116 results about "Data resolution" patented technology

The invention relates to the technical field of time-space sequence prediction, in particular to a downscaling prediction method for a sea wave significant wave height time sequence, which can simulate a downscaling process from low-resolution sea wave data to local high-resolution sea wave data. The method comprises the following steps of 1, generating large-range low-resolution effective wave height data based on the SWAN; step 2, generating local high-resolution effective wave height data based on SWAN-ADCIRC; and step 3, performing downscaling prediction on the significant wave height based on the time sequence network model. On one hand, the problem of low resolution of numerical model prediction data can be efficiently solved, and on the other hand, the prediction precision of the significant wave height value can be improved. The method is especially suitable for scenes with higher requirements for spatial resolution and significant wave high prediction precision, and significant wave high-precision prediction is carried out on coastal sea areas with complex geometrical shapes.

The invention relates to the technical field of drilling of soil layers or rocks, and particularly discloses a rotating speed-temperature-abrasion shield cutter intelligent sensing method. Comprising the steps of micro-synchronous acquisition, coupling feature extraction, physical modeling and synthesis, multi-scale thermal anomaly separation, physical constraint neural network prediction, time sequence evolution prediction, dynamic threshold risk decision, adaptive sampling scheduling and online increment calibration. According to the method, the early warning threshold value is dynamically adjusted based on prediction error distribution, flexible switching among different reliability requirements can be achieved, and the problem of early warning or missing report is avoided. Meanwhile, the sensing sampling frequency and the early warning level are linked, the data resolution can be automatically improved in the high-risk stage, calculation and transmission resources are saved in the low-risk stage, and efficient utilization of the computing power and the bandwidth by the monitoring system is achieved; therefore, distribution drifting caused by geological condition changes and cutter updating can be coped, the effective service life of the model can be prolonged, and shutdown maintenance is almost not needed.

The invention belongs to the technical field of petroleum and natural gas engineering, and particularly relates to a logging data intelligent inversion method fusing an attention mechanism and a convolutional neural network, and the method comprises the steps: S100, selecting a borehole which is subjected to well drilling and logging as a research well, and obtaining the drilling, logging and logging data of the research well; s200, constructing a unified depth reference of well drilling, well logging and well logging data, and improving the resolution of the well drilling and well logging data by adopting an interpolation method; s300, constructing an attention mechanism module based on a multi-scale adaptive compression excitation network, and improving the modeling capability through triple mechanisms of multi-scale feature perception, adaptive compression proportion adjustment and residual enhancement attention weight; s400, constructing a feature extraction and regression prediction network based on a one-dimensional convolutional neural network; s500, supervised learning training is carried out on the inversion model until comprehensive evaluation index requirements are met; and S600, intelligent prediction and inversion of the logging curve are carried out by using the trained model. According to the invention, intelligent and efficient inversion of the logging data by using the low-cost drilling and logging data is realized.

A computer-implemented method for classifying pixel data comprising an object of an object type. The method includes determining, by a first neural network, from the pixel data, a center and a stride for a predetermined kernel, wherein the kernel comprises at least part of the object when placed over the pixel data in accordance with the center and the stride; filtering the pixel data by subsequently convolving the pixel data with the kernel at locations defined by the center and stride, to obtain reduced pixel data thereby comprising at least part of the object, wherein the reduced pixel data has a predetermined resolution smaller than a data resolution of the pixel data; and classifying, by a second neural network, the reduced pixel data to the object type, thereby obtaining an object type classification.

The application discloses a testing device and testing method for hydrogen isotope permeation parameters of fusion reactor application materials, relates to material testing technology in the hydrogen field, and belongs to the field of material testing technology in the hydrogen field. The testing device for hydrogen isotope permeation parameters of fusion reactor application materials comprises the following: a membrane sample is installed in a membrane testing cavity to divide the membrane testing cavity into a gas inlet sub-cavity and a permeation sub-cavity; a gas inlet tank is selectively communicated with the gas inlet sub-cavity to supply testing gas; a collecting tank is selectively communicated with the permeation sub-cavity to collect outflowing gas; a vacuum pumping structure is used for pumping the gas inlet sub-cavity and the permeation sub-cavity; a heating structure is used for heating the gas inlet sub-cavity and the permeation sub-cavity; a high-resolution mass spectrometer is selectively communicated with the permeation sub-cavity, and gas selectively flows into the high-resolution mass spectrometer. The testing device for hydrogen isotope permeation parameters of fusion reactor application materials can control the gas pressure and temperature in the two sub-cavities respectively, thereby accurately determining the hydrogen isotope permeation parameters of the materials, and the precision is high, and the data resolution is excellent.

