21 results about "Density curve" patented technology

The invention provides a data reading method of a memory, storage equipment and a storage medium, and the data reading method of the memory comprises the following steps: carrying out reading operation on a programming unit to obtain target data; according to the bit flipping information of the target data, determining a corresponding failure bit counting exception type; wherein the failure bit counting exception type comprises a first type of exception caused by read voltage offset and a second type of exception caused by trough elevation of a probability density curve of a break-over voltage of a storage unit; in response to the first type of abnormality, adjusting the read voltage; or in response to the second type of exception, performing data refreshing operation or garbage collection operation. By means of the mode, the abnormal type causing reading voltage deviation is judged so as to judge the data state, then the rereading rate of a host is reduced, and the reading effectiveness is improved.

The present application relates to the technical field of rock fracture modeling, and discloses a method and a processor for modeling a multi-scale fracture network of a tight reservoir. The method comprises: obtaining a strike attribute of a fracture; generating a fracture strike conforming to a preset mode according to a cumulative probability density curve of the strike; taking a fracture position interpreted from a core and an imaging logging as hard data of a fracture position in a fracture network; dividing the hard data into seed hard data and correction hard data; correcting a cumulative probability curve of fracture development intensity according to the seed hard data, and generating a fracture element set according to the corrected probability density curve; generating a discrete fracture network through similarity fusion criteria, mechanical cause fusion criteria and intersection criteria; comparing the established discrete fracture network with the correction hard data, and correcting the number of fracture elements and the cumulative probability curve. Through iterative inversion of the seed hard data and the correction hard data, a more optimal multi-scale fracture network model that is more consistent with the hard data is generated.

The application provides a curve reconstruction method and device based on deep learning, equipment and medium, the method comprises the following steps: obtaining the logging curve data of the non-expanded section, and establishing a training set and a test set; a density curve reconstruction model is established and the training set is used to train the density curve reconstruction model, the density curve reconstruction model is a deep learning model; the test set is used to test the density curve reconstruction model, and a reconstructed density curve is generated, the density curve reconstruction model is adjusted until the correlation of the reconstructed density curve and the original density curve of the non-expanded section and the relative error of the density curve reconstruction model reach a preset condition; the density curve of the target well is reconstructed by using the adjusted density curve reconstruction model. The application simplifies the operation process, especially in complex formation conditions, can accurately reflect the actual situation of the formation, and improves the calculation accuracy of the reservoir parameters.

The invention discloses a short-term power load probability prediction method based on an improved neural network, and belongs to the technical field of power system load prediction. The method comprises the following steps: acquiring and preprocessing load and related data; the method comprises the following steps: constructing a DCS-CNN-BiLSTM-Attention-QR probability prediction model fusing a convolutional neural network, a bidirectional long short-term memory network, an attention mechanism, quantile regression and kernel density estimation; automatically optimizing key hyper-parameters of the model by utilizing a difference creation search algorithm; carrying out model training by adopting a quantile loss function to obtain load prediction values under different quantiles; and finally, generating a probability density curve of the prediction interval through kernel density estimation. According to the method, deterministic point prediction is expanded into probabilistic interval prediction, the uncertainty of load prediction can be quantified, the model precision and generalization ability are improved through an intelligent optimization algorithm, and richer and more reliable decision information is provided for power system scheduling and risk management.

The invention relates to a data analysis method and device for a wind turbine generator, electronic equipment, a storage medium and a computer program product. The data analysis method comprises the steps of obtaining historical data of target parameters of the wind turbine generator; calculating a target probability density curve of the target parameter based on the historical data of the target parameter; and evaluating the operation performance of the wind turbine generator based on the target probability density curve. Therefore, the evaluation of the operation performance of the wind turbine generator does not depend on any input information, but calculates the probability density curve corresponding to the parameters of the wind turbine generator based on the real operation data of the wind turbine generator in the historical operation process, and then evaluates the operation performance of the wind turbine generator based on the calculated probability density curve. According to the method, the actual operation rule of the wind turbine generator is fully combined, namely the design principle and the control characteristics of the wind turbine generator are fully utilized, the control mode of the wind turbine generator can be truly restored, and the accuracy of evaluating the operation performance of the wind turbine generator is improved.

