221results about How to "Reliable predictions" patented technology

The invention discloses a multi-time scale forecasting method for a road traffic running situation. Highway traffic parameters in different time scales are analyzed according to the running time-space characteristics of highway traffic flow; the highway road traffic running situations in different time scales are forecast by an exponential smoothing algorithm, a weighted average algorithm and a Kalman filtering method respectively; a highway road traffic running situation evaluation index system and a multi-time scale highway traffic flow running situation forecasting technology are constructed to implement the conversion from experience guide to science guide for the highway running management and the preliminary conversion from passive management to active management. Therefore, the running efficiency of a road traffic running situation forecasting system can be increased effectively, the running cost of the system is reduced, the coordination degree between road traffic guidance and management can be improved obviously, and an optimal policy is provided for improving a traffic management and control measure and planning a travel plan for a road traffic manager and a user to a large extent.

The invention relates to the field of rock fracture prediction, in particular to a shale hydraulic fracture propagation prediction method. The method comprises the steps that 1, the normal direction,tangential stress and effect stress of an inclined fracture under the action of the external stress and water pressure are calculated; 2, a strain energy density function is obtained according to thetype of the fracture; 3, a strain energy density factor is obtained according to the strain energy density function; 4, the propagation direction and propagation angle of the fracture are judged according to a strain energy density criterion; 5, influences of stratification, natural fractures and the like on the propagation direction of hydraulic fractures are obtained through numerical simulation, so that fracture propagation under the effect of shale hydraulic pressure is predicted. The prediction method is based on fracture mechanics, a hydraulic condition factor is introduced, and the influences of different stratification directions on the propagation direction of the shale hydraulic fractures are obtained by researching the relation between the fracture propagation direction and themagnitude of the hydraulic pressure and utilizing an extended finite element method, so that accurate prediction of hydraulic fracture propagation is achieved, and higher prediction accuracy is obtained.

The invention belongs to the field of concrete compressive strength prediction, and particularly discloses a method for predicting concrete compressive strength based on a random forest and an intelligent algorithm. The method comprises the following steps: establishing an original sample set of a concrete compressive strength index system, taking a training number set as the input of a random forest regression model to carry out importance evaluation on influence factors forming the concrete compressive strength index system, carrying out feature selection, and selecting an influence factor set with the minimum error as an optimal feature variable set; training the least squares support vector machine model by taking the test data set as the input of the least squares support vector machine model and taking the concrete 28d compressive strength value as the output, and verifying the prediction result of the trained least squares support vector machine model by adopting the test data set; and performing error analysis on a prediction result. The invention improves the precision of the prediction model, enables the prediction result to be more accurate and stable, and can serve as an effective tool for quickly predicting the concrete compressive strength.

The invention discloses a clustering and neural network-based power quality prediction method for a power distribution network containing a distributed power supply. The method comprises the steps oftraining data acquisition and normalization processing; performing clustering division on the historical input data set; dividing a training set and a verification set; enabling BP neural network prediction to perform model training; Solving an optimal training set division mode; obtaining predicted input variable values and normalizing the predicted input variable values; determining cluster affiliation of the input variable value; performing electric energy quality prediction output and reverse normalization; making a power quality prediction result assessment. The method has the advantagesthat 1, the BP neural network is used for effectively predicting the power quality of the DG-containing power distribution network; 2, a k-means algorithm is used to carry out classification preprocessing on the neural network training set by using a means clustering algorithm, and providing different prediction models for each class, so as to overcome the defect that a BP neural network is easy to fall into a local optimal solution, and obviously reduce the prediction error; and 3, the training set, the verification set division mode and the hidden layer node number N are circularly changed for multiple times, and the probability of obtaining the optimal model is improved.

