12results about How to "Reduce modeling difficulty" patented technology

The application discloses a 3D printing ocular prosthesis platform, which is prepared through the following steps: S1, designing the shape of the ocular prosthesis platform and dividing the shape into regions; S2, selecting a porous structure for the ocular prosthesis platform in each region and determining the voxel size according to the connecting point parameters of the selected porous structure; S3, voxelizing and fitting each region to form a voxelized interface; S4, filling the porous structure selected in step S2 into each corresponding region to complete the modeling of the porous structure in each region; S5, connecting the connecting points on adjacent voxelized interfaces to each other and converting the connecting lines into solid rod models to complete the modeling of the interfaces; S6, merging the models obtained in steps S4 and S5 to obtain a final model of the ocular prosthesis platform; S7, 3D printing to obtain a bioceramic blank; and S8, high-temperature sintering of the blank to obtain a 3D printed ocular prosthesis platform after porcelain forming. The application can obtain a new 3D printed ocular prosthesis platform, which provides technical support for ocular prosthesis repair.

The application discloses a lithium ion battery internal short circuit fault simulation method, comprising the following steps: S1, establishing a lithium ion battery single particle lumped parameter estimation model, and estimating the dynamics and transfer attribute parameters of a lithium ion battery to be simulated by using a control equation; S2, constructing an electrochemical-thermal coupling 3D model and setting a control equation thereof; S3, substituting the dynamics and transfer attribute parameters of the simulated lithium ion battery into the constructed electrochemical-thermal coupling 3D model, simulating the electrothermal behavior of the lithium ion battery under different fault types based on the control equation, and realizing internal short circuit fault simulation. The application solves the defects of the existing fault simulation scheme in simulation accuracy, calculation rapidity and application universality, establishes an internal short circuit fault electrochemical-thermal coupling 3D model, and provides simulation data basis for an internal short circuit fault detection method.

The application discloses a heterogeneous energy collaborative prediction and scheduling method considering source-load low-carbon characteristics complementation and matching, relates to the technical field of power system source-load prediction and low-carbon scheduling, and comprises the following steps: based on Laguerre polynomials, pseudo-inverse learning and integrated learning, robust local mean decomposition (RLMD) and weighted permutation entropy (WPE), a wind power (WP), photovoltaic power (PVP) and power load (PL) prediction model is constructed, which takes into account the prediction accuracy and stability; a HECM mathematical model is constructed, the HECM mathematical model framework comprises an integrated flexible operation mode of carbon capture power plant (IFCCPP), a wind farm (WF), a photovoltaic power station (PP), a concentrated solar power (CSP), a load active demand response (LADR), a ladder carbon trading mechanism (LCTM), an objective function and related constraints; in the source-load prediction, the two targets of accuracy and stability are considered simultaneously, a compromise solution is selected in the Pareto frontier through the MORUN algorithm, and the wind power, photovoltaic power and power load prediction results are more suitable for the power system scheduling scene with high robustness requirements.

The invention belongs to the field of power system operation. A heating type electric load short-term prediction method based on data-knowledge fusion driving under demand response is characterized by comprising the steps that heating type electric load interaction response knowledge under demand response and other prediction influence factors serve as features, and a data-knowledge fusion driving prediction model is introduced for training; a heating type electric load prediction value which is high in prediction and close to the actual power utilization level is obtained; according to the data-knowledge fusion driving model, heat supply type electric load demand response signals, user response rate, uncertainty and other knowledge, other common prediction influence factors and the like serve as data driving model input characteristics, a data-knowledge fusion driving prediction model is established, and model training is conducted under knowledge and data dual driving. According to the method, the prediction precision can be improved, the prediction result better fits the actual power consumption level, the modeling difficulty is reduced, and the generalization and interpretability of the prediction model are improved.

The application relates to a CAE-based light-emitting grid mist simulation method, which comprises the following steps: constructing a light-emitting grid product model based on three-dimensional data of the light-emitting grid and defining an analysis model; defining material parameters of parts of the light-emitting grid product model; defining outer wall surface simulation parameters of the light-emitting grid product model; defining the heat power of electronic components of the light-emitting grid product model; completing local mesh encryption of each part of the light-emitting grid product model; defining the inner surface of a mask of the light-emitting grid product model as a monitoring target, and defining a physical quantity as film thickness; setting solving parameters and performing calculation to obtain the film thickness of the inner surface of the mask of the light-emitting grid product model. The application can guide the internal structure design of the light-emitting grid and the layout of air holes on the shell.

This application relates to the field of medical image processing technology, and discloses a method, system, device, and medium for predicting tumor status, including: acquiring a set of CT images of the lungs of a target object during the current respiratory cycle; constructing a three-dimensional tumor model of the lung tumor based on the CT image set; the lung tumor has a real volume and real centroid coordinates; performing finite element analysis to obtain a finite element tumor model, calculating the simulated volume and simulated centroid coordinates of the lung tumor; comparing and obtaining the volume retention ratio and centroid offset; predicting the motion state of the lung tumor in the next respiratory cycle to obtain the initial motion state parameters of the lung tumor in the next respiratory cycle; if the motion state prediction quality of the lung tumor meets the preset motion state prediction quality based on the volume retention ratio and centroid offset, then the initial motion state parameters are used as the target motion state parameters to predict the motion state of the lung tumor, thereby improving the accuracy and correctness of radiotherapy.