The invention discloses a device and a method for testing hydrogen isotope permeation parameters of a fusion reactor application material, and relates to a material testing technology in the field of hydrogen, and the device for testing the hydrogen isotope permeation parameters of the fusion reactor application material is characterized in that a membrane sample is mounted in a membrane testing cavity to divide the membrane testing cavity into a gas inlet sub-cavity and a permeation sub-cavity; the gas inlet tank is selectively communicated with the gas inlet sub-cavity so as to supply test gas, and the collecting tank is selectively communicated with the permeation sub-cavity so as to collect flowing-out gas; the vacuumizing structure is used for vacuumizing the air inlet sub-cavity and the permeation sub-cavity, and the heating structure is used for heating the air inlet sub-cavity and the permeation sub-cavity; the high-resolution mass spectrometer is selectively communicated with the permeation sub-cavity, and gas selectively flows into the high-resolution mass spectrometer. According to the device for testing the hydrogen isotope permeation parameters of the fusion reactor application material, the air pressure and the temperature in the two sub-cavities can be controlled respectively, so that the hydrogen isotope permeation parameters of the material can be accurately measured, the accuracy is high, and the data resolution is excellent.

The invention relates to the field of meteorological data processing, and discloses a method for generating downscaling data by using mesoscale data and spatial topographic data, which comprises the following steps of: generating a global scale transformation coefficient alpha according to the data resolution a of the mesoscale data in a specified area and the resolution b of the downscaling data specified by a user, and if alpha is greater than or equal to a preset threshold value n, generating a global scale transformation coefficient; if yes, interpolating the mesoscale data by using the high-resolution meteorological observation data and the spatial dynamic weight of the grid to obtain intermediate data with the resolution of m, and m is greater than a and less than b; calculating a terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain evaluation feature group, and generating an adaptive intermediate scale transformation coefficient beta corresponding to each grid according to the terrain complexity index, the observation station density and the intermediate scale data quality; according to the beta and the intermediate data, downscaling data corresponding to each grid are generated, and the installation position of the fan is determined and the fan is installed. Therefore, the precision of downscaling data is improved, and the refined requirement of fan site selection is met.

Provided herein is an electronic device and a method of image processing. An electronic device may include an image sensor configured to obtain image data including color pixel values and texture pixel values using color pixels and texture pixels, a resolution converter configured to generate two or more images having different sizes based on the image data, a parameter calculator configured to acquire two or more parametric images, respectively corresponding to the two or more images, using converted color pixel values and converted texture pixel values that are included in each of the two or more images, a parameter synthesizer configured to acquire a synthesized parametric image by synthesizing the two or more parametric images, and an image acquirer configured to acquire an output image based on the synthesized parametric image and the texture pixel values included in the image data.

The invention relates to the technical field of motor performance testing, and discloses a micro-control motor multi-dimensional dynamic loading testing system and method, and the system comprises a cooperative control and data collection module which synchronously collects the electrical parameters, the multi-point temperature, the load torque and the rotating speed of a micro-control motor to be tested through a hardware synchronous triggering mechanism, the data processing and performance analysis module performs loss separation calculation considering real-time temperature on the basis of the data to generate a three-dimensional MAP graph representing a performance coupling relation, and is configured to analyze the data in real time and identify a performance parameter abnormal change area in the testing process, so that the performance parameter abnormal change area is analyzed in real time. And an instruction is automatically sent to the cooperative control and data acquisition module so as to increase the density of test working condition points of the area, and self-adaptive test is realized. According to the invention, the performance characteristics of the to-be-tested micro-control motor under a complex working condition can be accurately and comprehensively obtained, and the automation degree and the data resolution of the test are improved.

