54results about How to "Reasonable forecast" patented technology

The invention relates to a prediction device and a prediction method for the state of an emergency topic. The prediction device and the prediction method are provided based on the research of a microblog topic production mechanism and the influence of opinion leaders of emergency on the state of the emergency topic. The prediction device is provided with four components of a text acquisition unit, a topic clustering unit, a topic state recognition unit and a topic state prediction unit, and the topic state is predicted through the participation state of the opinion leaders, so that the prediction method of the topic state is simplified, the prediction accuracy is higher, and a basis can be provided for monitoring the tendency of the emergency topic. Through the calculation method of topic attention provided by the invention, the computation complexity of a traditional topic attention method is reduced. Moreover, the numerical value prediction of the topic attention is replaced by adopting the prediction of the state of the topic, so that the prediction content is more reasonable, and the prediction accuracy is higher. Furthermore, a prediction method of a hidden Markov model is provided, the set of the opinion leaders and the model of topic prediction are constructed through incremental iteration, so that the prediction operation can be obviously simplified, and the prediction efficiency is improved.

The invention discloses an accelerated degradation test prediction method based on fuzzy theory, which comprises the following steps: collecting test data; performing the analysis of regression aimingat performance degradation data under each stress level; extrapolating the performance degradation rate of the product under a normal stress level; estimating a diffusion coefficient sigma in an excursion Brownian motion with drift by adopting a maximum likelihood estimation method; establishing an accelerated degradation test life and reliability predication model based on the fussy theory; andpredicting the life and the reliability of the product by adopting the fussy life and reliability prediction model. The method firstly introduces the fussy concept into an accelerated degradation testto enable the prediction result of the accelerated degradation test to be more reasonable, avoids the condition of rash routine reliability estimation result through considering the fussiness fuzziness of the performance degradation threshold, solves the problem of failed performance degradation in the engineering reality, and is suitable for the accelerated degradation tests of step stress and progressive stress and unaccelerated performance degradation prediction for the problem of the performance degradation failure.

The invention discloses a medium and long term hydrologic forecasting method based on empirical mode decomposition. The medium and long term hydrologic forecasting method based on empirical mode decomposition comprises the steps that firstly, a hydrologic forecasting model is built according to the following procedures of (101) empirical mode decomposition, wherein empirical mode decomposition is carried out on a hydrologic time sequence s(t) of a forecast drainage basin, (102) kernel principle component analysis, wherein kernel principle component analysis is carried out on n intrinsic mode function components Fj obtained through empirical mode decomposition and a trend item rn, and p main components F'k are extracted, (103) building of a training sample set, wherein the training sample set is built according to the extracted p main components F'k, (104) building of a support vector machine model, and (105) training of the support vector machine model; secondly, annual runoff data of the years needing forecasting are forecast through the built hydrologic forecasting model. The medium and long term hydrologic forecasting method based on empirical mode decomposition is simple in step, convenient to realize, easy and convenient to operate, good in use effect and capable of effectively resolving the problem of low forecasting accuracy of an existing hydrologic forecasting method.

The invention belongs the virtual simulation technical field, in particular to a macroscopic, mid-scope and microscopic multilevel urban parking demand prediction model integrated system, which comprises a system data module, an urban overall parking demand macroscopic prediction module, a comprehensive development sharing parking demand mid-scope prediction module and a parking garage entrance service level microscopic analysis module, wherein the system data module is used for importing and storing parking demand prediction data; the urban overall parking demand macroscopic prediction module is used for predicting the overall parking demand; the comprehensive development sharing parking demand mid-scope prediction module is used for predicting the largest parking demand of each block and is connected with the urban overall parking demand macroscopic prediction module; the parking garage entrance service level microscopic analysis module is used for predicting a parking garage entrance service situation; and the three prediction modules are respectively connected with the system data module. The macroscopic prediction module not only considers the parking demand of traveling vehicles, but also considers the parking demand of the vehicles which are not on trip, the mid-scope prediction module considers the shared parking demand of multi-nature buildings under the reachability situation of buses, bi-directional correction of the prediction results of the macroscopic, mid-scope and the microscopic modules can be realized, the system quantitatively predict the parking demand of different cities of different levels so as to provide important support for different parking researches.

