27 results about "PROJECTIONS PREDICTIONS" patented technology

A power grid short-term load prediction method comprises the following steps: determining a prediction target period; and predicting the power grid short-term load in the prediction target period by using a pre-trained prediction model, wherein the prediction model is: The present application provides a power grid short-term load prediction method, system, computer device and storage medium, comprehensively considers the influence of long-term trend, seasonal factors, special events such as holidays and weather changes on the power grid load, establishes a load prediction model to accurately predict the future load of the power grid.

According to the in-province power flow calculation and report generation method and platform based on the digital intelligent AI, an integrated solution of data input, model calculation and report output is formed, standardization and processizing of provincial level power flow planning work are achieved, and the planning efficiency and the planning precision are improved; the method comprises the steps of 1, predicting load electric quantity, comparing and selecting data, and forming a high-medium-low scheme; historical load data, historical electric quantity data and historical economic data are uploaded, the load electric quantity is predicted by adopting 10 methods, and 10 groups of load electric quantities are predicted and generated; calculating an average value of the 10 groups of data, taking data similar to the average value as a middle scheme, taking a group slightly higher than the middle scheme as a high scheme, and taking a group slightly lower than the middle scheme as a low scheme; step 2, power balance and power flow calculation; step 3, report generation; and calling the DeepSeek-R1-0528 model to complete the writing of the report.

This invention discloses a time series forecasting method, apparatus, program product, storage medium, and its applications, relating to the field of time series analysis, to address the problems of high computational complexity and poor reliability of prediction results in time series forecasting tasks. This invention trains an inference model using time series data. This inference model extracts dependencies between variables based on the autocorrelation matrix of the input original time series; integrates these dependencies into the original time series to obtain key time-series features; learns long-term dependencies between variables from these key time-series features; and maps the time series for future time steps from these long-term dependencies. This invention achieves accurate time series forecasting with high reliability at a computationally low level.

The application discloses a kind of day-ahead wind power power ramp event prediction methods, comprising the following steps: S1.day-ahead wind power prediction: using extreme value driving model, based on historical wind power and meteorological characteristics, produce the wind power prediction of Q time points of next day;S2.predicting confidence interval construction: using wind power oriented conformal inference method, for each future time point, construct confidence interval C;S3.ramp event detection based on confidence interval: the prediction result of next day wind power ramp event is obtained by confidence perception detection algorithm.The application improves the prediction accuracy and reliability of wind power ramp event with significant operation risk by fusing multi-scale time series feature analysis, customized loss function for extreme event and adaptive confidence interval construction technology with statistical guarantee.

This invention discloses an ozone forecasting method based on multi-model fusion, which consists of the following steps: data preprocessing, feature engineering, model training, model evaluation, model prediction, and model correction. This process ultimately yields reliable ozone prediction results. This method is easy to operate, data acquisition is readily available, and it does not require the collection of large amounts of precursor pollutant emission data. It can predict most ozone pollution conditions using only meteorological data, significantly improving prediction accuracy and compensating for the large biases in predictions based on a single model.

Methods and systems for managing inference models are disclosed. To manage inference models, a plurality of predictions that each indicate whether a state will occur may be obtained, the plurality of predictions being generated by respective inference models of at least one inference model. The plurality of predictions may be analyzed to obtain a statistical characterization regarding agreement in the plurality of predictions. A determination may be made regarding whether the statistical characterization meets criteria. In a first instance of the determination in which the statistical characterization meets the criteria, an action set may be obtained, the action set being based on an occurrence of the state predicted by the plurality of predictions to occur and usable to update an operating state of the data processing system to hedge against a risk of an undesired outcome from the occurrence of the state.

This invention relates to the field of enterprise management technology, specifically to an intelligent prediction method and system for enterprise management indicators, comprising: a data processing module, a prediction model module, and a prediction analysis module; the data processing module processes structured and unstructured data; the prediction model module constructs a prediction model to predict business activities and financial statements; and the prediction analysis module performs sensitivity assessment on the prediction results. This system, through processing and analyzing structured and unstructured data, and by combining macro and micro perspectives, achieves the prediction of enterprise management indicators, thereby improving the universality of the prediction.