The invention discloses a wind turbine generator bearing fault detection method and system. The method comprises the steps of collecting power, wind speed, generator bearing temperature and generator rotating speed of a wind turbine generator in different set days; performing data box separation and quartile distance anomaly detection based on power and wind speed; dividing the operation data matrix with abnormal values removed by day, and fitting the wind speed and power in each sub-matrix into a sigmoid curve as a wind speed and power curve of each day; eliminating data of days in a non-full power generation state based on a wind speed power curve of each day; dividing the operation data matrix in the normal state into a plurality of samples, fitting the generator rotating speed and the generator bearing temperature in each sample into a set nonlinear function, constructing a probability density curve of a degradation value of each sample, obtaining the bearing degradation probability, and if the bearing degradation probability exceeds a set degradation probability threshold, determining that the bearing is not damaged; if yes, determining that a fault exists. According to the invention, the overall operation and maintenance cost is saved, and the power generation efficiency is improved.

The invention relates to the technical field of electricity market analysis and prediction, in particular to a probabilistic prediction method for the clearing price of an electricity market. The technical problems are that when an electricity market clearing price probabilistic prediction method in the prior art is practically applied, multi-peak distribution caused by market state structural change is difficult to accurately capture, a predicted price probability density curve is often too smooth, and a prediction result has systematic deviation; according to the technical scheme, the probabilistic prediction method for the clearing price of the electricity market comprises a market state mode division step, a mode probability prediction step, a condition price distribution prediction step and a probability distribution synthesis step. According to the method, recognition and division of the market state mode are introduced, and a complex probability prediction problem is decomposed into a plurality of simple sub-problems, so that the multi-peak characteristic of the clearing price probability distribution is more accurately captured, and the prediction precision is improved.

The application discloses a temperature-pressure decoupling control method based on an isodensity curve and related equipment, and the method comprises the following steps: after determining a target cabin for temperature-pressure control, obtaining an initial state and a target state of the target cabin; judging whether the initial state and the target state are located in the same isodensity curve range based on an isodensity temperature-pressure decoupling control graph of the target cabin; if not, calculating at least two intermediate target states by using the change relationship between the temperature and the pressure under the quantitative premise based on the target temperature and the target pressure in the target state; and performing temperature and pressure control on the liquid in the target cabin based on the at least two intermediate target states, so as to adjust the liquid from the initial state to the target state. By using the isodensity temperature-pressure decoupling control graph of the liquid, the temperature and the pressure of the liquid in the target cabin are adjusted by using the mode of separately controlling the temperature and the pressure, so as to solve the problem that it is difficult to simultaneously and accurately control the temperature and the pressure in the existing high-temperature and high-pressure environment.

The invention relates to a multi-temporal-spatial-scale air conditioner load demand response potential quantitative evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: classifying each building and an air conditioning system according to the building data of each building in a target area and a preset classification standard, and building a reference simulation model; a plurality of air conditioner load models are determined, and baseline working conditions before demand response of all the air conditioner load models are determined; carrying out analogue simulation on the air conditioner load models corresponding to various building air conditioner loads, and obtaining a demand response potential probability density curve by utilizing each simulation result and the corresponding baseline working condition; obtaining a total demand response potential probability density curve by using the demand response potential probability density curves of the various building air conditioner loads; according to the total demand response potential probability density curve, the demand response potential of the target area under different confidence degrees is determined, quantitative evaluation of the air conditioner load adjusting potential under multiple spatial and temporal scales can be achieved, and the method has the advantages of being high in precision and generalization.

The invention relates to the technical field of private domain reaching, and discloses a middle-aged and elderly private domain user reaching method and device based on dynamic preference prediction.The method comprises the steps that original behavior data and external environment data of a target user are obtained and preprocessed, and a structured time sequence data table of the target user is obtained; inputting the structured time sequence data table into a dynamic preference prediction model for prediction to obtain a touch response probability density curve of the target user in a future preset time period; obtaining to-be-reached content metadata, and generating a reaching task list of the target user in a future preset time period in combination with the reaching response probability density curve; and extracting the reaching content and the reaching time in the reaching task list, and pushing the reaching content to the target user according to the reaching time. According to the method, the response probability curve is generated based on the behavior and environment data, the user disgusting and shielding rate is reduced, the high response probability window is matched in combination with the content metadata, and the time and the content are synthesized, so that the pushed content is accurately reached in the user active period.

The application relates to a hybrid life prediction method of a complex electromechanical device, which comprises the following steps: preprocessing full-life data collected by the complex electromechanical device to obtain a labeled training sample set; obtaining a set of training sample subsets in a segmented mode; setting the horizontal and vertical coordinates of the best fitting curve as the 'feature value-weight value' pairs of the training sample subsets; obtaining an improved VPM model by summarizing all the models; obtaining a set of test sample subsets, and predicting each test sample subset according to the improved VPM model to obtain a preliminary prediction result of life evaluation; and obtaining a final life prediction value of the complex electromechanical device. im The application takes the independent variable probability as the weight w of the mean and variance, and reduces the influence of abnormal samples on the classification accuracy; the improved VPM model can be used for calculating the similarity of the probability density curves of a section of data of the measured continuous time sequence, and greatly reduces the influence of abnormal samples.

