1191 results about "Statistical model" patented technology

A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values.

The invention provides a new energy storage consumption capability index prediction method, and relates to the field of power system operation, and the method comprises the steps: building a nonlinear mapping relation between solar radiation intensity, photovoltaic module conversion efficiency, a temperature coefficient, an aging attenuation rate factor and power generation efficiency for photovoltaic power generation, so as to dynamically correct a photovoltaic power generation potential output prediction value; based on a potential output prediction result of photovoltaic and wind power generation, solving an energy storage capacity configuration and operation strategy through an intelligent optimization algorithm to maximize the benefit of an energy storage system, and obtaining a dynamic prediction value of an energy storage consumption capability index; and combining prediction results of the statistical model based on the historical data, the dynamic model based on the physical mechanism and the data-driven model based on machine learning by adopting an adaptive weighted fusion strategy. According to the method, accurate prediction of potential output of new energy power generation and dynamic evaluation of the energy storage absorption capability are realized, and effective technical support is provided for new energy grid connection and optimal configuration of an energy storage system.

Systems and methods are disclosed for implementing advanced statistical models and machine-learning algorithms to generate predictions. The method includes receiving a plurality of data from one or more sources; processing the plurality of data to select one or more relevant variables; training one or more prediction models based on the one or more relevant variables, and a combination of an advanced statistical model and a machine-learning model; evaluating performance of the one or more trained prediction models based on one or more validation techniques; and deploying at least one prediction model based on the performance for generating predictions.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention discloses a sugarcane yield prediction method and system based on machine learning, and relates to the technical field of intelligent agriculture. Through comprehensive application of technical means of data acquisition, preprocessing, feature extraction, model construction, training, prediction, decision support and the like, accurate prediction of the sugarcane yield is realized. According to the method, multi-source data is combined, key features are extracted by using an advanced machine learning algorithm, and a high-performance prediction model is constructed. Through historical data training and model parameter optimization, prediction accuracy and stability are improved. Meanwhile, the system provides a user-friendly interactive interface, and collects user feedback to continuously improve the model. The method is suitable for the field of agriculture, especially for sugarcane yield prediction, solves the problem that traditional methods such as experience estimation and statistical model prediction are inaccurate, realizes high-precision prediction through technologies such as machine learning, and has important significance for promoting agricultural sustainable development.

The invention discloses a rock-fill dam material parameter uncertainty inversion method based on Bayesian inference, and the method specifically comprises the steps: building a rock-fill dam finite element model, and determining the prior distribution of to-be-inverted parameters; constructing an sPCE proxy model based on prior distribution of inversion parameters; a rock-fill dam displacement statistical model is established, and actually-measured displacement water pressure components are separated; constructing a rock-fill dam material likelihood function; carrying out uncertainty inversion on the rock-fill dam material parameters; and updating and inverting the uncertainty of the rock-fill dam material parameters. According to the method, a Bayesian inference method is utilized, and uncertainty distribution characteristics of different partition material parameters of a dam body are obtained through actual measurement displacement data, parameter prior distribution and a likelihood function of the dam. According to the method, the defect that parameter uncertainty cannot be considered in a traditional inversion method is overcome, and obtained parameter values are closer to engineering practice; compared with a traditional inversion method, parameter updating inversion can be carried out through monitoring data in different periods, and continuous tracking of parameter timeliness evolution is achieved.