Embodiments of the present disclosure relate to performance monitoring on prediction model In an aspect, a terminal device receives, from a network device, an indication of a first input type, or an indication of a second input type or an indication associated with a switch between the first input type and the second input type for a prediction model. The terminal device takes one of the following as an input of the prediction model for a next prediction: prediction results of the prediction model, wherein the indication of the first input type is received or wherein the indication associated with the switch is received and a previous input of the prediction model is measurements; or measurements of the feature, wherein the indication of the second input type is received or wherein the indication associated with the switch is received and a previous input of the prediction model is prediction results.

The application discloses a Markov attack path prediction method based on CVSS, and specifically comprises the following steps: step 1, scanning network host vulnerability information to generate a configuration file of.nessus; step 2, generating an attack graph: importing the configuration file generated in the previous step into Mulval, associating information between various host vulnerabilities through Mulval, and generating an attack graph; step 3, constructing a state transition graph: obtaining a simplified state transition graph according to the attack graph generated in step 2; step 4, initializing a Markov probability transition matrix: obtaining a probability transition matrix according to the state transition graph; and step 5, predicting an attack path probability. The method adopts a mode of measuring attack benefits to accurately predict a path to a single vulnerability level, realizes multi-step and multi-time prediction, simplifies the prediction method, and solves the problems of path redundancy, rationality and effectiveness of prior probability setting in the prediction path of the Bayesian model.

The application discloses a two-three-dimensional map adaptive projection matching method based on a hidden Markov chain, constructs a hidden Markov projection prediction model based on user preferences and projection characteristics, takes a map projection category as a hidden state, takes a user browsing position or a visual angle as an observation state, fuses user preferences and projection standard parameters to form a probability of each hidden state occurrence at each moment, updates an observation probability at each moment, and forms a current optimal projection matching combination through continuous iteration. The application integrates quantitative projection characteristics, combines user preferences, and influences a two-three-dimensional GIS visualization mode in a brand-new concept, selects a map projection suitable for different users and different scenes from an intelligent perspective, effectively overcomes browsing obstacles caused by a lack of projection field knowledge of part of users, and enhances a user's visualization experience.

The invention relates to the field of power system automation, and provides an industry power load prediction method based on ensemble learning and big data driving, and the method comprises the steps: S1, collecting multi-source data; s2, data preprocessing and feature engineering; s3, optimal feature selection and feature combination; s4, performing multi-model training and prediction; s5, predicting result fusion; and S6, recording a final prediction result. According to the method, a feature candidate set capable of comprehensively describing load driving factors is constructed by time sequence features, meteorological features and date social features, discrete weather types are quantified into continuous meteorological influence indexes, and dynamic weight time sequence features based on time attenuation coefficients are constructed; algorithms such as XGBoost and random forest are fused, and complex nonlinear relations and interaction effects in data can be automatically and efficiently learned and captured, so that the prediction precision is remarkably improved.

The invention belongs to the technical field of data mining, and discloses a standard necessary patent prediction method based on transfer learning and a related device. The method comprises the following steps: acquiring target domain patent data and target domain patent data to be predicted; obtaining a pre-trained source domain standard necessary patent prediction model; carrying out transfer learning on the pre-trained source domain standard necessary patent prediction model based on target domain patent data to obtain a target domain standard necessary patent prediction model; and processing the target domain to-be-predicted patent data by using the target domain standard necessary patent prediction model to obtain a prediction result whether the target domain to-be-predicted patent can become a standard necessary patent or not. According to the method, a method of combining feature-based migration and model-based migration is adopted, data distribution differences are utilized, migration learning is used for improving the performance of standard necessary patent prediction, and the prediction accuracy of data of different countries is improved.

