainage basin to obtain updated DEM data; and then calculating the gradient and slope length factor LSl of the lth sub drainage basin W1, and calculating the annular erosion sediment production f (Wl) according to a rainfall erosive force factor Rl, a soil erosive factor Kl, a slope length factor LSl, a vegetation factor Cl, an engineering measure factor El, a tillage measure factor Tl and a liner erosion coefficient factor Gl of Wl so as to further predict the annular erosion sediment production of the to-be-detected drainage basin W. The method overcomes the defect that the topographic parameters of a single experience model are immobilized and considers maldistribution of the rainfall space and the erosion sediment production mechanism fully, so that the soil erosion process can be betterreflected.

The invention relates to a compound posture and orbit control method for an underwater vehicle. The compound posture and orbit control method for the underwater vehicle is used for solving the limitation that the posture and orbit control of the underwater vehicle only depends on the hydrodynamic force. According to a temporal-scale difference between a hydrodynamic force control and a vectorial force control, a system is divided into a slow link and a fast link based on the singular perturbation method and the sliding mode variable structure control technology, a linear feedback control law of the compound posture and orbit control matched with a synovium theory is designed, the accelerated speed and the the pitch angle rate of the vehicle are taken as response parameters, so that the posture and orbit adjustment time can be effectively reduced, and the stability and the maneuverability of a whole system are improved. The compound posture and orbit control method for the underwater vehicle has the advantages that the dynamic response performance of the vehicle is improved and the stability of the vehicle is increased.

The invention, which relates to the technical field of ecology, discloses an ecological model simulation uncertainty analysis method. According to the method, key parameters of an ecological model arescreened and optimized, uncertainty of the key parameters is quantified, and the obtained parameter uncertainty is propagated into a simulation result; the largest contribution to the model simulation result uncertainty by certain parameter uncertainty is traced; and a bridge between field observation and model simulation is established. Therefore, the guidance is provided for future direction offield observation and the direction is also provided for the improvement of model process, so that the uncertainty of model simulation results is reduced continuously and the model simulation accuracy is improved. Moreover, according to different specific needs, the method can be applied to model simulation uncertainty analysis with different spatial-temporal resolutions and different time spansby combining different-scale observation data; and the different spatial and temporal scales of model simulation uncertainty study of the ecological system is supported.

The invention provides a progressive decision-making method for forecasting a rainstorm in a mountainous region. The progressive decision-making method comprises a multi-mode nested mountainous regionrainstorm progressive forecasting method, a forecasting mode nested node control method and a mountainous region rainstorm target evaluation and feedback method. According to the progressive decision-making method, a rainfall forecasting mode comprising a numerical weather forecasting product, comprehensive forecasting information based on high-altitude and ground atmospheric sounding data, radarand satellite prediction information and the like is adopted, and rainfall forecasting is gradually carried out according to spatial scales from large to small and time scales from long to short forthe differences of spatial-temporal scales and effects in various modes of forecasting, and a method of continuously evaluating a forecasting target, circulating frequency of a decision-making mode and forecasting results is adopted.

The invention provides a spatial and temporal scale matching method and device for land surface evapotranspiration remote sensing estimation. The method comprises the steps of: S1: acquiring a remote sensing data set with multiple spatial resolutions and multiple temporal resolutions; S2: according to the acquired remote sensing data set, carrying out spatial and temporal data fusion to obtain a remote sensing data set which simultaneously has a high temporal resolution and a high spatial resolution; S3: carrying out ET parametric inversion on the data set generated in the steps S1 and S2 by utilizing a preset data model so as to obtain a remote sensing parameter set with different temporal and spatial resolutions; S4: according to remote sensing parameters with different temporal and spatial resolutions, which are acquired in the step S3, respectively carrying out spatial and temporal scale matching judgment; and if a spatial scale matching judgment result and a temporal scale matching judgment result meet the accuracy requirement of ET estimation, using remote sensing data in the step S4, which meets the accuracy requirement of ET estimation, as a matched data set. The spatial and temporal scale matching method and device provided by the invention can solve the problem of uncertainty of ET inversion, which is caused by temporal and spatial scale matching uncertainty in existing ET estimation.