Methods and systems for predicting the onset and progression of neurodegenerative diseases using blood test data and machine learning models are disclosed. The method involves receiving blood test values for a target subject over a previous time period, extracting features from the blood test data, and applying a trained predictive model to compute a risk score indicating the probability of developing a neurodegenerative disease. The system includes a processor that executes code to perform the method and can be implemented as a standalone application, cloud-based service, or integrated with healthcare systems. The predictive models are trained using supervised learning on labeled data and can include statistical models or machine learning algorithms. The systems and methods enable early detection of neurodegenerative diseases, personalized risk prediction, and integration with existing healthcare infrastructure. By leveraging blood test data and machine learning techniques, patient outcomes may be improved.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a multi-target water supply scheduling method and system based on big data driving, and the method comprises the steps: collecting the multi-source data of a plurality of water supply targets, and enabling the scheme to obtain more reliable data support in the scheduling planning of the water supply targets; based on multi-source data serving as input features, the scheme combines the multi-source data with a graph space-time prediction model, a season statistical model and a gradient lifting regression model to obtain water consumption predicted by the three models respectively, prediction results of the three models are integrated through error adaptive weighting, more reliable predicted water consumption requirements are obtained, and the prediction efficiency is improved. And after the linear distribution model of the water source and the water supply targets is solved, initial water supply distribution schemes in one-to-one correspondence with the multiple water supply targets are obtained, the water supply accuracy of the water supply targets can be improved, multiple costs are used as targets to be minimized, and after multi-constraint combined solving, the water supply accuracy of the water supply targets can be improved. Therefore, the scheduling cost optimization is considered under the condition that the multi-target water supply scheduling meets flexibility and reliability.

The invention discloses a classified processing pit-pond water storage amount remote sensing statistics joint estimation method, and relates to the technical field of water resource remote sensing monitoring, and the method comprises the steps: calculating the pit-pond density, and determining the grid width; calculating a normalized water body index, and extracting a pond water surface boundary; dividing the pit pond into an invertible pit pond and a non-invertible pit pond according to the water surface area and width of the pit pond; calculating the water storage amount of the invertible pit-pond by combining the area of the invertible pit-pond; calculating the water storage amount of the non-invertible pit pond by using the relationship between the water surface area of the non-invertible pit pond and the statistical model; and calculating the total water storage amount of the region, and generating a spatial distribution diagram of the water storage amount. According to the method, remote sensing inversion and a statistical model method are used for estimating the water storage amount, targeted processing can be carried out on different types of ponds, remote sensing images are combined with actually measured data, the water depth inversion precision of the invertible ponds can be effectively improved, and therefore a more reliable water storage amount estimation result is provided.

The invention relates to the technical field of crop saline-alkaline tolerance analysis, in particular to a rice saline-alkaline tolerance prediction method and system based on big data analysis, and the method comprises the following steps: collecting soil environment data, rice physiological phenotype data and historical stress response data, and constructing a multi-dimensional data cube by using a space-time alignment engine; quantization feature analysis is adopted to carry out quantum probability modeling on Na < + > / K < + > ion transmembrane transport and PSII photosynthetic exciton transfer features, and a quantum probability cloud chart of rice physiological response is generated. Based on a geodesic line domain adaptation model of geodesic line mapping, a soil conductivity thermodynamic diagram and a quantum probability cloud chart are fused, a rice saline-alkaline tolerance adaptability prediction model is established, finally, a three-dimensional saline-alkaline tolerance performance prediction map is output, and the adaptability of rice varieties in specific saline-alkaline land is visually displayed. According to the method, the regional generalization ability of saline-alkali tolerant varieties is effectively improved, the limitation of a traditional statistical model is broken through, and a scientific basis is provided for saline-alkali tolerant rice breeding and cultivation and saline-alkali soil agricultural optimization management.

The invention discloses an automobile part surface defect detection method based on a large model. The method comprises the following steps: acquiring feature data of a target surface by using a multi-dimensional imaging device; feature extraction, clustering analysis and abnormal mode recognition are carried out through the large model; extracting an abnormal region and generating a statistical model; comparing the statistical model with standard sample data to determine the position of an abnormal region; defect types are classified and identified, and resource allocation is dynamically optimized and calculated to adapt to detection complexity; and finally outputting defect types and positions. According to the method, the spectral imaging technology and the deep learning algorithm are combined, high-precision detection, positioning and classification of defects such as cracks, scratches and bubbles are achieved, a visual detection report is generated, the method is suitable for real-time quality control in large-scale production, the detection efficiency and accuracy are improved, and manual errors and cost are reduced.

