115results about How to "The predicted value is accurate" patented technology

The invention discloses a depth map intra prediction method based on a linear model. The gray values and coordinates of the previous line and left row of adjacent pixels of a current coded block are utilized to determine a linear model parameter; and according to the parameter and the pixel coordinate of the current coded block, the pixel gray value of the current coded block is predicted. According to the spatial character of a depth map, the depth map intra prediction method based on a linear model has the advantages of accurate prediction; meanwhile, because the previous line and left row of adjacent pixels of the current coded block are adopted to calculate the model parameter, a coding end does not need to code the model parameter; and a decoding end can directly determine the model parameter. The depth map intra prediction method can be applied to the coding standard of a three-dimensional video.

The invention discloses a complex scene-based human body key point detection system and method. The method comprises the following steps of: inputting monitor video information to obtain a single-frame static map and a multi-frame optical flow graph; extracting features of the single-frame static map through a convolution operation so as to obtain a feature map, and in order to solve influences, on personnel target detection, of interference targets under complex scenes, judging a practical confidence coefficient and a preset confidence coefficient of the feature map by adoption of a personneltarget detection algorithm so as to obtain a discretized personnel target surrounding box; and carrying out optical flow overlapping on the multi-frame optical flow graph to form a two-dimensional vector field; extracting features in the discretized personnel target surrounding box to obtain a feature map, obtaining key points and association degrees of parts, generating a part confidence map foreach part of a human body by utilizing a predictor, and accurately detecting human body key points through the part confidence map and the two-dimensional vector field. The system and method are usedfor human body key point detection under complex scenes so as to realize accurate detection of personnel target key points.

The invention discloses a method and device for obtaining a predicted value of a motion vector. The technical scheme provided by the embodiment of the invention is as follows: the method comprises the following steps of: obtaining position coordinates and a motion vector of more than one encoded / decoded block neighboring a current block; obtaining association parameters of the motion vector and the position coordinates in accordance with the preset congruent relationship between the motion vector and the position coordinates, and the position coordinates and the motion vector of more than one encoded / decoded block; and obtaining a predicted value of the motion vector of the current block in accordance with the preset congruent relationship between the motion vector and the position coordinates, and the position coordinates of the current block and the association parameters of the motion vector and the motion vector. The embodiment disclosed by the invention can be applied to the video image encoding and decoding process, such as H. 264 / AVC and the like.

The invention discloses a dynamic migrating method of a virtual machine based on performance prediction. The method comprises the following steps of obtaining a characteristic vector by using a singular value decomposing principle by extracting system performance data and analyzing the similarity of the singular value to obtain; then, obtaining the performance prediction value at the next moment by a reverse SVD (Singular Value Decomposition) algorithm so as to start a dynamic migrating mechanism of the virtual machine according to future performance prediction values. According to the method, the performance prediction mechanism can make accurate prediction value and the dynamic prediction mechanism can balance the system load. The system can effectively cope with sudden load by effectively analyzing current information of a physical machine and effectively predicting the future load, so that the performance loss caused by unnecessary migration is reduced.

The invention belongs to the technical field of road traffic detection and discloses a GPS data-based prediction method for predicting the traffic jam-resulted delay time of a bus. The method comprises the steps of pre-processing the historical GPS data of a bus; conducting the statistic analysis on the pre-processed historical GPS data of the bus; classifying the traffic jam state of the bus; discriminating the state of the bus; predicting and correcting the traffic jam-resulted delay time of the bus. According to the technical scheme of the invention, the advantage that a large volume of bus GPS data are available is fully utilized. Meanwhile, in combination with the historical statistical information and real-time GPS data, traffic jam states are classified into six conditions through the decision tree classification method and different prediction calculation methods are adopted for different conditions. Moreover, prediction results are corrected according to the real-time data and the historical traffic jam information. Therefore, more accurate prediction values can be obtained in recurrent and accidental traffic congestion conditions.

The invention relates to the technical field of energy storage equipment, in particular to a prediction method and device of the battery charging time, and a battery management system. The predictionmethod of the battery charging time comprises the steps that the current temperature of a battery is detected, and a temperature difference value between the current temperature and a set temperaturethreshold value is calculated; according to the temperature difference value, the temperature compensation time required in the process that the current temperature is changed to the set temperature threshold value is obtained; according to the charging power of the battery and the charging power of a charger, the basic time required by charging within the set temperature threshold value is determined; and the charging time of the battery charging process is determined, wherein the charging time comprises the temperature compensation time and the basic time. According to the prediction methodand device of the battery charging time, and the battery management system, the battery charging time is divided into the temperature compensation time and the basic time required by charging within the set temperature threshold value, and the estimation accuracy of the battery charging time can be improved.