The invention relates to the technical field of resource allocation, in particular to an object recognition and task processing method and system for multi-machine cooperation of competition robots, and the method comprises the following steps: pre-defining a plurality of service quality levels of a target object recognition and path planning calculation task of a cooperative robot, and obtaining CPU resource consumption and task processing performance corresponding to each grade based on the visual sensor data resolution and algorithm complexity under different grades. According to the method, a plurality of task service quality levels are defined in advance, and different visual data resolutions and algorithm complexity are mapped into CPU resource requirements and task processing performance indexes, so that quantitative association of tasks and resource requirements is realized, and the resource allocation accuracy is improved; and meanwhile, the CPU load state of the robot cluster is sensed in real time, a dynamic QoS regulation and control instruction is established by combining the load change trend and the task emergency degree, and the real-time performance and sensitivity of resource allocation are improved.

The invention discloses a space transcriptome and space metabolome integration method based on deep learning. The method comprises the following steps: firstly, acquiring original space transcriptomics data and original space metabonomics data of a biological tissue, and performing data preprocessing to obtain a preprocessed biological tissue data set; then, aligning space metabonomics data in the preprocessed biological tissue data set to space transcriptomics data, unifying data resolution, obtaining corrected space metabonomics data, and updating the biological tissue data set; and finally, generating to-be-integrated sample data from the newest biological tissue data set, inputting the to-be-integrated sample data into the spatial multi-omics data integration model, and outputting final joint embedding by the model, so that cross-modal and cross-sample effective integration of the data can be realized. According to the method, the problems of form and resolution inconsistency and batch effect caused by technical difference of ST and SM data are solved, and high-precision and interpretable spatial multi-omics integration analysis is realized.

The invention provides an atmospheric data analysis method based on observability analysis, belongs to the field of aircraft navigation, guidance and control, and aims at solving the problem that the precision of traditional atmospheric data measurement is reduced under the condition of a complex flow field or a sensor fault. In the prior art, due to lack of strict theoretical support, estimation errors and convergence problems may occur in a transonic stage. According to the method, an atmospheric data state space model is established through flight dynamics, and atmospheric data estimation is carried out by adopting an extended Kalman filter based on the model; in the estimation process, observability analysis is executed to verify the reliability of an estimation result; and finally, optimizing the system model according to an analysis result. According to the method, the dependence of an existing method on an empirical model is reduced, the convergence and the stability of an estimation result are ensured, and the adaptability and the robustness of a system in a complex environment are remarkably improved.

The invention relates to the technical field of data three-dimensional visualization, and discloses a volume rendering method and system based on WRF output data, and the method comprises the steps: carrying out the interpolation processing of the WRF output data to improve the data resolution, and building a bounding box; the emergent point and the light direction when the light passes through the bounding box are solved, and the sampling step length is adjusted by using the light length and the resolution; at each sampling point, calculating a color value and an opacity value of the point by adopting a layered resampling method and applying different interpolation methods; and finally, calculating reflectivity and shadow parameters by combining the gray scale and gradient information of the sampling points so as to optimize a transmission function and calculate a final color value of the pixel point. The method provided by the invention can realize efficient and accurate meteorological data volume rendering, has relatively high rendering quality and performance optimization, is suitable for the fields of weather forecast, environmental perception and the like, and can provide powerful technical support for meteorological data analysis and environmental monitoring and prediction.

The invention belongs to the field of seismic data processing, relates to a stable deconvolution method based on mixed norm constraint and frequency band optimization, and aims to solve the problems that the prior art is easily interfered by noise and is insufficient in data resolution and dynamic optimization in a low signal-to-noise ratio and complex geological environment. The method comprises the following steps: obtaining a linearization frequency division operator according to collected seismic data; based on a linearized frequency division operator, establishing a stable deconvolution objective function of sparsity constraint and mixed norm constraint; decomposing the target function into a plurality of sub-problems based on an ADMM algorithm, alternately optimizing and updating each variable, and solving an approximate optimal solution; and stopping iteration until a termination criterion is met, taking the variable obtained at the last time as an approximate optimal solution, and outputting a stable deconvolution result. According to the method, the mixed norm sparsity constraint and the linearization frequency division operator are introduced, so that the sparsity of data can be effectively kept, meanwhile, the dominant frequency band range is accurately controlled, and the authenticity and precision of a deconvolution result are ensured.