The invention belongs to the technical field of corrosion protection and prediction of ocean engineering structures, and particularly relates to a method for predicting the cathode protection potential of an FPSO stand pipe supporting structure. According to the prediction method, the advantages of the seasonal autoregression integral moving average model and the long short-term memory neural network are combined, collaborative modeling and high-precision prediction of linear and nonlinear characteristics in the potential data are achieved, the prediction precision is high, and the prediction result robustness is high. The method for predicting the cathode protection potential of the FPSO riser support structure comprises the following steps: collecting historical potential time sequence data of the FPSO riser support structure under the condition of external cathode protection potential, and preprocessing the historical potential time sequence data; constructing and training a seasonal autoregressive integral moving average model; constructing and training a long-short-term memory neural network model; and performing combined prediction on the target time period to obtain a prediction result of the target time period of the cathode protection potential of the FPSO riser support structure.

The invention relates to a converter valve submodule monitoring method based on status words and a computer system, and belongs to the technical field of power system fault prediction. According to the invention, the fault state of the sub-module after the current time period is predicted according to the electrical quantity time sequence data and the status word sequence of the sub-module in the current time period, the prediction result is output in the form of the status word, and the sub-module is monitored according to the predicted status word. The electrical quantity time sequence data and the status word sequence of the sub-module are selected for prediction, fault related information of the sub-module is effectively reflected, the potential relation between each fault state and the abnormal state of the sub-module is captured through prediction of the fault state of the sub-module, accurate prediction and monitoring of the sub-module are achieved, and the fault diagnosis accuracy of the sub-module is improved. The fault prediction and monitoring capability of the flexible direct current converter valve sub-module is effectively improved, and the problem that potential abnormity cannot be found and processed in time due to the fact that real-time online analysis cannot be carried out on the sub-module in the prior art is solved.

The invention discloses a spaceflight BDR module life prediction method based on Arrhenius and Wiener models. The spaceflight BDR module life prediction method comprises the following steps of S1, determining life characteristic parameters of a BDR module; s2, designing a stepping accelerated degradation test scheme; s3, preprocessing the test data in the step S2; s4, establishing a mixed life prediction model; s5, importing the preprocessed test data into the mixed life prediction model for model parameter identification; and S6, based on the identified mixed life prediction model, extrapolating to obtain the actual life of the BDR module. According to the method, the actual service life of the BDR module under the typical working condition of 25 DEG C is predicted, the prediction error is lower than 9%, the development requirements of high reliability and long service life of spaceflight electronic products are met, the test effect is high, and sample consumption is low.

The invention discloses a data grading processing method based on a storage manager, and the method comprises the steps: obtaining a multi-dimensional dynamic feature vector through refined perception and feature extraction; performing hierarchical value comprehensive prediction by using a preset fusion model, and predicting a future access trend; collaborative decision-making and scheduling of resource awareness are carried out based on grading value scores; and carrying out atomization non-interference migration and effect feedback through a migration plan and an intelligent scheduling scheme. According to the method, through triple mechanisms of resource pool isolation, self-adaptive rate control and intelligent opportunity scheduling, it is ensured that data migration operation does not generate perceptible performance interference on foreground key services under any load condition, and the strict requirements of high-performance application for stability and low delay are met; and a feedback optimization closed loop is introduced to autonomously learn from historical decision results and dynamically adjust a prediction model, a decision threshold and a scheduling strategy, so that the system can continuously adapt to a complex and changeable service environment, long-term optimal operation is realized, and tedious manual adjustment and optimization are avoided.

The long-term and short-term intelligent prediction and early warning method for microseismic frequency and energy of rock burst mine comprises the following steps: 1. Importing microseismic event frequency and energy historical data; 2. Preprocessing the data of step 1; 3. Making a training set and a test set; 4. Building a microseismic prediction model; 5. Inputting the training set data into the prediction model for training; 6. Inputting the test set data into the prediction model for prediction, and recording the evaluation index result value; 7. Using the grid search method to determine the model hyperparameters; 8. Selecting the optimal hyperparameter combination of the evaluation index to the model of step 4; 9. Inputting the historical microseismic frequency and energy data into the model to realize short-term prediction; 10. Continuously adding the prediction results to the historical microseismic data to realize long-term prediction; and 11. Comparing the prediction value with the critical index to realize early warning. The present application can realize long-term and short-term intelligent prediction and early warning of microseismic frequency and energy through the above steps, has the advantages of intelligent prediction and early warning, solves the problems of poor generalization and poor dynamic prediction of traditional mathematical and physical models, and effectively improves the accuracy and timeliness of the intelligent prediction and early warning method for microseismic frequency and energy of rock burst mine.