A neural network system for predicting time series data. The system receives analyzed data obtained by multiresolution analysis of the time series data. The input processing layer of the system includes a series of neurons corresponding to the different levels of analysis, each neuron receiving the analyzed data for it own level. The output of each of these neurons is supplied as an additional input to the neuron for the next lower level of analysis. A predicted value is derived from the output of the neuron at the lowest level. The passing of results from one level to another improves prediction accuracy and simplifies the structure of any further processing layers.

The invention discloses a lithium ion battery remaining life prediction method based on fusion of a gating circulation unit neural network and a Kalman filtering model, and relates to the technical field of lithium ion battery health state detection. The objective of the invention is to solve the problems of poor fitting capability and low adaptability to different working states in a nonlinear degradation process of an existing lithium ion battery residual life prediction method based on a fusion model. According to the method, through establishing a GRU-RNN deep network model, the lithium ion battery capacity degradation characteristics are extracted by using the strong characteristic extraction capability of the GRU deep learning model on the time sequence, so that a more accurate battery capacity prediction model is obtained, and finally, noise is reduced and a more accurate prediction value is obtained through a KF filtering method.

The invention discloses a point cloud prediction method based on a space sequence, a point cloud layering method and equipment. The point cloud prediction method comprises the steps: generating two ormore spatial sequences of point clouds; carrying out forward and / or backward search of the current point by using the two or more space sequences to obtain neighbors of the current point; and determining an attribute prediction value of the current point according to the neighbor of the current point. The point cloud layering method comprises the steps: generating spatial sequence codes of all the points by utilizing coordinates of point cloud; and for the points with the same high order of the spatial sequence code, selecting part of the points, and dividing the point cloud into two or morelayers. According to the point cloud prediction method provided by the invention, a flexible neighbor search mode is realized, a more accurate prediction value is provided during point cloud attributeprediction, hierarchical division of the point cloud is realized through the point cloud layering method provided by the invention, and the geometric information and attribute information correlationof the point cloud can be further utilized to improve the compression performance of the point cloud attributes.

The invention discloses an intra-frame forecast encoding and decoding method and a device. The method comprises the following steps that an encoding end selects a reference pixel required for executing intra-frame forecast of a set intra-frame forecast direction according to the set intra-frame forecast direction for an input intra-frame forecast unit; the forecast unit is forecasted with the reference pixel according to the set forecast direction; a pixel forecast value of the forecast unit is obtained; the pixel forecast value is filtered by the selected reference pixel; all forecast directions are traversed; entropy encoding is conducted on the optimal forecast direction; and a decoding end resolves an intra-frame forecast mode and adopts a intra-frame forecast method same as that of the encoding end to obtain an intra-frame forecast value.

The invention provides a prediction data-based transformer early warning evaluation method and device. The method includes the following steps that: a prediction model of gas concentration is established on the basis of acquired training sample data; a fuzzy similarity matrix is established on the basis of prediction data obtained by the prediction model, so that an early warning threshold value can be obtained; and an early warning is reported or not reported according to the early warning threshold value and a national standard limit value. With the prediction data-based transformer early warning evaluation method and device provided by the technical schemes of the invention adopted, the early warning threshold having a plurality of standards is set according to the features of various types of early warning indexes, and with national standards adopted in combination, early warning results can satisfy actual application requirements to the greatest extent.

The invention discloses a method for detecting the flow abnormity of a wireless sensor network based on a GM model. The method employs the GM (1, 1) model, is small in amount of used historical data, is quick in building speed of a model, is accurate in prediction value, and is very suitable for the condition that the node energy and calculation capability of the wireless sensor network are limited. enabling a historical modeling data quantity to be fixed through employing a sliding window in a proper size, thereby guaranteeing the quickness of modeling and also guaranteeing the latest effectiveness of historical data; optimizing albinism differential equation solving initial conditions of the GM (1, 1) model, and enabling the prediction value to be more accurate; generating a flow prediction value, finally used for abnormal judgment, at the next moment through the exponential weighting mean of the former L predication values, thereby introducing certain inertia to the prediction of flow. When an abnormal flow happens, a normal flow prediction model cannot be changed easily, but a normal flow prediction value can be obtained better, and the flow abnormality can be detected more easily.