The invention discloses a long-period prediction and forecast method for a sea surface marine environment. The method comprises the following steps: acquiring GLORYS reanalysis data and satellite observation data; data preprocessing including data latitude expansion, data resolution alignment, mask marking, exception processing and data normalization is carried out on the collected data; 3, constructing a training and testing sample by adopting a sliding window strategy; constructing a spatio-temporal data set; through ConvLSTM layer stacking design, space-time dependency of data is extracted, and redundant information is reduced through feature fusion compression; constructing a hybrid model special for a sea surface downscaling prediction task through feature up-sampling, a convolution refining network and residual connection; a hybrid model of a sea surface downscaling prediction task is trained, long-period prediction and forecast of a sea surface marine environment are carried out, feature level fusion of GLORYS and satellite data is realized, prediction and downscaling tasks share a feature extraction network, error accumulation of staged training is avoided, and calculation efficiency is improved.

The invention relates to the technical field of automobile glass processing, in particular to an automobile glass film removing method and a matrix film removing system. The method comprises the steps that three-dimensional contour scanning is conducted on an area, to be subjected to film removal, of the automobile glass, a space coordinate grid of the surface of the glass is constructed based on scanning data, the area, to be subjected to film removal, is divided into a plurality of sub-area units with unique coordinate identifiers, and the precision of the coordinate grid is dynamically adjusted according to the curvature of the curved surface of the glass. The grid density of the area with the large curvature is higher than that of the plane area. According to the method, a dynamic coordinate grid division mechanism based on three-dimensional contour scanning is constructed, the grid density is intelligently adjusted according to the curvature characteristics of different areas of the curved glass, and it is ensured that the machining precision of the area with the large curvature is matched with the data resolution; the problem that in a traditional method, the fixed grid precision cannot adapt to the complex form of the curved surface is fundamentally solved.

The present invention relates to the technical field of drilling in soil or rock, and specifically discloses a method for intelligent perception of speed-temperature-wear of shield tools, including: micro-synchronous acquisition, coupled feature extraction, physical modeling fitting, multi-scale thermal anomaly separation, physical constraint neural network prediction, time series evolution prediction, dynamic threshold risk decision-making, adaptive sampling scheduling and online incremental calibration; the present invention dynamically adjusts the warning threshold based on the prediction error distribution, and can flexibly switch between different reliability requirements to avoid the problems of "premature warning" or "missed reporting". At the same time, linking the sensor sampling frequency with the warning level can automatically improve the data resolution in the high-risk stage, save computing and transmission resources in the low-risk stage, and realize the efficient use of computing power and bandwidth by the monitoring system; therefore, it can not only cope with the distribution drift caused by changes in geological conditions and tool updates, but also extend the effective life of the model, with almost no need for downtime maintenance.

The invention belongs to the field of intelligent security and protection, and relates to an intelligent decision support method for frontier public opinion and multi-source data fusion, which comprises the following steps: S1, collecting preset type public opinion data and preset type basic multi-source data related to frontier protection, and removing public opinion data and multi-source data irrelevant to frontier protection by adopting a data filtering algorithm to obtain a data filtering algorithm; primary data screening is completed; s2, performing semantic and spatial dimension alignment on the primarily screened public opinion data and multi-source data, and cleaning the aligned data to process data conflicts to obtain an associated fusion data set; s3, extracting a corresponding data resolution, collecting frequency class decision demand indexes, and performing priority ranking on the demand indexes; s4, integrating the scheduled data with the public opinion data to generate a decision support report; and S5, collecting application effect feedback of the decision support report, and optimizing data acquisition key points and fusion parameters in the associated fusion data set based on the feedback.

The embodiment of the invention provides a seismic acquisition data speed normalization method and system, and belongs to the technical field of seismic data processing. The method comprises the following steps: performing signal reduction normalization processing on acquired data, eliminating the influence of frequency spectrum shaping equipment, classifying according to the type of a detector, forming a corresponding seismic data set, eliminating the influence of gain, and converting the signal into a unified speed physical quantity; determining a deconvolution operator according to the detector frequency response and executing deconvolution processing; performing high-pass filtering and unified calibration on the processed data to obtain data with consistent dynamic response; and outputting a speed normalization result based on the data. According to the scheme, the problem of inconsistent response caused by the type difference of the detectors is effectively reduced, and the seismic data resolution and the signal-to-noise ratio under the combined acquisition condition of multiple types of detectors are improved.