This invention relates to a method and apparatus for predicting the depth of the overpressure top surface before drilling in exploration wells. The method includes: collecting formation pressure test data from drilled wells in the study area, including multiple drilled formation pressure test points; obtaining the density curves of the exploration wells; and calculating the overlying strata pressure P of the exploration wells based on the density curves. O and the hydrostatic pressure P of the exploration well 静水 According to the overlying strata pressure P of the exploration well O The hydrostatic pressure P of the exploration well 静水 Based on the aforementioned multiple drilled formation pressure test points, the pre-drilling overpressure top surface depth H of the exploration well was determined. TOP This invention relates to a method for predicting the depth of the overpressure top surface before drilling in exploration wells. It utilizes the trend of density variation with burial depth and analysis of formation pressure test data from drilled wells to avoid the uncertainty caused by relying solely on formation velocity to predict the overpressure top surface, thereby improving the accuracy of overpressure top surface prediction before drilling in exploration wells.

The invention provides a post-earthquake structure evaluation and repair method and device based on two-stage time-history analysis, and relates to the technical field of structure damage evaluation. The method comprises the following steps: performing two-stage elastic-plastic time-history analysis on a multi-layer structure through a fiber beam model to obtain structure response data; building a deep learning model based on MLP and LSTM, and predicting structure response data of a second stage; performing weighted summation dimension reduction processing on the predicted structure response data of the second stage through an evaluation index dimension reduction method to obtain a one-dimensional damage evaluation index after the earthquake of the structure, and calculating 95% quantile of a probability density curve; constructing a one-dimensional damage evaluation index limit value of the structural mechanical response state as a damage grade division basis; according to the post-earthquake structure safety assessment method and the post-earthquake structure safety assessment system, rapid assessment of post-earthquake structure safety can be achieved, and a basis is provided for post-earthquake repair decision making.

The invention discloses a Bayesian update-based industrial equipment residual life prediction method and system, and the method comprises the steps: building a prior model of an equipment degradation process through historical failure data, and obtaining an initial parameter through maximum likelihood estimation; collecting state monitoring data of the equipment, and performing feature extraction and normalization processing to form a degradation amount observation value; carrying out recursive updating on posterior distribution of degradation model parameters by utilizing a particle filtering algorithm and a Bayesian theorem; through a Markov chain Monte Carlo sampling technology, outputting a point estimation curve, a confidence interval curve and a failure probability density curve of the residual life; an equipment maintenance plan is dynamically adjusted according to the prediction result, and early warning is triggered when the failure probability exceeds a preset threshold value, so that decision support is provided for predictive maintenance; the system correspondingly comprises a data acquisition and preprocessing module, a prior model construction module and the like. According to the method, through fusion of probability classification and statistical reasoning, prediction accuracy is improved, and a reliable decision basis is provided for predictive maintenance of equipment.

The application relates to the technical field of emergency diesel generator monitoring, and discloses an emergency diesel generator fault monitoring method, device, equipment and product, which comprises the following steps: obtaining operation data, health state parameters and operation data of an emergency diesel generator at different time points in a historical period; adopting a reinforced mean clustering algorithm, combining with working condition auxiliary data, performing clustering on feature data to obtain a plurality of clustering clusters, and determining the working conditions corresponding to the clustering clusters; respectively determining initial alarm threshold values of various features according to probability density curves; correcting the alarm threshold values according to the health state parameters; and determining a fault state according to current operation data and the alarm threshold values; in the application, the data obtained through detection points are used for monitoring mechanical faults, the alarm threshold values are determined in combination with operation data obtained through the detection points, the fault state at the current time is judged based on the alarm threshold values, the problem of insufficient mechanical fault monitoring effect in a traditional monitoring method is solved, and the monitoring result is more accurate.

The application discloses a kind of industrial equipment residual life prediction method and system based on bayesian updating, the prior model of equipment degradation process is established by historical failure data, and initial parameters are obtained using maximum likelihood estimation;Collect the condition monitoring data of equipment, after feature extraction and normalization processing, form the observation value of degradation amount;Using particle filtering algorithm and bayes theorem, the posterior distribution of degradation model parameters is recursively updated;Through Markov chain Monte Carlo sampling technology, the point estimate, confidence interval and failure probability density curve of residual life are output;According to the prediction result, dynamically adjust equipment maintenance plan, and trigger early warning when failure probability exceeds preset threshold, provide decision support for predictive maintenance;The system corresponds and contains data acquisition and pretreatment, prior model construction and the like module.The application is fused by probability classification and statistical inference, improves prediction accuracy, and provides reliable decision basis for equipment predictive maintenance.

