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 provides an intelligent banknote distribution method and an intelligent banknote distribution system. The method comprises the steps of obtaining the historical transaction data of a banknote distribution website; training the neural network model according to the historical transaction data to obtain a trained banknote distribution prediction model; evaluating the trained banknote distribution prediction model, and selecting a target banknote distribution prediction model; and predicting the cash demand of the cash distribution network according to the target cash distribution prediction model. By utilizing the method and the system, the banknote distribution amount can be predicted. The prediction result is reasonable and accurate, so that the website can meet the cash demand during withdrawal. The banknote reserve pressure is reduced to the minimum. According to the method and the system, the banknote distribution prediction model is evaluated based on the true value and the prediction value. The optimal model is selected for banknote distribution prediction. The income and risks caused by excessive and insufficient banknote distribution are comprehensively measured. The banknote distribution scheme is more reasonable, and the prediction precision of the banknote distribution prediction model is effectively improved.

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 system and method for evaluating advantages and disadvantages of a traffic organization scheme during road-related project construction. The system comprises a traffic volumeforecast model building module, a road traffic volume forecast module, a simulation module and an evaluation module, wherein the traffic volume forecast model building module is used for building a forecast model for traffic volume during the road-related project construction; the traffic volume forecast module is used for receiving the values of various parameters input into the traffic volume forecast model and calculating to obtain the traffic volume during the road-related project construction; the simulation module is used for simulating a forecast result; the evaluation module is used for quantitatively evaluating the advantages and disadvantages of the traffic organization scheme during the road-related project construction.

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 science and technology service industry development level prediction method based on multiple linear regression. The method comprises the following steps that: collecting andcarrying out statistics on science and technology service industry statistical indexes in an existing document, utilizing a frequency analysis method to select a required high-frequency index as thescience and technology service industry statistical index of the method, and establishing a corresponding index system; selecting an index to be predicted from indexes which reflect a science and technology service industry output level as a dependent variable, taking rest indexes as independent variables, and constructing a science and technology service industry index sample matrix; calculatingthe relevant coefficient matrix of the science and technology service industry index sample matrix; on the basis of a step-by-step multiple linear regression algorithm, adopting a proper criterion toselect independent variables as few as possible, and establishing a science and technology service industry development level prediction model; and predicting the selected dependent variable. By use of the method, the independent variable index which affects science and technology service industry development can be dynamically screened, so that the selected index can more accurately predict the dependent variable development, and the development of the science and technology service industry is accelerated.

The invention discloses a saturated power demand prediction method considering urbanization development and an electric energy substitution effect. The method comprises the following steps: data selection and preprocessing, an urbanization development process, electric energy substitution, re-electrification influence analysis, a saturated electric power demand measurement economics equation, a causal relationship graph, a stack flow graph, a saturated electric power demand scene generation model considering emerging electric power demand influence factors, economics multi-scene setting, electric power demand saturation criteria and a saturated electric power demand prediction model. According to the invention, factors influencing the change of the power demand are provided; interpretationof an urbanization development process, an energy substitution effect and a reelectrification theory and policy is completed and parameterization is realized; the vacancy value is fitted by means oftwo growth models, namely a logistic model and a Gabor model, and the model with the best fitting effect is used for filling vacancy data.

The invention relates to an order meal delivery time prediction method and device, electronic equipment and a storage medium, and aims to solve the problem of inaccurate prediction result of order meal delivery time in related technologies. The order meal delivery time prediction method comprises the steps of obtaining actual arrival time and actual meal taking time of a deliveryman of a to-be-delivered order; inputting the actual arrival time and the actual meal taking time into a meal delivery time prediction model to obtain predicted meal delivery time of the to-be-delivered order; whereinthe loss functions used for training the meal delivery time prediction model are different for the sample orders with different relationships among the predicted meal delivery time, the actual arrivaltime of the deliveryman and the actual meal taking time.

The invention discloses a non-signalized intersection lateral traffic volume prediction device and method. The prediction device comprises an intersection traffic volume information collecting module which communicates with a lateral traffic volume computation module, and the lateral traffic volume computation module communicates with a prediction information release module; the intersection traffic volume information collecting module transmits quantity information to the lateral traffic volume computation module, the lateral traffic volume computation module predicts the quantity of the traffic volume obtained when traffic participants pass the intersection through a Poisson distribution formula according to the traffic volume information collected by the intersection traffic volume information collecting module, and the predicted traffic volume information is released in a prediction information release module. The prediction device and method predict lateral motor vehicles, non-motor vehicles and pedestrians, are especially suitable for controlling intersections in a non-signalized mode and are used for predicting the intersection lateral traffic volume information, safety is increased, and the traffic capacity is improved.

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 is suitable for the technical field of computer aided article design, and provides an article molecule generation method, device and equipment and a storage medium. The method comprisesthe following steps: inputting a first molecular diagram structure and a first connection tree structure of a source molecule into a molecule generation model, and encoding the first molecular diagramstructure and the first connection tree structure through the molecule generation model to obtain embedded representation of the source molecule; decoding based on the structure information of each substructure in the label set and the embedded representation to generate a second connection tree structure of the target molecule; and decoding the second connection tree structure to obtain a secondmolecular diagram structure of the target molecule. The structure information of each substructure in the tag set and the embedded representation of the source molecule are combined for decoding, sothat the structure information of each substructure in the tag set can be well utilized to predict the connection tree structure of the target molecule, the reasonability of the prediction result is improved, and the influence of tag imbalance is relieved.