The invention discloses an industrial process soft measurement method based on layer-by-layer data expansion deep learning, and belongs to the soft measurement technology field. According to the technical scheme disclosed by the invention, the sample size of the process variable is expanded by adopting the data expansion auto-encoder; a plurality of data expansion auto-encoders are stacked to forma deep network model; a large number of samples from a low level to a high level are obtained layer by layer from industrial process data, enough sample size is provided for a deep learning model, accurate prediction of product quality is achieved, the method can be widely applied to product quality prediction of complex industrial processes such as a hydrocracking process and a steel sintering process, and the method has the advantages of being high in prediction precision, good in generalization and the like.

The invention discloses a rapid food and drug detection method based on a near infrared technology. The method comprises the following steps: collecting a modeling detection sample, establishing a near infrared technology analysis model, debugging the model data, performing rapid field detection, drawing a conclusion and preserving the conclusion. According to the method disclosed by the invention, the rapid field detection is performed by utilizing a near infrared spectrum, and the information is simple and rich; nondestructive sampling is adopted, direct sample introduction is realized, the operation is simple, pretreatment of the sample is not needed, the sample is green and environmental-friendly, and the performance is reliable; only the detection amount of 0.1mg is needed, related consumables and maintenance cost are avoided, the operating cost and environmental protection risk are reduced, lots of manpower, material resources and financial resources are saved, actual problems such as long-term hidden grass-roots criminal activities, wide supervision range, large quantity and high difficulty are effectively solved, and the aim of guaranteeing the quality safety of foods and drugs is achieved.

The invention relates to a multi-energy microgrid energy prediction and optimization scheduling method based on a communication network, and is used for obtaining a microgrid optimal scheduling strategy in an economic and environment-friendly mode. The method comprises the following steps that: 1) multi-energy microgrid energy prediction is carried out: a photovoltaic power generation prediction model, a wind power generation prediction model and a load prediction model are established separately, and prediction of the photovoltaic power generation power, the wind power generation power and the load power is performed separately; and 2) in feedback correction of prediction control on photovoltaic power generation, wind power generation and load, an ARIMA ultra-short-term prediction methodis adopted to perform prediction error prediction, so as to obtain more accurate prediction values of photovoltaic power generation power, the wind power generation power and the load power; and by taking the prediction values as input of the prediction control mode, and the prediction control mode is solved to obtain the optimal microgrid optimization dispatching strategy. Compared with the priorart, the method has the advantages of being accurate in prediction, stable in dispatching operation and the like.

The invention discloses a high-speed railway sound barrier insertion loss prediction method of a five-sound-source mode. The method comprises the following steps of equivalently simplifying a high-speed railway noise source into a wheel-rail area noise source, a train body lower part noise source, a train body upper part noise source, a current collection system noise source and a bridge structure noise source according to the composition, position, frequency characteristics and attenuation law of the high speed railway noise source, and respectively calculating the insertion loss of a wing plate for the five sound sources by virtue of a single-sound-source mode; spreading the noise of a wheel-rail area, a train body lower part, a train body upper part, a current collection system and a bridge structure to be superimposed with noise on a sensitive point after a sound barrier is installed, obtaining a main noise grade after the sound barrier is installed, subtracting the main noise grade from the noise grade before the sound barrier is installed, introducing an insertion loss correction item of the bridge wing plate, and obtaining an insertion loss prediction value by adopting the five-sound-source calculation method. By adopting the method, the weaknesses in the traditional sound barrier insertion loss prediction method can be overcome. The high-speed railway sound barrier insertion loss prediction method is applied to the high-speed railway sound barrier engineering design and the ambient noise influence valuation and has the advantages of accurate prediction value and high engineering practicability.

The invention discloses a real-time digital image compression prediction method, facilitating realization of a digital circuit and capable of performing time sequence optimization, based on prediction. Based on the prediction method, according to the image resolution, realization difficulty of the digital circuit and trade-off of compression efficiency, numerical values of a group of unit and prediction interval are selected. Higher is the image resolution, higher is the requirement for a time sequence of the digital circuit, at the moment, and the numerical values of the unit and prediction interval can be appropriately increased; according to the values of the selected unit and prediction interval and the position of the unit where a current pixel is located, corresponding reference pixels are selected for calculating the prediction value of the current pixel. By the adoption of the prediction method, the requirement for the time sequence of the digital circuit in a chip can be lowered, high practical value is achieved, and the method is expected to be applied to integrated circuits needing real-time video stream compression and decompression.