The invention relates to the technical field of data management, in particular to a data asset sorting method, which comprises the following steps: preliminarily classifying all data assets in an enterprise, performing quality evaluation on the preliminarily classified data assets, generating a quality evaluation report, and performing data redundancy detection and optimization based on the quality evaluation result. Cleaning redundant data by adopting a deduplication algorithm and an optimization algorithm, carrying out data correlation analysis, generating a data correlation graph through a graph model and a statistical model, and determining the relationship between data sets; and finally, according to a correlation analysis result, integrating the data sets with high correlation into a unified data set by adopting a data fusion algorithm, and reducing a storage space through a data compression technology. According to the method, the accuracy of data classification and management can be improved, the data redundancy is reduced, the data storage and access efficiency is optimized, and the data security and privacy protection are enhanced through security evaluation and protection measures.

The invention relates to the technical field of warehouse management, in particular to an unmanned warehouse management method and system with a real-time inventory function, and the method comprises the steps: building a signal strength-probability statistical model through return signal data generated in a historical inventory process, and carrying out the stability verification of each RFID tag return signal in a current inventory period, and under the condition that the stability is confirmed, carrying out preliminary positioning based on the time difference of arrival and determining the probability of the storage unit. And finally, the preliminary positioning is verified in combination with the probability of the storage unit and the checking information of the last checking period, so that the situation that whether the position of the RFID tag is changed really cannot be confirmed due to the fact that verification cannot be carried out when the positioning jumps is avoided. According to the invention, low-cost and high-reliability material positioning can be realized by using an existing RFID system, so that real-time and accurate checking is realized, and an information basis is provided for management of unmanned warehouses.

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

The invention discloses an ozone concentration prediction method based on a GCN-BiLSTM-Attention space-time neural network. The ozone concentration prediction method is used for predicting O3 concentration in the next 24 hours. The model fuses a graph convolutional network GCN, an attention mechanism and a bidirectional long short-term memory network BiLSTM so as to learn the space-time dependence of the air pollutant concentration. The model can effectively improve the prediction accuracy of the O3 concentration by integrating meteorological data, geographical location information of a national control site and historical data of concentrations of pollutants such as O3, PM2.5 and CO. A research result shows that compared with a traditional statistical model and a single deep learning model, the GCN-BiLSTM-Attention shows higher accuracy and a lower error rate on a plurality of evaluation indexes.

The invention discloses a surface topography modeling method and particularly provides a roughness calculation formula based on Fourier series and polynomial combination, Fourier series can effectively describe periodic roughness features of a surface, a polynomial function can describe an overall deformation trend of the surface, the advantages of the Fourier series and the polynomial function are combined, and the surface topography modeling method has the advantages that the surface topography modeling efficiency is improved. The method can accurately simulate the random roughness and overall deformation characteristics of material corrosion, terrain, sea wave plane and other phenomena, and avoids the problem that the traditional surface topography modeling method is difficult to accurately describe the randomness and nonlinear characteristics of a complex surface based on simple statistical model or experimental data fitting. According to the method, the morphological characteristics of phenomena such as material corrosion, topography and sea wave planes can be simulated more accurately, and technical support is provided for analysis and prediction in related fields.

The invention discloses a slope deformation trend prediction method based on three-dimensional point cloud and deep learning, and relates to the technical field of geological disasters, and the method comprises the following steps: S1, obtaining multi-time sequence three-dimensional point cloud data of a target slope, S2, carrying out the preprocessing, obtaining a standardized time sequence point cloud data set, and carrying out the prediction of the deformation trend of the target slope. S3, extracting slope deformation characteristic parameters from the standardized time sequence point cloud data set, S4, constructing a prediction model, S5, integrating the data into a model training sample, and training and optimizing the deep learning prediction model, and S6, inputting the data into the trained deep learning prediction model, and outputting a deformation trend prediction result of a target slope. And S7, carrying out reliability evaluation on the deformation trend prediction result, and generating a final prediction report. According to the method, through the deep learning model fusing the CNN and the attention mechanism LSTM, the spatial relevance and the time dynamics of slope deformation can be mined at the same time, compared with a traditional statistical model, the prediction precision is improved, and the method is especially suitable for long-term deformation trend prediction.