The application belongs to the technical field of deep learning, and discloses a model migration-based airfoil gas-water dynamic coefficient prediction method.The prediction method comprises the following steps: establishing a sample database of airfoils and airfoil gas-water dynamic coefficients; generating an airfoil geometric image; generating an airfoil grayscale image; constructing a migration learning framework; and performing airfoil gas-water dynamic coefficient prediction.The prediction method solves the distribution deviation problem between air medium and water medium by constructing a model migration-based learning framework, efficiently realizes the cross-water-air medium domain migration of the model in a pre-training combined with fine-tuning manner, relaxes the dependence on airfoil hydrodynamic coefficient samples, and improves the efficiency and prediction accuracy of the prediction model.

The present application relates to a water level height prediction method and system based on spatial expansion convolution and multi-head multivariate attention, belonging to the data processing technical field for prediction purposes, comprising the steps: given water level height time series data as input sequence, using reversible instance normalization to operate on the data; time position information is added as a variable to the input sequence; the sequence containing time information passes through a lightweight embedding layer; the prediction method of spatial expansion convolution and multi-head multivariate attention is used to train and predict the data set respectively; the prediction sequence is transmitted to the feedforward neural network; the time position variable in the prediction result is removed, and the final subway water flooding height prediction result is obtained through inverse instance normalization. The present application emphasizes the exploration of complex dependency relationship between variables, and captures features from two dimensions of time and space, making up for the lack of water level height time series variable dimension feature, effectively improving the prediction accuracy of water level height.

In order to solve the problem that the existing time series prediction method cannot extract multi-scale information and the dependence between variables at the same time, a time series prediction method based on a multi-scale network is provided.The method first constructs a prediction model MSLA (Multi-scale and Local Attention), which uses a multi-scale network to extract information at different scales of data;secondly, the correlation information between variables is extracted through a local attention module;and then the weighted fusion is carried out through a feature fusion module;finally, the prediction is carried out.The prediction model achieves good results under different real scene data sets, and the model provided by the application can provide more accurate prediction results compared with other models through experimental verification, and good experimental results are achieved in the multi-scene experiment, which shows good generalization ability and has great application prospect in the civil aviation field.

The present application relates to a kind of structured data table field missing oriented prediction filling method, belong to data management, data preprocessing, machine learning and artificial intelligence technology cross field.The method of the present application includes the following steps: data reading and missing value detection, classification statistics;Complete record set DBSCAN clustering processing;Cluster training prediction model based on autoencoder;Missing value prediction;Predictive result fusion, determine final filling value;Filling value backfilling and verification.The method of the present application has advantages in filling precision, robustness, adaptability, engineering landing, etc., compared with prior art, can effectively solve the defects existing in prior art.

The invention belongs to the technical field of deep learning, and discloses an airfoil profile gas-water dynamic coefficient prediction method based on model migration. The prediction method comprises the following steps: establishing a sample database of airfoils and airfoil gas-water dynamic coefficients; generating an airfoil geometric image; generating an airfoil grayscale image; constructing a transfer learning framework; and carrying out airfoil profile gas-water dynamic coefficient prediction. According to the prediction method, the problem of distribution deviation existing between an air medium and a water medium is solved by constructing a learning framework based on model migration, cross-water-air medium domain migration of the model is efficiently achieved in the mode that pre-training is combined with fine adjustment, dependence on an airfoil hydrodynamic coefficient sample is relaxed, and meanwhile the prediction accuracy is improved. And the efficiency and the prediction precision of the prediction model are improved.

This invention relates to a method for predicting financing demand based on a heterogeneous clustering distributed lag model. The method includes the following steps: constructing a set of predictive and response variables for investors; establishing a heterogeneous clustering distributed lag model and training it using an improved K-means algorithm, determining the number of clusters using the Bayesian information criterion; and performing out-of-sample prediction based on the trained model to predict changes in the financing balance under different financing interest rate adjustment schemes. The method, apparatus, processor, and computer-readable storage medium for predicting financing demand based on a heterogeneous clustering distributed lag model of this invention aim to utilize economic and econometric models, combined with customer credit information and macroeconomic indicators, to predict the likelihood of customers adjusting financing interest rates and the changes in the financing balance after interest rate reductions. By identifying the customer group truly affected by interest rate changes, this helps companies adjust interest rate strategies more accurately and improve the efficiency of financing balance management.

