22results about How to "Guaranteed forecast accuracy" patented technology

The present application relates to the technical field of bearing life prediction, in particular to a kind of gradient composite coating sliding bearing life prediction method, comprising the following steps: step one, build the coating degradation physical simulation model of multi-field coupling;Step two, adopt active learning algorithm to build high-precision proxy model, design active learning query strategy;Step three, online monitoring and multi-domain feature extraction;Step four, real-time state mapping and life prediction based on transfer learning;Step five, prediction result output and model updating.The present application can improve the accuracy of gradient composite coating sliding bearing life prediction, greatly reduce the operation load, and improve the prediction efficiency of bearing life.

The invention provides a new energy day-ahead prediction power correction method and system for a load peak period, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the new energy initial day-ahead prediction power of a prediction object in the whole period of a prediction day, and the historical electric power before the prediction day; constructing a new energy output deviation power set in the load peak period according to the historical electric power, and calculating a new energy output expected deviation value; the load peak period part in the initial day-ahead predicted power of the new energy is corrected according to the output expectation deviation value of the new energy, and then the day-ahead predicted power of the daily load peak period and the non-load peak period part of the initial day-ahead predicted power of the new energy are combined; and obtaining the new energy target day-ahead prediction power of the whole time period of the prediction day. According to the method, the prediction power before the new energy initial day is corrected through the obtained new energy output expected deviation value, the credibility of the power prediction result of the prediction day is improved, and the risk of a power supply gap is reduced.

The invention discloses an alpha-arylation reaction enantioselectivity prediction method based on machine learning. The alpha-arylation reaction enantioselectivity prediction method comprises the following steps: acquiring a reaction data set; calculating a physical and chemical descriptor based on a ligand molecular structure, and carrying out feature screening, standardization and dimension reduction processing on the descriptor to construct low-dimensional feature representation of the reaction; constructing and training a supervised machine learning regression model based on low-dimensional feature representation; and inputting a to-be-predicted ligand structure into the trained machine learning model to obtain a prediction result of the enantioselectivity of the alpha-arylation reaction, and applying the prediction result to virtual screening and optimization of candidate ligands. The method can effectively describe the influence of the ligand structure on the reaction enantioselectivity without depending on complex reaction mechanism assumption, realizes prediction of the enantioselectivity of the asymmetric catalytic reaction, and shows good generalization ability. According to the method, molecular structure characteristic engineering and machine learning modeling are combined, so that the accuracy of enantioselectivity prediction and the ligand screening efficiency are improved.

The application discloses a kind of sepsis multiple complications prediction method, system, storage medium and equipment, belong to intelligent medical technical field, method includes the following steps: S1. acquisition sepsis patient multidimensional time series physiological data;S2. medical perception feature vector is constructed to the time series physiological data;S3. sepsis multiple complications prediction model is constructed and introduced physiological logic space-time composite mask matrix;S4. the output multiple complications prediction probability using hierarchical expert hybrid network H-MoE;S5. the model is trained using dynamic weighted loss function based on task uncertainty;S6. risk output and feature contribution degree explanation;The model of the application is introduced by physiological logic mask and H-MoE architecture, realizes the accurate, synchronous prediction of sepsis complex complications, can effectively filter redundant features, and reveal the complex correlation therebetween, provide quantitative decision support for clinician, to realize early intervention and individualized treatment.

The embodiment of the application discloses a grid water depth prediction method and device based on an LSH attention mechanism, and relates to the field of hydrological monitoring. The method disclosed by the application first collects B batches of boundary flow time series data and grid water depth time series data of a target basin output by a two-dimensional IFMS model; then, standardizes the collected data, constructs a first feature tensor and transposes the first feature tensor into a second feature tensor; then, a weighted feature tensor is calculated based on an attention algorithm by using a Reformer model; finally, the weighted feature tensor is decoded by using a pre-trained iTransformer to obtain prediction values of grid water depths at future time points. The application can realize cross-time period prediction, improve prediction efficiency and accuracy, and provide a reliable basis for flood control and disaster reduction.

This invention discloses a wellbore trajectory identification method and device based on marker layer vertical depth, belonging to the field of oil and gas drilling technology. The method integrates actual drilling data and seismic data, formation data, and logging data from the surrounding work area to predict and invert marker layers that may be encountered during drilling, and reconstructs a planar structural map of the marker layer's burial depth. By capturing the marker layers encountered in the actual wellbore trajectory and calculating and comparing them with the vertical depth of the marker layers encountered in the preset wellbore trajectory, the downhole drill bit position is calculated and inferred, thereby achieving the identification and correction of the actual wellbore trajectory. This invention also discloses a wellbore trajectory identification device based on marker layer vertical depth. The technical solution of this invention can be used for wellbore trajectory identification during re-entry operations of old wells in new drilling operations, greatly reducing the drilling difficulty of re-entry and exploration of old wells, providing a reference for drilling process adjustments, and ultimately achieving effective plugging of old wells.

