11results about How to "Forecast stability" patented technology

The invention relates to a building load prediction method based on forward decomposition denoising and deep integrated learning, and the method comprises the steps: carrying out the forward decomposition of original load data into a plurality of IMFs components through employing a CEEMDAN algorithm, removing a high-frequency noise component, and carrying out the reconstruction fusion of residual components, and obtaining clean load data; then, a DWedRVFL neural network model is constructed, the DWedRVFL neural network model captures the dynamic change of the load sequence by adopting the predicted latest precision, and a diversity index is constructed to utilize the multi-scale features of different output layers; in addition, a sorting strategy is constructed to integrate the latest precision and diversity contributions, and meanwhile, the influence of an abnormal prediction value on combined prediction is avoided; and then, the historical load, the weather condition and the personnel occupation data form a connection matrix, and the connection matrix is fed into the DWedRVFL neural network model for learning, so that the accuracy and the stability of building load prediction are improved. The method is beneficial to improving the precision and stability of short-term building load prediction.

The invention belongs to the technical field of biological information processing, and particularly relates to a method and system for constructing a risk prediction model from Kawasaki disease to huge coronary artery tumor. The invention provides a Kawasaki disease MGCAA risk prediction model construction method and system, and the method comprises the steps: building a prediction model based on six conventional clinical variables through employing a random forest (RF ranger) algorithm; an SHAP explanation mechanism is introduced, so that the contribution of each variable to a prediction result can be visually displayed in global and individual levels, and the transparency and clinical interpretability of the model are improved; through external verification and intercept-only recalibration, the reliability and the applicability of the model in different groups of people can be ensured; according to the method, an online webpage tool (Shiny App) is deployed, and a doctor can input clinical data of a patient in real time and immediately obtain individualized risk prediction and explanation, so that clinical early recognition of an MGCAA high-risk child patient is realized, and formulation of an individualized treatment scheme and early intervention measures is assisted.

This invention discloses a method for calculating the three-phase phase equilibrium of oil, gas, and water at the nanoscale, relating to the field of shale oil reservoir development technology. The method includes the following steps: receiving input parameters; constructing a total pressure model of the confined fluid at the nanoscale based on the input parameters; constructing a three-phase pressure equilibrium model among the oil, gas, and water phases at the nanoscale based on the total pressure model; constructing a phase stability test model, and determining whether phase separation occurs in the system under given temperature and pressure conditions based on the tangent plane distance function; when the phase stability test model determines that phase separation has occurred, calculating the phase fraction and composition of the oil-gas-water three phases by iteratively solving for the conditions satisfying the three-phase pressure equilibrium model and the equality of the fugacity of each phase component, and outputting the results. This invention can accurately and stably predict the fluid phase state in the nanopores of shale oil reservoirs, providing key technical support for its component simulation and efficient development.

The invention relates to the field of data model integration evaluation analysis, in particular to an analysis management method and system for improving the voltage reduction rate of an opinion work order, and the method comprises the steps: generating a multi-source integration evaluation model through an influence factor parameter and a voltage reduction rate observation value in a corresponding associated time period, and obtaining a total voltage reduction rate observation value in all time periods; the integration of multi-source data can make full use of information of different sources, the understanding and prediction ability of the model on the change of the pressure reduction rate is enhanced, so that the prediction accuracy is improved, the integrated model combines the advantages of a plurality of prediction algorithms, the deviation and error possibly generated by a single model are reduced, the overall prediction is more stable, and the prediction efficiency is improved. By quantifying the parameters of the influence factors, the model can clearly identify the specific influence of each factor on the pressure reduction rate, and powerful support is provided for subsequent decision making.

The present invention relates to an algorithm for using a GRU neural network to assist navigation during a GNSS signal interruption. When the GNSS signal is available, the speed, heading and output of the IMU of the INS will be used as input features for training the GRU model, and the position increment of the GNSS is the output of the model. Once the GNSS signal is lost, the information of the INS will be fed into the GRU model to generate a pseudo-GNSS increment. After accumulating the pseudo-GNSS increment, the pseudo-GNSS position will be sent to the Kalman filter to correct the INS navigation solution result. In addition, considering that it is difficult to obtain a large amount of GNSS observation data with good observation in complex scenarios, the number of observable satellites in some areas may be 1 to 3. At this time, the use of a tightly coupled model can still perform combined navigation, which guarantees a more effective positioning result to a certain extent and can provide better training data for the neural network. Therefore, the present invention selects a GRU-assisted tightly coupled mode.