The invention provides a method for predicting the key industrial electricity consumption based on an industrial condition index. The method comprises the following steps: (1) obtaining the key industrial condition index and historical electricity consumption data; (2) performing seasonal adjustment and a stationary test on the data; (3) judging whether the industrial condition index and the industrial electricity consumption have a causal relationship or not through a Granger causality test and determining an optimal lag period of the condition index; (4) creating a time sequence ARIMA (autoregressive integrated moving average) model of the key industrial electricity consumption, introducing the key industrial condition index into an original ARIMA model, and creating a regressive model; (5) on the basis of an AIC (Akaike information criterion), screening out an optimal model; (6) performing model popularization and application, and predicting the industrial electricity consumption in the future. The key industrial electricity consumption is taken as a study object, the electricity consumption and the influence of the industrial condition index on the electricity consumption are studied by introducing the industrial condition index, the key industrial electricity consumption is accurately predicted in combination with the time sequence model, and a basis is provided for development and planning of electricity industry in the future.

The invention discloses a shale gas well yield prediction method, which comprises the following steps that: S1: according to a shale gas well analysis model, constructing the double logarithmic plateof theoretical model data; S2: obtaining the actual measurement data of a shale gas well to be analyzed, and constructing the double logarithmic plate of data according to the actual measurement data;S3: carrying out fitting on the double logarithmic plate of theoretical model data and the double logarithmic plate of the actual measurement data, an determining the yield prediction model parameterof the shale gas well to be analyzed; and S4: according to a preset shale gas well yield prediction model and the yield prediction model parameter of the shale gas well, determining the prediction yield of the shale gas well to be analyzed. The method is suitable for the shale gas well with irregular cracks, parameter input reliability during yield prediction is improved, the accuracy of a yieldprediction result is guaranteed, and the method performs an important function on evaluating the fracturing transformation effect, the production dynamics and the economic benefit of the shale gas well.

The invention provides a landslide displacement prediction method based on wavelet transform-rough set-support vector regression (WT-RS-SVR) combination. By means of the method, according to the characteristics of the influence factors of landslide displacement, the complex displacement process and landslide displacement monitor data measured in real time, accumulative displacement of a typical monitor point is decomposed into trend-term displacement and periodic-term displacement through WT, and a trend-term displacement prediction function is obtained through curve fitting; screening is conducted on the influence factors of the landslide displacement through an RS algorithm, and selected factor sets are used as input factor sets of an SVR machine, accordingly a landslide displacement optimization prediction model based on WT-RS-SVR combination is established, and the precision of a prediction result is analyzed and evaluated. The prediction result of the landslide displacement prediction method can well embody the development and change tendency of the landslide displacement. The landslide displacement prediction method has high prediction capacity, and is accurate, effective and practical.

The invention discloses a semi-supervised semantic segmentation method based on maximum confidence, and the method comprises the steps: selecting a part of images from an existing training data set asmarked images, and taking the remaining images as unmarked images; constructing a network model, and predicting prediction class probability graphs of the marked images and the unmarked images through a segmentation network in the network model; maximizing the confidence coefficient of the marked image prediction class probability graph by adopting supervised learning and adversarial generation modes; predicting a segmentation error region in the unmarked image prediction class probability graph by adopting an unsupervised learning mode; training the network model by combining loss of supervised learning and loss of unsupervised learning; and in a test stage, inputting an unmarked image to be segmented into the trained network model to obtain a segmented semantic image. According to the scheme provided by the embodiment of the invention, semantic segmentation can be accurately carried out on the unmarked image.

The invention discloses a distribution network layering load forecasting method based on topology. A distribution network is layered according to a topology connection relation, forecasting is conducted on the layers respectively, a total forecasting result is obtained by summing in an optimizing mode, summarizing load data of a 10 kV outgoing line switch are read, and contrastive analysis is conducted on the data and the forecasting result. Forecasting accuracy is improved obviously, an intelligent distribution network system is utilized effectively, deep mining based on big data is achieved, the immunity to the data is improved, and lean management of the distribution network is achieved easily.

The invention relates to a geological disaster prediction and forecast technology, and aims to provide a rainfall threshold analysis method causing basin landslide risks. The method comprises the following steps: establishing a three-dimensional grid according to DEM topographic data and soil layer thickness data of a target watershed, and generating boundary conditions and initial conditions required by an RIRM hydrological model; calculating space-time changes of hydrological elements in the drainage basin and drainage basin slope stability reference indexes of any three-dimensional positionin the drainage basin; drawing a relation curve that the safety factor SF, the landslide volume and the landslide area respectively change along with time under the conditions of different rainfall durations and different rainfall intensities; analyzing the relationship between landslide deformation damage and rainfall to obtain rainfall thresholds causing basin landslide risks under different conditions. According to the method, natural basin geomorphic hydrological response characteristics are reserved, and prediction errors can be effectively reduced; drainage basin hydrology and slope stability analysis can be carried out simultaneously, the actual occurrence process of drainage basin landslide disasters is met, and the prediction result is more real and reliable.