This invention discloses a real-time control method for electron cyclotron resonance heating power based on model predictive control, belonging to the field of electron cyclotron resonance heating control technology. The method includes a piecewise modeling step, dividing the operating range of the electron cyclotron resonance heating system into multiple power intervals according to the output power, and establishing a linear system dynamic model describing the dynamic characteristics of the system within each interval; and a real-time control loop step, acquiring the real-time output power and state estimate in each control cycle, determining the target model to be used based on the real-time output power and a hysteresis switching strategy, constructing a quadratic programming problem based on the target model and a preset power reference trajectory, including an objective function and constraints that minimize the power tracking error, solving for the optimal control input and applying it to the system. This invention aims to solve the problem that existing control methods struggle to simultaneously consider adjustment speed and multivariable constraints, achieving rapid and accurate control of electron cyclotron resonance heating power, and improving the safety and stability of system operation.

The present application relates to the technical field of auxiliary driving, and particularly provides a 3D detection method and device based on a fisheye camera, a medium and a vehicle, aiming to solve the problem of how to effectively reduce the complexity of a prediction model while ensuring the accuracy of a 3D detection process based on a fisheye camera. To this end, the present application pre-establishes a horizontal coordinate system, which coincides with the origin of the camera coordinate system of the fisheye camera and is parallel to the ground plane formed by the x-axis and the z-axis. When a 3D detection model is applied to 3D target detection of image data collected by the fisheye camera, only the yaw angle of the horizontal coordinate system needs to be predicted, without simultaneously predicting the yaw angle, the pitch angle and the roll angle, thereby effectively reducing the complexity of model prediction while ensuring the prediction accuracy, and the obtained 3D detection result is also easier to be associated with the appearance in the image.

This invention discloses a pre-trained online Gaussian process device and method for predicting hydrogen fuel cell voltage, applicable to marine propulsion systems. Addressing the problems of traditional models relying on real-ship data, weak generalization ability, and inability to adapt to battery aging, the device includes a sensor module, a memory, and a processor. The sensor module collects pressure, current, temperature, and voltage data; the memory stores the model and program; and the processor performs data processing and prediction calculations. The method pre-trains a Gaussian process regression model using experimental data, updates it online according to preset rules using real-time ship data, and outputs the prediction results after data preprocessing, model construction, updating, and prediction. This invention achieves high-precision prediction in zero-sample / few-sample stages, dynamically adapts to complex operating conditions and battery aging, operates efficiently without redundancy, and ensures reliable operation of marine hydrogen fuel cell propulsion systems.

The present application relates to the technical field of medical image analysis and computational pathology, and proposes a cancer survival prediction method based on a visual language model. The method first acquires a pathological whole field slice image of a cancer patient, detects the tissue area of the slice and divides it into multiple image blocks, and extracts visual features of the image blocks through a pre-trained visual encoder. Then, a metastasis diagnosis model is used to predict the image blocks, generate metastasis pseudo-labels and uncertainty information at the image block level, and obtain metastasis priors. On this basis, an adaptive hierarchical prompting mechanism is constructed, including slice-level survival semantic prompts and image block-level metastasis semantic prompts, and corresponding text semantic features are generated through a visual language model text encoder. Subsequently, the image block visual features, metastasis prior information and text semantic features are combined to generate slice-level representation features through a concept-guided feature aggregation module. Finally, the slice-level representation is optimized using an uncertainty perception calibration strategy, and the survival risk prediction result of the cancer patient is output according to the calibrated features. The present application can effectively utilize the multi-scale tissue morphology information and metastasis-related semantic information in the pathological whole field slice under the condition of few samples, realize the automatic prediction of cancer survival risk, and can be applied to a computer-aided pathological diagnosis and prognosis evaluation system.

The application discloses an AI intelligent glasses based on computer vision and a man-machine interaction method, and relates to the technical field of man-machine interaction. The application comprises the following steps: collecting eye movement data of a wearer, fitting a fixation rhythm baseline model according to the eye movement data, and obtaining interval duration and continuous duration of a fixation window; collecting a turning angle, a zoom ratio, an angular velocity and an angular acceleration parameter in a running process of a camera in real time. The application fits a fixation rhythm baseline model by collecting eye movement data of a wearer, combines real-time motion parameters of a camera to predict a period of a stable fixation window, divides an identification result into two types of static basic information and dynamic change information to perform hierarchical rendering scheduling, keeps the static basic information continuously displayed in a continuous motion process of the camera, and makes a smooth position offset according to the motion parameters, so that the wearer can continuously acquire basic scene information in the whole lens adjustment process without waiting for the motion to stop and then reading the content again.