The invention discloses a stress-strain prediction method based on machine learning, and belongs to the technical field of detection and prediction. The prediction method comprises the steps of takingthe machine learning as a medium, processing the stress-strain experiment data as input and output of a learning model, selecting a proper algorithm and the corresponding training parameters, training, so that a prediction network is obtained. During the prediction process, the computer is operated to control the loading force of the loading device each time, a demodulator is used for collectingthe measurement data, a data analysis software is used for processing the experimental data, an appropriate learning model is established, the model is trained, and therefore accurate prediction of the strain field of the tested system is achieved. The method is suitable for the stress-strain field prediction of any optical fiber strain sensor detection system, avoids the errors caused by consideration of determination of loading force and pre-tightening force, simplification of the elastic modulus range of a test piece and a complex model and the like, greatly improves the calibration precision, and is convenient and rapid to operate and easy to popularize.

The invention provides an oil refining process product prediction method based on variable weighting deep-learning and a system. The method includes: acquiring process variables in a debutanizer process, and using the process variables as input of a trained deep-learning model to obtain a product quality prediction value on the basis of the deep-learning model, wherein the deep-learning model includes at least three variable weighting autoencoders, and when the deep-learning model is trained, hidden-layer feature data of a variable weighting autoencoder which is in every two adjacent variableweighting autoencoders and is arranged at a front are used as input variables of a variable weighting autoencoder arranged at a rear, and the variable weighting autoencoder arranged at the rear is trained. The multiple weighting autoencoders are utilized to be stacked to form the deep network model, deep output-related features from a low level to a high level can be gradually obtained, features related to a quality index can be enhanced, the accurate prediction value can be provided for product quality, and the method has the advantages of high prediction precision, good generalization and the like.

The invention discloses a video lossless compression method based on adaptive model selection. The method comprises the following steps: judging the type of an input video, determining operand, performing video pre-processing operation; adopting the airspace redundancy-removing method, the time domain redundancy-removing method and the direct mode redundancy-removing method to perform redundancy-removing operation to the current sample and obtain the predictive value of the current sample; utilizing the redundant information of space, time and frequency domain to separately perform infraframe prediction and interframe prediction to the original YUV sequence; if the original sequence is RGB sequence, performing infraframe prediction, interframe prediction and direct prediction to the converted sequence; using an adaptive prediction model selector to judge the predictive value of the current sample and determine that the minimum is the optimal prediction error; and finally performing context-based arithmetic encoding to the map value of the final prediction error and outputting to obtain bit stream. The invention uses the adaptive prediction model selector to replace the additional bit prediction model, thus obviously increasing the compression efficiency of video; and the method can be used for video compression in the fields of aviation and navigation.

Page-group data indicating many pages is analyzed to acquire the degree of reuse of page components as an analysis result. A set of page data pieces in the page-group data is acquired as a data block, the number of the pieces corresponding to a unit page number that is the number of pages to be processed collectively as a single task when a conversion processor in a system actually converts the page-group data into drawing data. A predetermined conversion processor converts the data block into drawing data to acquire a conversion processing time. A predicted value of a conversion processing time required for the conversion processor in the system to actually convert the page-group data is accurately obtained using the conversion processing time of the data block. An operator is notified of the analysis result and the predicted value and can easily ascertain the appropriateness of the page-group data.

The invention discloses a data mining method and system based on time series. The method comprises following steps: obtaining original event records within a set period of time and determining a basic event type and M related event types according to the original event records; generating a first historic time series and M second historic time series corresponding to the basic event type; calculating correlation coefficients between a low-frequency time series of the first historic time series and a low-frequency time series of each second historic time series; determining a low-frequency time series of a kth second historic time series when the correlation coefficient is larger than a first threshold value and establishing a prediction model according to the low-frequency time series of the kth second historic time series and the low-frequency time series of the first historic time series, and obtaining a predicted value of the basic event type according to the prediction model, wherein M is a positive integer, and K is an integer no more than M. The method and system are used for solving the problem of inaccurate predicted results in the prior art.

A control system for a plant is provided. This control system can control the plant more stably, when the model parameters of the controlled object model which are obtained by modeling the plant, which is a controlled object, are identified and the sliding mode control is performed using the identified model parameters. The model parameter identifier (22) calculates a model parameter vector (θ) by adding an updating vector (d θ) to a reference vector (θ base) of the model parameter. The updating vector (d θ) is corrected by multiplying a past value of at least one element of the updating vector by a predetermined value which is greater than “0” and less than “1”. The model parameter vector (θ) is calculated by adding the corrected updating vector (d θ) to the reference vector (θ base).