The invention discloses a forecasting method of household electricity consumption based on household electrical appliance energy consumption accounting, which includes: firstly selecting a sample period of residential electricity consumption, and then classifying the residential electricity consumption categories. And then extracting the residential electricity consumption amount according to theresidential electricity consumption category divided in the step S2; And then calculating a predicted value of the terminal power consumption equipment quantity; predicting the energy consumption of the power consuming equipment of the unit terminal; Finally, according to the predicted value of the terminal power consumption equipment quantity obtained in the preceding step, the predicted value ofthe unit terminal power consumption equipment energy consumption and the predicted value of the number of residential households, calculating the residential power consumption of the target area in the prediction period. The invention decomposes the residential power consumption in the sample period into the power consumption of the terminal equipment, On this basis, the method analyzes the influencing factors of terminal equipment power consumption in the sample period, and constructs a quantitative analysis model to predict the growth trend of various terminal equipment power consumption, and further summarizes and realizes the prediction of residential power consumption, which can explore the deeper power source of residential power consumption growth, and the prediction results are accurate.

The invention discloses an express business volume prediction method, an express business volume prediction system and an express employee scheduling method. The express express business volume volumeprediction method comprises the following steps: counting the daily average express volume of each unit area in an express website in a specified time period; performing summarizing to obtain the total express quantity of the express website, and determining the spreading proportion of the daily average express quantity of each unit area; and predicting the everyday express quantity of the express business of each unit area according to the total predicted express quantity of the website express and the distribution proportion. According to the embodiment of the invention, the distribution proportion of the daily average express quantity of each unit area is determined, so that the daily express quantity of the express business of each unit area is predicted according to the distributionproportion, the prediction of the daily express quantity of each unit area has objective data support and reasonable expectation, and the prediction result of the express business quantity is more accurate and reasonable.

The invention discloses a mine water inrush prediction system based on information fusion, which includes an information collection device and a computer with a GIS (Geographic Information System) and an information fusion processing system for processing the collected signals. The information collection device can scan through graphics , data transmission and other means to extract information related to water inrush; it can also obtain various parameters from mine hydrological monitoring in real time, transfer the information of discrete points in the prediction area to the computer, optimize sample information, and use information fusion Methods A water inrush prediction model was constructed to predict the water inrush and determine the safety status of the prediction area to make the prediction results more accurate, reasonable and practical.

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 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 a tea garden yield prediction method based on multi-modal information, and the method comprises the steps: 1), obtaining or generating a tea garden data set, each sample containing a tea garden image, tea garden environment data and yield; 2) training a tea garden yield prediction model by using the data set; wherein the tea garden yield prediction model comprises a tea garden image feature learning module, an environment feature learning module, a feature fusion module and a full connection layer; the tea garden image feature learning module is used for acquiring image features of the tea garden from the tea garden image; the environment feature learning module is used for acquiring environment features F omega of the tea garden from the tea garden environment data; the feature fusion module fuses the image features and the environment features F [omega] to obtain a feature FC, and inputs the feature FC into a full-connection layer to predict the tea yield of the tea garden; and 3) for a tea garden to be predicted, inputting image data and environment data of the tea garden to the trained tea garden yield prediction model to obtain the tea yield of the tea garden to be predicted.

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 provides a quantitative transaction factor prediction method and device, and the method comprises the steps: inputting obtained to-be-tested data into a pre-trained target GAN (Generative Adversarial Network) model, and obtaining a plurality of groups of simulation to-be-tested data outputted by the target GAN model; selecting three groups of to-be-tested data with low correlation from the plurality of groups of simulated to-be-tested data; and inputting the three groups of to-be-tested data into a pre-trained target TCN model to obtain a prediction result of the quantitative transaction factor corresponding to the to-be-tested data output by the target TCN model, so that the problems that the prediction of the quantitative transaction factor does not support parallelism and the prediction result is not accurate enough in the related technology can be solved, simulation data is generated by applying a GAN network, and the prediction accuracy of the quantitative transaction factor is improved. The sample size is increased, the diversity of samples is improved, and prediction is more reasonable; and parallel prediction of multiple variables and multiple time steps can be realized by using the TCN model, and the prediction accuracy is improved.

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.

A method for predicting plant lncRNA-miRNA interaction, comprising: obtaining an original data set, processing it to obtain a set of lncRNA sequence features, a set of interaction profiles of lncRNA, a set of miRNA sequence features, a set of interaction profiles of miRNA, and calculating The similarities S_lncS, S_lncP, S_miS, and S_miP corresponding to the lncRNA sequence feature set, the lncRNA interaction profile set, the miRNA sequence feature set, and the miRNA interaction profile set respectively, the sequence feature similarity of lncRNA S_lncS, and the interaction profile of lncRNA The similarity S_lncP is combined to obtain the combined similarity of lncRNA, and the sequence feature similarity S_miS of miRNA and the interaction spectrum similarity S_miP of miRNA are combined to obtain the combined similarity of miRNA; from the first interaction matrix 1 / 5 of the elements are randomly selected and set to zero to obtain the first training matrix.