This invention belongs to the field of pedestrian trajectory prediction technology, and particularly relates to a method for predicting pedestrian trajectories at unsignalized right-turn intersections based on game theory. The method includes the following steps: S1, acquiring historical data on pedestrians and vehicles at unsignalized right-turn intersections; S2, analyzing the human-vehicle game factors at unsignalized right-turn intersections and constructing a corresponding human-vehicle game model; S3, inserting the human-vehicle game model into a pre-set S-GAN model to obtain an SDG-GAN model for predicting pedestrian trajectories; S4, training the SDG-GAN model using the historical data acquired in S1; S5, using the trained SDG-GAN model to predict pedestrian trajectories at unsignalized right-turn intersections in real time. This invention ensures the accuracy of pedestrian trajectory prediction at unsignalized right-turn intersections and the effectiveness of assisted driving decisions at such intersections, thus balancing the efficiency and safety of vehicles passing through unsignalized right-turn intersections.

This invention relates to the field of pharmaceutical data management technology, and discloses a method and system for analyzing pharmaceutical inventory demand based on sales data. The method includes extracting pharmaceutical sales time-series characteristics, integrating external influencing factors to construct a gated cyclic unit prediction model, correcting demand forecasts based on real-time inventory status, integrating business rule parameters, establishing a mixed-integer linear programming model with the objective of minimizing total cost to generate replenishment suggestions, and simultaneously outputting an analysis report containing key decision factors. This application enables accurate, interpretable, and rule-constrained intelligent pharmaceutical replenishment decisions.

The present application relates to the technical field of multi-modal data prediction, and discloses a fashion preference prediction method and device, which comprises obtaining multi-modal data including images, texts and user behavior time sequence, performing feature extraction to obtain image features, text features and time sequence features; constructing spatial features based on city embedding tables and corresponding regional culture label codes; aligning the dimensions of the spatial features and the time sequence features, fusing the aligned spatial features and the time sequence features by using a gated attention mechanism to obtain spatio-temporal fusion features; splicing the spatio-temporal fusion features with the image features and the text features to obtain multi-modal fusion features; decoupling the multi-modal fusion features to obtain material decoupling features, style decoupling features and scene decoupling features; and after feature splicing of the various decoupling features, performing Transform coding and MLP classification to obtain fashion preference prediction probability.

This invention belongs to the field of load forecasting, and specifically relates to an automatic optimization method and system for electricity spot trading strategies. It includes: a data acquisition and processing module for acquiring and preprocessing multi-source time-series data; an electricity price forecasting module for inputting high-dimensional feature vectors into an electricity price forecasting model and outputting high-dimensional electricity price forecast vectors for future scheduling cycles; a strategy generation module for inputting high-dimensional electricity price forecast vectors into a strategy generation model and outputting a trading strategy parameter set; and an adaptive calibration module for online calibration of the electricity price forecasting model according to a preset calibration strategy. The preset calibration strategy involves real-time monitoring of the moving average of the high-dimensional electricity price forecasting error and the statistical drift index of the data distribution. When the error threshold is exceeded or the drift exceeds a preset confidence interval, an incremental parameter update mechanism is triggered. This invention solves the problem in existing technologies where static forecasting models cannot adapt to the non-stationary environment of the power system, leading to a decrease in forecast accuracy and consequently, strategy failure.

The invention provides a water conservancy project maintenance quota material unit price calculation method, and relates to the technical field of water conservancy maintenance cost calculation, engineering function attributes and market maturity of materials are quantified into comprehensive field regulatory factors, contribution degree thresholds are dynamically set for different material types according to the comprehensive field regulatory factors, key features are screened out, and the value of the key features is calculated. The method comprises the following steps: constructing an optimization model taking minimization of prediction errors and maximization of time sequence extrapolation stability as double objectives, performing automatic optimization on random forest hyper-parameters by using a particle swarm algorithm to obtain a combined prediction model, training and evaluating the model through forward chain type time sequence cross validation to ensure that the model meets stability and precision requirements, and performing prediction on the random forest hyper-parameters. And finally, predicting unit prices in multiple periods in the future and prediction intervals thereof by adopting a rolling window mechanism based on the model, and performing dynamic weighted synthesis by applying dual weights fusing time decay and interval width inverse ratio to generate an optimal quota unit price fitting a high-confidence market trend.