The invention discloses an integrated wind power prediction method and system based on a multi-source data set. The method comprises the following steps: acquiring historical meteorological factors and fan operation data; preprocessing the historical meteorological factors and the fan operation data to obtain a data set; based on the data set, key features are obtained through a Boruta algorithm, the key features are processed through a sliding window mechanism and a VMD algorithm, and an enhanced feature matrix is obtained; inputting the enhanced feature matrix into a deep learning model for prediction, and obtaining a preliminary prediction value; and carrying out residual error correction and fusion on the preliminary prediction value to obtain a final wind power prediction result. The method effectively improves the capability of processing wind energy intermittency, volatility and randomness, avoids the defects that a physical model is complex in calculation and a statistical model is difficult to process nonlinear and non-stationary features, reduces the over-fitting risk of a single deep learning model, can improve the prediction accuracy and stability, and improves the prediction efficiency. And the method has better generalization ability in practical application.

A three-dimensional human head reconstruction method, an electronic device and a non-transient computer-readable storage medium are provided. The method includes: acquiring a target portrait image; inputting the target portrait image into a target model to obtain an output result of the target model; wherein the target model is obtained by pre-training with a plurality of training samples which are generated according to a sample portrait image and a sample three-dimensional human head model, the sample three-dimensional human head model is obtained by iteratively fitting a standard three-dimensional human face statistical model according to two-dimensional feature information related to a portrait in the sample portrait image, and the two-dimensional feature information includes human face feature points and a human head projection contour line; and generating, according to the output result, a target three-dimensional human head model corresponding to the target portrait image.

The invention relates to the technical field of agricultural intelligent irrigation, in particular to an intelligent irrigation control system and method based on meteorological environment acquisition, and the system comprises a sensing layer, a transmission layer, a control layer, a cloud layer and an execution layer. Then meteorological environment data collected by the sensing layer is transmitted to the control layer through a wireless transmitting module and a wireless receiving module, and a controller of the control layer is integrated with an edge computing unit, a communication unit and a decision-making unit; the problems that an existing agricultural meteorological monitoring and irrigation control system is high in network transmission load coefficient, and the edge computing capability of sensor nodes cannot be effectively utilized; the problem that an irrigation control algorithm based on a static threshold mechanism or a single statistical model lacks adaptive processing of rainfall time sequence prediction and dynamic response ability of specific requirements of crop growth stages is solved.

The invention relates to an earth and rockfill dam seepage safety monitoring, forecasting and early warning system and method, and belongs to the technical field of dam safety monitoring and artificial intelligence, and the system comprises a data acquisition module, a dam safety monitoring and forecasting model module, a dam safety early warning analysis module, and an early warning setting and pushing module. On the basis of a statistical model, engineering data such as geological exploration, detection and test are collected and analyzed according to rainwater conditions and dam safety monitoring data, the equivalent reservoir water level of a leakage channel is deduced, a seepage pressure monitoring and forecasting model considering the lagging time of the leakage channel is established, seepage pressure four-level monitoring and early warning indexes are constructed, and the seepage pressure monitoring and early warning efficiency is improved. On-line early warning of dam seepage safety is achieved, and the safety state of the dam is evaluated in real time.

A management node local to a customer site of a private communications network stores a model. The model is a compact version of a visual language model remote from the customer site. A first screen shot of a dashboard of telemetry data measured from the private communications network is accessed. A prompt is formulated comprising the first screen shot and information to adapt the model to the private communications network via few shot learning. The prompt is submitted to the model. An output is received from the model comprising textual information about anomalies or trends depicted in the first screen shot. The output is checked against data from a statistical model of the telemetry data, the statistical model being independent of the model. In response to the check being successful, an action is triggered to manage the private communications network according to the output.