This invention discloses a quantitative microseismic prediction method based on the fusion of theoretical model constraints and data-driven approaches. The method comprises: 1. Obtaining raw microseismic data from coal mine working faces to form an initial sample dataset; 2. Preprocessing the data and dividing it into training and test sets; 3. Constructing a Long Short-Term Memory (LSTM) network model; 4. Sampling historical microseismic data as samples for quantitative microseismic event prediction; 5. Constructing a multi-objective optimization model containing seven sub-objective functions: microseismic temporal fractal dimension, microseismic spatial fractal dimension, microseismic energy fractal dimension, total predicted energy of microseismic events, microseismic reconstructed mining stress, microseismic activity process, and the microseismic energy-frequency power law b-value; 6. Calculating the optimal sample as the predicted value. This invention achieves quantitative microseismic event prediction based on the fusion of theoretical model constraints and data-driven approaches through the above steps. The prediction effect is good, and the prediction results can effectively guide the control of on-site production activities, the implementation of pressure relief measures, and disaster early warning.

The present application relates to the technical field of concrete quality control, in particular to a concrete strength prediction system and method based on SVR; the present application collects multi-dimensional data of rebound test, compressive strength test and carbonation depth value determination through a data acquisition module, provides rich input features for the model, and enhances the prediction ability of the model; the Grubbs criterion is used to eliminate abnormal data, ensuring the quality and consistency of the input data and reducing the influence of noise on model training. The present application respectively establishes a concrete rebound curved surface regression equation and a support vector regression model, both of which serve as prediction criteria, and data fusion is carried out through a Kalman filter, further improving the prediction accuracy and reliability. Through the automatic and standardized data processing process, including data acquisition, preprocessing, regression prediction, SVR prediction and data fusion, the present application realizes the rapid prediction of concrete strength and improves the prediction efficiency.

The application discloses a wind farm cluster power prediction method of structural consistency generated graph network, comprising: wind farm multi-source data input and variable definition; decoupling coding of endogenous power sequence and exogenous meteorological variables; wind farm operation mode memory bank construction and sample level mode activation; mode driven wind turbine dynamic graph structure generation; future power rough prediction based on the generated model; structural consistency constraint of the prediction sequence and the real sequence; operation mode consistency constraint; power representation propagation and prediction refinement based on the dynamic graph; joint optimization target; model reasoning and actual deployment. Through the collaborative design of the mode memory bank, the dynamic graph generation and the structural consistency constraint, the application solves the problems in the traditional wind power prediction method, such as the difficulty of the static graph structure to adapt to complex wind conditions, the information interference caused by the multi-source variable fusion, the lack of structural rationality of the prediction result and the like, and can significantly improve the accuracy, the robustness and the engineering applicability of the wind farm cluster power prediction.

The application discloses a typhoon track prediction method based on deep learning and particle filtering, and comprises the following steps: constructing a typhoon track prediction model based on deep learning and particle filtering, which is based on a Bayesian framework and is divided into typhoon observation and typhoon updating; in the typhoon observation, a typhoon probability map is obtained by means of deep learning and considering typhoon features, the probability map is used to correct and constrain the predicted typhoon range in the typhoon track prediction model; in the typhoon updating, a multi-modal neural network is used to predict the typhoon position in combination with historical environmental factors, the prediction process simultaneously comprises particle prediction, the newly obtained particle weight is updated by means of the probability map obtained by the typhoon observation, the particle weighted average is used to obtain the posterior estimation of the typhoon position at the next moment, and the posterior estimation is used as the input of the next prediction; the typhoon observation and the typhoon updating are continuously cycled to obtain the posterior estimation of the typhoon eye position and obtain the typhoon track prediction result. The typhoon path prediction problem under the condition that the real path is not real-time available is solved.