To provide efficient and effective modeling of data set, the data set is initially separated into several subsets which can then be processed independently. The subsets themselves are chosen to have some internal commonality, thus providing effective independent tools where possible. This commonality may include correlation between variables or interaction amongst the variables in the subset. Once separated, each subset is independently modeled, creating a subset model having predictive qualities related to the data subset. Next, the subset models themselves are aggregated to generate a overall final model. This final model is predictive of outcomes based upon all data in the data set, thus providing a more robust stable model.

The invention provides a method for comparing stability expansion capabilities of casing treatment schemes. According to the method, control volume analyzing measures are used, single-channel numerical simulation is relied on, the whole characteristic lines of various circumferential groove schemes do not need to be calculated, comparative analysis is only conducted on different circumferential groove treatment cases under the flow near the stall point of a smooth wall, and the stability expansion capabilities of all the schemes can be rapidly and accurately obtained through comparison. The method has good universality, the advantages and the disadvantages of different casing treatment schemes for a compressor can be rapidly and reliably compared, the pertinence of initial stage design can be greatly improved, and time and developing cost are saved.

The invention discloses a natural gas reservoir physical property analysis technology and relates to a method for calculating the irreducible water saturation of the low-permeability tight sandstonewhich comprises the following steps: screening a rock sample of the low-permeability tight sandstone, and acquiring characteristic parameters of a pore structure of the rock sample by adopting a physical experiment method; establishing a three-dimensional pore physical model by adopting a digital core technology according to the pore structure parameters; according to the actual given simulation temperature and pressure of the gas reservoir, determining water-phase and gas-phase high-pressure physical property parameters; and completely saturating the three-dimensional pore physical model withwater, setting a displacement pressure difference, simulating a gas-driven water process, and calculating the water saturation in the model after displacement is stabilized. The irreducible water saturation of the low-permeability tight sandstone gas reservoir under different temperatures and pressures can be effectively predicted, the test and analysis period can be greatly shortened, the defects and shortcomings of an existing experimental analysis method are overcome, a large amount of test and assay expenses are saved, and a foundation is laid for comprehensive evaluation, seepage law research, reserve calculation and the like of the tight sandstone gas reservoir.

The invention discloses a kit and system for prognosis prediction of a metastatic colorectal cancer and relates to the field of biomedicine. The kit comprises a reagent for detecting the expression level of a target gene, wherein the target gene is selected from one or more genes in the group consisting of ACOT11, C12orf45, CHDH, COX17, CTNNB1, CYP2S1, DDTL, DUSP18, FAM221A, FGFR4, KLC4, LARS2, PFDN6, SLC27A3 and TNFRSF11A, the expression levels of the target genes are detected by using the kit, the prognosis condition of the metastatic colorectal cancer can be stably predicted according to the detection result, the prediction result is reliable, the kit has a clinical application prospect, and a new prediction thought or strategy is provided for the prognosis of the metastatic colorectal cancer.

The present invention discloses a traffic accident prediction method based on the PCA and the BP neural network. The method comprises the following steps: constructing a traffic accident prediction model based on the PCA and the BP neural network, introducing a traffic accident data set in the vehicle network into the model, and selecting the feature vector of the traffic accident data set by using the model; performing decorrelation processing on the feature vector by using the PCA to obtain a preset number of linearly independent features in the feature vector; inputting the linearly independent features into the BP neural network for training, and obtaining new independent features for determining whether a traffic accident will occur; and inputting real-time traffic data, and predicting whether the traffic accident will occur according to the new independent features by using the prediction model. According to the traffic accident prediction method based on the PCA and the BP neural network provided by the present invention, accuracy rate for traffic accident prediction is higher, and the occurrence of traffic accidents can be effectively prevented.