The invention discloses an information management method based on intelligent classification and efficient retrieval, relates to the technical field of information management, and solves the technical problem of inaccurate retrieval results caused by lack of deep understanding of information semantics and content. Secondary classification is carried out based on semantics, a standard classification result is determined through an integration and screening mechanism, the information classification accuracy is improved, the core content of information is reflected, word segmentation is carried out through a statistical model to determine retrieval heads, the retrieval heads without retrieval content are removed, the retrieval efficiency is improved, the content to be analyzed is screened by calculating the number proportion, and the retrieval efficiency is improved. Retrieval content is screened according to a scientific quantitative standard, information highly related to user requirements can be quickly positioned, the association strength of a retrieval head and retrieval information is accurately analyzed by means of assigning a grammatical position of a pre-selected retrieval head, calculating cosine similarity and the like, and a strong-association retrieval head and a weak-association retrieval head are distinguished.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention discloses an underground water pollutant migration simulation method coupled with a physical mechanism and data driving, which comprises the following steps: establishing a statistical model through a machine learning method to correct the structural error of an underground water model, and expressing the output of the underground water model as the sum of a physical mechanism model, a structural error model and an observation error. According to the method, underground water pollutant migration simulation under complex conditions can be carried out, structural errors existing in the model are effectively corrected based on a data driving method, the defect that a traditional underground water model is poor in simulation precision under the complex conditions is overcome, and the application scene of underground water pollutant migration simulation is expanded; according to the method, underground water pollution migration simulation can be carried out aiming at novel and macromolecular organic pollutants such as polycyclic aromatic hydrocarbon, the defect that a traditional underground water model cannot accurately describe pollutant and colloid co-migration behaviors is overcome, the uncertainty of a model prediction result is remarkably reduced, the accuracy and reliability of the model prediction result are improved, and the method is suitable for popularization and application. And the model prediction performance is improved.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention relates to the technical field of power generation prediction data processing, and discloses a photovoltaic power station power generation prediction method based on multi-model fusion, which combines a photovoltaic physical model, a power generation statistical model and a power generation prediction model, not only considers a physical rule, but also combines statistical learning and machine learning to improve the stability and accuracy of prediction, and improves the prediction efficiency. And predicting the generating capacity of the photovoltaic power station by using environmental meteorological data, electrical parameters of the photovoltaic power station and historical power generation data. Accurate photovoltaic power generation power prediction is beneficial to better arrangement of power generation plans and resource scheduling of the power grid. The prediction precision and stability are improved through multi-model fusion, so that power grid dispatching is more accurate and efficient, and power grid instability factors caused by photovoltaic power generation fluctuation are reduced. Therefore, photovoltaic power generation can be connected to a power grid more stably, the power demand is met, and wide application and development of clean energy are promoted.

The invention discloses an analysis and prediction system based on disease data, which belongs to the technical field of disease data analysis and comprises a cross-modal distillation data fusion module, a time domain causal chain analysis module, a multi-level disease risk prediction module, a disease critical state real-time monitoring module, a self-adaptive intervention strategy generation module and a clinical collaboration and feedback optimization module. A time decay factor and a smoothing coefficient are introduced through the time domain causal chain analysis module, the causal strength between variables is updated in real time, nonlinear changes of disease progression are adapted, and individual risk scores are generated through the multi-level disease risk prediction module in combination with the dynamic weight and the time decay factor of the causal atlas. A spatial smoothing function is introduced to suppress the influence of an abnormal value, group risks are quantified through a modal mean value and a standard deviation, individual weights are dynamically adjusted through a feedback mechanism, the long-term stability of prediction is improved, causal strength and feature importance are used for joint modeling, and the problem that a traditional statistical model ignores a potential causal path is avoided.