The invention discloses a solar power generation capacity prediction method for a photovoltaic power station. The method includes the steps of firstly, calculating the annual average peak sunshine hours of recent years of the region where the photovoltaic power station is located and the installed capacity of the photovoltaic power station; secondly, calculating the solar power generation capacity Wn of the nth year of the photovoltaic power station and the total solar power generation capacity Wtotal of set years of the photovoltaic power station according to the annual average peak sunshine hours, the installed capacity and photovoltaic module attenuation rates. The method has the advantages that the method is applicable to various regions and meteorological conditions, the generated daily average irradiance of each month, annular average irradiance, installed capacity, annual solar power generation capacity in N years and total solar power generation capacity are representative, solar irradiation data accuracy of regions away from a local solar irradiation observation station or regions without a solar irradiation observation station is increased, and prediction and evaluation results are objective and reasonable.

The invention discloses a method for identifying a non-bright spot oil reservoir in a third AVO bright spot characteristic reservoir stratum zone, and belongs to the technical field of oil exploration and development. In the method, seismic data and well data are comprehensive utilized, wave impedance models of different reservoir stratum numbers are established for forward simulation, the corresponding relationship between the reservoir stratum wave impedance model and the non-bright spot oil reservoir is determined, a forward data body of the non-bright spot oil reservoir is generated on the basis of the corresponding relationship, inversion parameters which are sensitive to the non-bright spot reservoir stratum are preferably selected out by means of a constraint sparse spike inversion algorithm, wave impedance inversion is applied to actual seismic data, and a reasonable seismic reservoir stratum prediction result is finally determined. According to the invention, forward and inversion combined prediction of the reservoir stratum is carried out, the prediction precision is improved, the exploration risk is reduced, and the method for identifying the non-bright spot oil reservoir is an effective means for predicting a lithologic oil reservoir stratum.

The invention discloses a method for predicting the remaining service life of a power transformer. The method comprises the following steps: a, building a multivariate Weibull distribution model; b, solving the partial derivative of F(.) to obtain a joint probability density function f(.); c, obtaining a log-likelihood function of the f(.); d, obtaining experimental data of degenerative characteristic parameters of an insulating oil paper of the transformer at different times; e, calculating the minimum value of the log-likelihood function to obtain estimated values of the parameters; f, fitting the model parameters to obtain functions of time t, of the parameters; g, obtaining a reliability function expression; and h, predicting the remaining service life of the transformer according to the reliability function. The method disclosed by the invention has the advantages that the remaining service life of the power transformer is predicted according to the degradative characteristic parameters of the insulating material; the probability density function reflecting the random uncertain characteristics of the remaining service life can be conveniently obtained; the prediction results are accurate and reasonable; and reliable information can be provided for the state maintenance or the predictive maintenance of the transformer.

The invention discloses a broad earthquake wave impedance low-frequency information prediction method, comprising the steps of utilizing broadband overlapped earthquake information and earthquake wavelet to establish complex frequency domain earthquake impedance calculation; utilizing Bayesian inversion theory to calculate initial target function of earthquake wave impedance according to positive calculation of complex frequency domain earthquake impedance; adding initial model constraint to initial target function of earthquake wave impedance to get low-frequency prediction target function and work out the low-frequency prediction target function to get earthquake wave impedance low-frequency information. The method is only utilizes broadband earthquake materials to realize low-frequency prediction of earthquake wave impedance, that is to say, shaft information constraint is not required to measure, and low-frequency information prediction result can still be obtained if complicated geological conditions such as lenticle and strong change of physical property exist. The invention also discloses a broad earthquake wave impedance low-frequency information prediction system, having the above beneficial effect.

The invention provides a method for manufacturing a seismic slice with an overthrust fault, which comprises the following steps of: carrying out explanation on a geologic horizon (with an overthrust fault) and a fault; connecting the geologic horizon with the fault so as to form a new horizon according to the explanation results of the geologic horizon and the fault; carrying out up-and-down horizon connection on the obtained new horizon at the position of the fault so as to form a closed fault block; and according to the formed fault block and the connected horizon, executing an operation ofslicing.