The invention relates to a reservoir identification method, and belongs to the technical field of oil-gas exploration and development. And determining the reservoir development condition of the target stratum by using the seismic data, the logging data and the gas testing data of the target stratum. Based on the reservoir development condition of the target stratum, wave crest and wave trough positions of the target stratum are determined through well seismic calibration and forward modeling, and structural interpretation is conducted on the wave crest and wave trough positions of the target stratum. And determining the seismic reflection characteristics of the reservoir by using a structural interpretation result, a multi-well synthetic record and a forward modeling result. Subtracting the wave trough horizon from the obtained wave crest horizon of the target layer to obtain a wave trough-to-wave crest interval time plane graph, and utilizing the interval time plane graph to construct a high-timeliness deposition interface. And recognizing the reservoir according to the determined seismic reflection characteristics of the reservoir and the constructed high-temporal sedimentary interface. According to the method, the problems that the improvement of the seismic data resolution is limited and the identification result is easily interfered by the seismic event time penetration, so that the reservoir identification precision is relatively low are effectively solved.

The invention discloses a power system load data super-resolution reconstruction method and system, and relates to the technical field of power system data quality improvement, and the method comprises the steps: carrying out the weighting of weather influence factors under different weather conditions, calculating the correlation between load data and weather influence factor data, obtaining key influence factor data, and carrying out the calculation of the correlation between the load data and the weather influence factor data; performing dynamic range normalization processing by adopting a sliding window; carrying out super-resolution reconstruction on the normalized data by adopting a trained time sequence generation model to obtain high-resolution load data; the time sequence generation model adjusts the weight of a loss function according to the change of the current training loss during training, and after training is completed, super-resolution reconstruction is carried out by extracting combined features of normalized load data and key influence factor data. The defect of insufficient resolution of low-quality sampling data is overcome, the quality of reconstructed data is improved through meteorological factor constraints, and the problem that training or using requirements cannot be met due to the fact that the resolution of the sampling data is low is solved.

The invention discloses a generating capacity prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining at least two environmental data sets with different data resolutions, the environmental data sets comprising a plurality of environmental data members; for one target member in the target environment data set, sampling and fusing the target member, the members to be selected and the operation environment data of the wind power station to obtain a meteorological data set corresponding to the target member; and inputting the meteorological data set corresponding to each target member into the power prediction model to obtain a plurality of power generation prediction values of the wind power plant station in a preset time range, and determining a power generation final value and a statistical distribution result of the wind power plant station in the preset time range according to all the power generation prediction values. The method can accurately predict the generating capacity data of the future medium-and-long-term power generation field by using the medium-and-long-term fused meteorological data and the operating environment data of the wind power plant, and is high in generalization capability and high in prediction precision.

Embodiments of the present application provide a video transcoding method, a video display method, an apparatus and an electronic device, which relate to the field of video processing technology. The video transcoding method includes: acquiring a first image in an initial images obtained by stitching multi-channel video images; determining an initial resolution of each of initial sub-images; wherein each initial resolution is not greater than a maximum resolution of input data that can be supported by a preset encoder, and a target resolution of a target sub-image obtained after transcoding each initial sub-image is not greater than a maximum resolution of output data that can be supported by the preset encoder; segmenting a to-be-cut image into respective initial sub-images based on the respective initial resolutions; encoding each initial sub-image according to a target resolution of a target sub-image after transcoding this initial sub-image by using a preset encoder, to obtain each of target code-streams. Compared with the prior art, by applying the solution provided by the embodiments of the present application, it is possible to transcode the video screen obtained after stitching multi-channel video screens.

A system and method for generating synthetic high-frequency seismic data for subsurface imaging comprises injecting a first seismic shot having a low frequency into a surface region and receiving reflected waves at a plurality of seismic receivers. Seismic traces are recorded and processed to generate a low-frequency seismic shot gather. A second seismic shot having a high frequency is injected, and a sparse number of high-frequency traces are recorded. A computing device resamples the low-frequency traces to match the sparse high-frequency dataset and applies a one-dimensional (1D) local matching filter to transform the low-frequency traces into simulated high-frequency traces. The transformed dataset is used to generate a high-resolution subsurface image of geological interfaces. The system enables computationally efficient high-frequency seismic data synthesis, optimizing seismic inversion accuracy while reducing acquisition costs and computational overhead.