The invention discloses a rotary hot spare dispatching method in the construction of an intelligent power grid on the basis of a relevance vector machine. The invention relates to the rotary hot spare dispatching method in the construction process of the intelligent power grid on the basis of the relevance vector machine and aims to solve the problem that a rotary hot spare is difficult to set for a new energy resource power system to stabilize the power fluctuation of a wind power connected grid, wherein the new energy resources comprise scale wind power and the like. An initialization setting result is transferred into a wind power relevance vector machine forecasting system; a wind power station wind-power acquisition module acquires a measurement value of a wind power of a wind power station in real time and transfers data into the wind power relevance vector machine forecasting system after carrying out data preprocessing; the wind power relevance vector machine forecasting system receives the data and carries out forecasting on the wind power at the moment in the future so as to obtain a forecasting result, i.e. an error band of the wind power value at the moment in the future and the wind power; and the obtained forecasting value and error band are sent into a dispatching controller, wherein the forecasting value is a power generation plan of the wind power station in the future and the power range represented by the error band is rotary hot spare distributed to the wind power station. The rotary hot spare dispatching method is used in the construction of the intelligent power grid.

The present invention discloses a folium apocyni veneti total flavonoid near infrared super rapid detection method including chemical value measurement, original spectrum acquisition, spectral data preprocessing, PLS quantitative calibration model establishment, quantitative calibration model stability prediction, folium apocyni veneti total flavonoid mode content model prediction and other steps. Compared with the prior art, waste time and energy, higher operating and maintenance cost and other problems of folium apocyni veneti total flavonoid detection methods in the prior art can be fundamentally solved, a PLS quantitative model is established by use of near infrared spectroscopy for super rapid detection without sample pretreatment, the folium apocyni veneti total flavonoid near infrared super rapid detection method is convenient, fast, free of side effects on the human body and the environment and small in testing result relative deviation, and if a test sample chemical measured value has high precision, a predicted value of the method may be close to a true value.

The invention discloses a multi-temporal dimension data fusion method for the large data of a power distribution network. The method comprises the steps of classifying data according to data sources; setting a statistical cycle and determining a sliding window value; calculating the smoothing factor value and the predictive value of the n cycle; calculating the deviation degree between an actual monitoring value and the predictive value of the n cycle; comparing the deviation degree with a preset deviation degree; when the deviation degree is larger than the preset deviation degree, calculating and obtaining a corrected sliding window value; when the deviation degree is smaller than the preset deviation degree, obtaining actual monitoring values obtained during several cycles from the n cycle and ranking the monitoring values according to the numbers of the cycles to obtain a window data set; calculating the weight coefficient of each data; calculating to obtain reported data; adding the reported data obtained through calculation to a similar reporting data set; and forming a new data set based on similar reporting data sets obtained through calculation. According to the technical scheme of the invention, the sliding window value can be dynamically adjusted. Meanwhile, data within a window can be fused according to weight factors in real time, so that the better data fusion effect is ensured. Moreover, a data basis is provided for upper-layer services.

The invention discloses a predicting method of rolling mill torque in a slab rolling process and belongs to the technical field of medium heavy slab rolling. An equipment built-in rolling force fundamental formula is adopted, according to actual rolling process data, a rolling force and torque formula is regressed to be complied into an easily-operated computational procedure, and rolling reduction predicting under different rolling conditions can be conducted off-line. The predicting method of rolling mill torque in the slab rolling process has the advantages that budgeting of torque values under any rolling condition and any rolling reduction can be conducted, and the budgeting is conducted on the condition that the normal production rhythm is not influenced.

The invention relates to an electric power engineering fund prediction method and device, belongs to the technical field of electric power engineering, and solves the problem of inaccurate prediction result in the prior art. The electric power engineering fund prediction method comprises the steps: comprehensively analyzing a plurality of historical projects to determine influence factors of electric power engineering fund budget, wherein the influence factors comprise project periods, the number of participants, material usage and equipment usage; collecting influence factors of historical projects and corresponding electric power engineering funds; performing normalization processing on the influence factors, and then dividing the normalized influence factors and the corresponding electric power engineering funds into two sets including a training set and a verification set; constructing a neural network model based on Bayesian formula optimization, and training and verifying the neural network model through the training set and the verification set to obtain a prediction model; and inputting the influence factors of the to-be-predicted project into the prediction model to obtain a fund prediction value. The accuracy of prediction results is improved.