The invention relates to a method for building an LS-SVM prediction model based on the chaotic search. The method includes the following steps: (A) building a sample training data set; (B) calculating coefficients of the model; (C) conducting optimization with the chaotic search improvement algorithm, and obtaining the minimum value and the optimal chaotic variable of a to-be-optimized function; (D) determining the optimized LS-SVM prediction model; (E) updating a sample. By means of the method, the LS-SVM self-adaptation resource prediction model is built after parameters of the model are optimized with the chaotic search improvement algorithm, the operating state of prediction objects in cloud calculation can be dynamically predicted, prediction results have good adaptability, and it can be guaranteed that the prediction resultsmoreapproximate to true values of the prediction objects. The sensibility of the chaotic search to the initial value is remitted through the model; in addition, in the chaotic iterative search process, the second-time search can be rapidly carried out in the optimal solution neighborhood through the adjustment on the chaotic variable, the search efficiency is improved, and the possibility of being caught into the local optimum is decreased.

The invention discloses a fast calculation method for engineering sea area sea-level rise. The method comprises the technical steps that multiple kinds of sea surface height data of an engineering sea area are disposed, and a sea-level change data sequence is established; smoothing processing is conducted on the sea-level change data sequence of the engineering sea area; the rise rate of the sea-level is piecewise calculated by using the smoothed data sequence; correlation analysis is conducted on an average sea-level rise rate data sequence and a corresponding temperature data sequence; a future sea-level rise rate calculation model is established by using a correlation analysis result; the future sea-level rise rate is calculated according to time demands for engineering sea area sea-level prediction; a quantity value of the sea-level rise range and an error range thereof are calculated. By means of the method, a predicted value of the seal-level rise range and a predicted error are calculated, the calculated amount is smaller, the sea-level rise trend and the uncertainty of climate prediction under the background of climate change in the future can be objectively reflected, and the requirement for accurately and fast predicting future seal-level rise can be met.

The invention discloses a well-to-seismic collaborative sedimentary microfacies division method based on a high-precision sequence framework model, and the method comprises the steps: building a matching relation of well-to-seismic sedimentary facies features based on the lithologic combination of a stratum under a high-precision sequence stratum shelf and the seismic response features of sedimentary body cycle features, and optimizing the seismic attributes sensitive to the types of stratum sedimentary facies; and establishing a three-dimensional data volume for researching sedimentary subfacies and microfacies of a target layer through well-to-seismic collaborative simulation. And verifying the reliability and accuracy of the sedimentary facies three-dimensional data volume through a phase sequence rule, and finally obtaining a sedimentary facies division result suitable for industrial standards. According to the three-dimensional data volume of the sedimentary microfacies, the working efficiency of sedimentary facies division can be greatly improved, the transverse resolution and the longitudinal resolution are high, and the determinacy and the reliability of sedimentary microfacies fine division results are higher.

The invention discloses a house price prediction method, device and equipment, and a storage medium. The method comprises the steps of obtaining historical house price data and region information corresponding to the historical house price data; dividing the historical house price data according to the region information to obtain initial house price data sequences of different regions; calculating the initial house price data sequences of different regions according to a preset gray scale prediction model to obtain initial house price prediction values corresponding to the regions; calculating a spatial response value corresponding to each region according to the initial house price data sequences of different regions; and correcting the initial house price prediction value correspondingto each region according to the spatial response value corresponding to each region to obtain a target house price prediction value of each region. On the basis of a traditional time sequence, space factors are added, and influence factors among all regions are considered, so that a prediction result is more accurate and reasonable.

The invention discloses an electric vehicle charging scheduling method considering the demand matching degree, relates to the technical field of electric power system control, and solves the problems of under response, over response, low solving convergence speed and the like in the electric vehicle charging scheduling process, and the method comprises the following steps: establishing an electric vehicle charging load prediction model of a trip chain; inputting the sorted information into a model for predicting the charging load of the electric vehicle to obtain a prediction result; establishing an electric vehicle scheduling model of the demand matching degree; inputting the prediction result in the step 1 into the scheduling model in the step 2, and solving through a fuzzy cuckoo algorithm to obtain a scheduling scheme; according to the method, the improved trip chain is used for predicting the load of the electric vehicle, the charging load of the electric vehicle in different time and space is predicted, various factors such as the number of the electric vehicle and the battery capacity can be considered, and the prediction result is more real and reasonable.