The invention aims to provide recognition equipment and recognition method for an emotional communication mode, and the equipment comprises a camera, a microphone, a feature extraction unit, a first emotion recognition unit, a second emotion recognition unit and an emotional communication mode recognition unit, and the camera collects images of family members and outputs video signals; the microphone collects the sound of the family members and outputs an audio signal; the feature extraction unit receives the video signal and the audio signal, and detects and identifies registered family members; the first emotion recognition unit recognizes a first emotion track of the first family member according to the first feature vector; a second emotion recognition unit recognizes a second emotion track of the second family member according to the second feature vector; the emotional communication mode recognition unit recognizes emotional communication modes of the two family members according to the first emotional track and the second emotional track. The method is applied to the technical field of man-machine interaction.

The present invention discloses a generalizable classification method, system and device based on domain-invariant category features, belonging to the field of computer vision processing. The method of the present invention first collects images from multiple environments for a classification task of a preset category to construct a source domain image data set, and after sampling and preprocessing the source domain image data set, constructs multiple batch sample sets for training, each preprocessed image corresponds to a real category label, and then trains an image classification model on the batch sample set, and finally inputs the image to be classified into the trained image classification model, and outputs the classification result of the image category. The present invention can find stable features that are domain-invariant and category-specific from the input features, and predict categories based on this, which can effectively improve the generalization ability of the image classification model.

This invention discloses an intelligent planning method and system for spinal pedicle screw placement based on medical imaging. The method first acquires spinal medical images, target vertebral body segmentation results, and screw planning annotation data. Based on the target vertebral body segmentation results, the target vertebral region is extracted and divided into left and right pedicle sub-blocks along the left-right direction. The right pedicle sub-block is mirror-mapped, and its screw start and end coordinates are simultaneously transformed to mirror coordinates, thereby unifying the samples from both sides into a standard sample space with the same orientation. This reduces the interference of anatomical orientation differences on model training and improves sample utilization efficiency. Then, a three-dimensional deep learning model is trained using the standard sample set, simultaneously predicting screw start, end, and diameter-related parameters. The screw diameter is output using ordinal regression, and the predicted diameter value is solved through ordered thresholds and cumulative probabilities, making the prediction results more consistent with the ordered attributes of clinical specifications. For the case to be planned, the model output results are restored to the original coordinate system according to the corresponding relationship to obtain the placement path and diameter parameters of both pedicles. This invention can simultaneously improve the positioning accuracy of the placement path, directional consistency, and screw specification prediction stability, and is suitable for preoperative planning and assisted surgical systems for the spine.

This invention discloses a method for calculating and intelligently predicting longitudinal discontinuous deformation of shield tunnels considering segment misalignment and circumferential joint opening. The method includes: first, establishing a segmented beam mechanical model; then, combining the theory of segmented continuous beams and variable stiffness of circumferential joints to construct a longitudinal discontinuous deformation mechanical model for the tunnel; adding misalignment displacement and stiffness discontinuity conditions at the circumferential joint to characterize the deformation jump characteristics. By deriving an analytical solution for longitudinal deformation, the accumulated segment misalignment and circumferential joint opening can be accurately calculated. Subsequently, the theoretical model and monitoring data are integrated to construct a set of deformation characteristic parameters. Finally, using a temporal neural network combined with the analytical model as physical constraints, intelligent deformation prediction is performed to analyze the development trend of circumferential joint opening and misalignment, and to identify high-risk sections. This invention accurately reflects local stiffness abrupt changes and deformation discontinuities, with high prediction accuracy, and is suitable for tunnel design evaluation, long-term monitoring, and early warning of tunnel defects.

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 invention belongs to the technical field of 3D scene reconstruction, and particularly relates to a 3D scene reconstruction method and system based on illumination adaptive plane representation, and the method comprises the steps: completing scene multi-view image collection and equipment parameter recording, extracting multi-scale image features, carrying out the targeted enhancement processing of a weak light image, and integrating all image features to provide high-quality data support; based on the scene range of the enhanced image, initializing a multi-layer grid structure, predicting the plane height through stable calculation, quantifying the plane perception effectiveness and reliability and storing related data, and providing structured plane representation for subsequent reconstruction; calling early-stage reconstruction data and the integrity matrix to identify a defect area, constructing a state input tensor fusing plane key indexes, and screening out an optimal plane set for defect supplementation through a double-depth Q network; and extracting features and position data of the optimal plane, projecting the data to a 3D space, and completing semantic decoding in combination with scene prior.