The invention discloses a method for predicting, regulating and controlling liquid level fluctuation of a slab continuous casting crystallizer. The method comprises the steps that current process control parameters of the crystallizer are obtained; inputting the current process control parameter into a pre-constructed liquid level fluctuation amplitude prediction model to obtain a crystallizer liquid level fluctuation amplitude which is output by the liquid level fluctuation amplitude prediction model and corresponds to the current process control parameter, the liquid level fluctuation amplitude prediction model is a power function expression which is obtained through regression analysis inversion by combining physical simulation experiment data with the crystallizer liquid level fluctuation amplitude as a response variable after a dimensionless relational expression used for describing crystallizer liquid level fluctuation is determined on the basis of a dimensional analysis principle. According to the method, the crystallizer liquid level fluctuation amplitude can be quickly predicted based on the current process control parameters by utilizing the prediction model. The model not only improves the prediction efficiency, but also quantifies the influence degree of each parameter on the fluctuation of the liquid level, provides a clear regulation and control direction, and is beneficial to the improvement of the quality of the casting blank under the working condition of high pulling speed.

The invention relates to a multi-camera collaborative vision and intelligent cutting path dynamic adjustment technology which is mainly used for high-precision motion and boundary prediction of flexible material three-dimensional cutting pieces in automatic processing. In order to solve the problem that an existing cutting path is insufficient in response to real-time deformation of a material, a multi-view video stream is obtained through annular multi-camera collection, space registration and illumination normalization, boundary features are extracted according to a depth separable convolution and graph neural network mixed model, and abnormal path replacement is achieved in combination with Bezier curve re-planning. The system has the capability of adaptively adjusting the reasoning frequency and the feature weighting strategy based on prediction error closed-loop feedback, so that the adaptability and precision of a cutting path to dynamic deformation are improved, errors and abnormities in the cutting process are remarkably reduced, and the stability and efficiency of overall automatic cutting are improved.

The invention relates to the technical field of motors, in particular to a driving motor NVH performance evaluation method and device, and the method comprises the following steps: S1, obtaining the time domain electromagnetic force distribution data of each skewed pole section stator tooth of a target motor through electromagnetic simulation; s2, performing spatial domain equivalent synthesis on the electromagnetic force of each skewed pole section to generate a global equivalent electromagnetic force time domain signal; s3, performing frequency domain-order conjoint analysis on the global equivalent electromagnetic force time domain signal, identifying a target frequency point having significant influence on NVH performance, and extracting a main space order and a main time order corresponding to the target frequency point to obtain a corresponding synthetic excitation force amplitude; s4, on the basis of the time domain electromagnetic force distribution data, under the target frequency point, the main space order and the main time order, calculating the electromagnetic force phase difference between the at least two groups of skewed pole sections, and generating a phase difference characterization quantity; and S5, inputting the target frequency point, the synthesized exciting force amplitude and the phase difference representation quantity into an NVH performance evaluation model, and outputting an NVH performance index.

The application provides a protein-metal ion binding site recognition method, device and equipment, and belongs to the field of protein detection. The method comprises the following steps: extracting features of known metal ion binding proteins to obtain a plurality of sample evolutionary information features; for an unknown protein sequence, candidate distant homologous metal ion binding proteins are determined through multiple sequence comparison and cosine similarity screening, and a training set is constructed; a composite framework of a bidirectional long short-term memory network and a full connection neural network is adopted, input features include evolutionary information features and physicochemical property features, and output is probability values of different metal ion binding sites; the composite framework is trained through the training set to obtain a site prediction model; and the prediction model is used to predict the binding sites of the unknown protein sequence. Stable and efficient prediction of metal ion binding sites is realized.

The application discloses a professional health examination result prediction method based on an expert system and machine learning, and the method is: after adding the number of visits information to the physical examination archive information to form an original data set after cleaning and standardizing, important features are extracted through deep learning to obtain a new data set by recombination; all hazard factor types of each physical examination personnel in the new data set are identified; the corresponding prediction label is obtained through logical judgment of each hazard factor according to the occupational health standard to form a decision set; the decision set is marked with occupational contraindications and suspected occupational diseases respectively, and the corresponding feature SOD and feature OC of the marked results are added to the new data set; the new data set after the expert system link is randomly divided into a training set and a test set, and more than two machine learning models are selected to train the training set to obtain respective prediction results; the prediction results of the more than two models are weighted and averaged through a weighted voting method to obtain the final output result. The application can more effectively identify the occupational health risk.