The application provides a transformer area load data resolution improvement method and device, and relates to the technical field of data processing. The method comprises the following steps: encoding and processing transformer area load low-resolution time series data based on a time series encoder to obtain a hidden state vector of hidden input data time series characteristics; decoding and processing the hidden state vector of the hidden input data time series characteristics based on a time series decoder to obtain a hidden state vector of each interpolation time; mapping the hidden state vector of each interpolation time to an interpolation result based on a fully connected neural network layer, inserting the interpolation result into the transformer area load low-resolution time series data, and obtaining transformer area load high-resolution time series data. The device executes the above method. The transformer area load data resolution improvement method and device provided in the application can effectively improve the interpolation accuracy of transformer area load data.

A data set is stored in memory circuitry, the data set indicating a state of a semiconductor manufacturing process or a semiconductor structure manufactured thereby. Features in the data set are resolvable within limits constrained by a data resolution. A machine learning model is also stored in the memory circuitry, the machine learning model including parameters having respective assigned values constrained by a model training process. Processor circuitry communicatively coupled to the memory circuitry generates an output data set from the data set in accordance with the machine learning model such that features in the output data set are resolvable within limits constrained by an output data resolution, the output data resolution being finer than a data resolution of the data set.

A super-resolution system that increases the resolution of image data captured by one or more cameras comprises one or more controllers with one or more super-resolution neural networks containing a model for obfuscated image data and / or a model for focused loss. The one or more super-resolution neural networks receive paired training data during a training phase. The paired training data is representative of the image data captured by the one or more cameras, depicting an environment, and includes low-resolution and high-resolution image data. The low-resolution obfuscated image data and the high-resolution image data both represent identical images, and the low-resolution obfuscated image data contains an object of interest located in the environment that has been obfuscated using an obfuscation technique.

A parameter estimation section 106 is configured to perform estimation, for aggregate data in which values are associated with respective regions obtained by subdividing a space and for a Gaussian process model that expresses a plurality of aggregate data of differing partition granularity. The estimation is performed based on the Gaussian process model including a spatial scale parameter of a correlation function between regions of the aggregate data and including a noise variance parameter of the correlation function, by estimating the spatial scale parameter and the noise variance parameter so as to maximize a function expressing values of the aggregate data by area integrals of a Gaussian process. A high resolution data computation section111 is configured to perform computation in the Gaussian process model including the estimated spatial scale parameter and noise variance parameter by computing high resolution data in which the resolution of the subject aggregate data is enhanced by taking area integrals of the values of the aggregate data subject to enhancement, by computation for each region obtained by subdividing the space at the granularity indicated by the target partition.

The invention discloses a medical equipment operation state monitoring and data analysis method and system, and belongs to the technical field of information processing. The system comprises a virtual conference management module, a virtual reality interaction module, a digital supervision module, a main control module and a data storage module. The virtual conference management module is used for creating a virtual conference space, performing identity verification on participants and creating virtual characters; the virtual reality interaction module is used for collecting conference data generated by participants in reality, converting the conference data into virtual data and projecting the virtual data into the created virtual conference space; the digital supervision module is used for synchronizing the virtual data converted from different participants according to the time frames and recording the conference data analysis rate; the main control module is used for analyzing the conference data analysis rate and automatically adjusting the resolution display of the virtual data according to the analysis result; the data storage module is used for storing historical conference data and historical virtual data.

The invention discloses a multi-source meteorological data fusion processing method for new energy output prediction, and the method comprises the steps: S1, determining a new energy type and a core meteorological impact factor, and determining the data time granularity according to a prediction time scale; s2, collecting localized observation, reanalysis and numerical forecasting data, removing abnormal values and unifying the abnormal values into a structured format; s3, unifying data resolution through bilinear interpolation, and realizing meteorological data and station space matching in combination with station boundary cutting and space verification; s4, unifying all data as Beijing time, aligning timestamps, filling missing values according to priorities, and controlling a single factor missing rate to be less than or equal to 5%; s5, quality control is carried out from the dimensions of data integrity rate, spatial correlation and the like, and if the data does not reach the standard, an interpolation method is adjusted or a data source is supplemented; s6, outputting CSV or netCDF format fusion data according to requirements, and performing classified storage according to stations and time so as to facilitate subsequent association; the method has the advantages of being good in input data basis, good in output data space-time consistency and convenient to use.