The application relates to the technical field of marine engineering environment parameter calculation, and discloses a deepwater wave element calculation method suitable for long-period waves, which comprises the following steps: using global-scale ocean wave dynamics model output boundary wave parameters as boundary conditions; inputting the boundary conditions into a regional nearshore wave mathematical model to calculate and simulate parameters; comparing observed wave data with the simulated parameters, selecting a white cap dissipation coefficient with the minimum root mean square error, and feeding back the white cap dissipation coefficient to the model; using reanalysis wind field data to drive the regional nearshore wave mathematical model after the parameters are updated to generate a wave element set; extracting a wave element time sequence from the wave element set, screening and extracting annual extreme characteristic values according to a preset target period interval to generate an annual extreme sequence; fitting the annual extreme sequence by using a probability density curve, and outputting deepwater wave element return period extremes in combination with return period parameters. The application makes up for the defects of long-period swell energy loss in the open sea, and improves the accuracy of deepwater wave element extreme value calculation.

The invention discloses an AC / DC hybrid power grid static voltage stability margin online probability prediction method and system considering source load uncertainty, and belongs to the field of power system operation and control. The method comprises the following steps: constructing a multi-source random scene sample set containing wind-light and load uncertainty; preprocessing the real-time operation data through a sliding time window and data consistency verification; screening an optimal feature subset based on the SHAP value; point prediction is realized by using a Light GBM model in combination with hyper-parameter optimization; generating a probability density curve and a confidence interval through KDE kernel density estimation; and designing a prediction error and topological change double-triggered incremental learning updating mechanism. The method solves the problems that a traditional method is time-consuming in calculation, poor in self-adaption and insufficient in risk quantification, has the advantages of being high in prediction precision, high in timeliness and good in interpretability, can provide reliable decision support for large power grid dispatching, and improves the safe and stable operation level of a power grid.

The invention discloses an active sonar true and false target identification performance evaluation method based on a probability distribution model. The method comprises the steps of 1, setting parameter conditions; step 2, estimating class conditional probability density distribution; 3, performing true and false target identification forecast based on a binary hypothesis testing method; and step 4, outputting a true and false target identification performance result of the active sonar under the parameter condition. According to the method, on the basis of given parameter conditions and true and false target class condition probability density determination, a binary hypothesis testing method is utilized to carry out identification performance evaluation forecasting, and the probability of target error forecasting and correct forecasting can be obtained by calculating the area of an overlapped part and a non-overlapped part of a class condition probability density curve of true and false targets. The identification performance forecasting method can effectively evaluate and forecast true and false target identification performance, and has high engineering practical value.

The application provides a fracture logging identification method and system under oil-based mud conditions, which can improve the precision of fracture logging identification under oil-based mud conditions, effectively evaluate the development degree of formation fractures, and judge the filling property of the fractures. The identification method comprises the following steps: S1, based on the conventional logging data of a formation to be analyzed, rock wave impedance values are calculated according to a density curve and a P-wave interval transit time curve; S2, the rock wave impedance values calculated are corrected according to a shale content curve in the conventional logging data, corrected wave impedance values are calculated according to a preset correction formula, and a corrected wave impedance curve graph is drawn; and S3, whether the fractures of the formation to be analyzed are developed is judged based on the corrected wave impedance values calculated, if the corrected wave impedance values are not greater than a preset wave impedance threshold value, it is indicated that the fractures of the corresponding formation are developed fractures, otherwise, the fractures are undeveloped fractures.

The invention discloses a system reserve reservation and cost allocation method and system based on Monte Carlo sampling and Shapley values and a readable storage medium, and belongs to the technical field of power system economic dispatching and market transaction.The method comprises the following steps that firstly, a probability density curve of renewable energy / load prediction errors is deduced from parameter distribution; a typical uncertainty scene is generated through Monte Carlo simulation, and a standby demand quantification model is constructed; then, on the theoretical basis of marginal node electricity price, the electric energy market and the standby auxiliary market are jointly cleared, and the total standby cost of the system is calculated; and finally, the average marginal contribution of each main body to the system reserve cost is calculated by using a Shapley value method based on a cooperative game theory, and fair allocation of the reserve cost is realized. According to the method, through an efficient scene generation and fair allocation mechanism, the scientific and economic efficiency of system reserve reservation and reserve fee allocation is remarkably improved while the calculation precision is ensured, and the method is suitable for a power system with high-proportion new energy access.