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.

The application discloses a CRTSII type track slab upwarp stability discrimination method, which comprises the following steps: establishing a two-dimensional numerical calculation model of the CRTSII type track slab; combining different adverse influence factors to form a calculation condition, and calculating the track slab allowable temperature amplitude under the upwarp allowable vector condition of the calculation condition by using the two-dimensional numerical calculation model; establishing a sample database according to the calculation condition and the track slab allowable temperature amplitude, constructing a regression model between the adverse influence factors and the slab body allowable temperature amplitude by using a regression analysis method, and improving the regression model; and judging the CRTSII type track slab upwarp stability according to the improved regression model, the measured value of the adverse influence factors and the actual maximum temperature amplitude of the track slab. The application accurately simulates the adverse influence factors of the CRTSII type track slab, establishes a representative sample database, and improves the accuracy and efficiency of the CRTSII type track slab upwarp stability discrimination through the improved regression model.

A computational method for investigating the gas compression and temperature rise phenomenon in the main steam pipe of a nuclear reactor is proposed. This method is applicable to simulating the abnormal temperature and pressure rise in the secondary steam pipe of the steam generator caused by the loss of secondary feedwater and the closure of the main steam isolation valve in a full-pressure small reactor. The method mainly includes the following parts: 1. Simplifying the geometry of the secondary steam main pipe and constructing a 3D model; 2. Using SpaceClaim software to perform 3D modeling of the simplified steam main pipe structure; 3. Using the mesh function of Fluent software to generate a computational mesh for the model; 4. Establishing suitable compressible flow and turbulence models; 5. Defining boundary conditions and initial conditions; 6. Setting the solver and solution method, and setting the time and spatial discretization formats; 7. Adjusting the sub-relaxation factor and setting the time step that conforms to the computational time scale; 8. Setting the convergence criterion and monitoring physical quantities such as cross-sectional temperature and inlet pressure until the pressurization process in the 3D model ends and the container reaches a steady state.

This invention discloses a composite disturbance rejection control method and system for permanent magnet synchronous motors (PMSMs) based on recurrent neural network disturbance estimation and prediction. It constructs a dynamic model of the PMSM considering parameter uncertainties, slowly varying load disturbances, and impulsive load disturbances. A recurrent neural network with a self-feedback structure for historical disturbance estimation is designed. This network learns the dynamic characteristics of disturbances online through a recursive structure and estimates the current disturbance in real time. Simultaneously, it predicts the disturbance at the next moment while keeping the network weights constant, thus compensating for the computation and execution delays of the control system. A fixed-time non-smooth robust controller is designed, using the residual error after prediction compensation as input to rapidly suppress sudden impact disturbances. The predicted disturbance is introduced into the controller as feedforward compensation, constructing a predictive-feedback cooperative composite control law to achieve hierarchical suppression of multi-source disturbances. This invention improves the dynamic response performance, disturbance rejection capability, and control stability of PMSMs in complex operating environments.

The invention discloses an SRM harmonic closed-loop torque ripple suppression method based on current square. The method comprises the following steps: (1) constructing an electromagnetic torque expression containing current square, and solving a current square harmonic model with optimal torque ripple; (2) obtaining a current square model with an optimal current square direct current bias value and an optimal torque ripple; (3) generating a fundamental wave amplitude reference value of the current square model with the optimal torque ripple, and obtaining a current square excitation model in an actual injection motor and an actual current square model of an output motor; (4) in order to realize closed-loop suppression of the torque ripple, the auxiliary channel executes a correction process; and (5) compensating an instantaneous torque peak appearing in a specific electrical angle range, completing harmonic closed-loop torque ripple suppression, and realizing an optimal torque ripple scheme. According to the invention, a double-layer compensation architecture based on current square harmonic injection and torque real-time feedback is constructed, so that the systematic suppression of the torque ripple of the switched reluctance motor is realized.

The invention discloses a non-low-conductivity contra-rotating gas turbine pneumatic design method, which comprises the following steps of: giving value ranges and selection principles of parameters such as a high-low pressure turbine power ratio, a high-low pressure rotating speed ratio, a high-pressure turbine outlet airflow angle and an AN2 value through multi-round iteration of three parts of a gas compressor, a combustion chamber and a turbine and the overall performance of an engine; sufficient pre-rotation is provided for the low-pressure turbine moving blades through high-pressure turbine outlet airflow, the aerodynamic performance of the turbine is improved, meanwhile, the blade modeling difficulty of the high-pressure and low-pressure turbine moving blades and the performance matching debugging difficulty of a high-pressure rotor and a low-pressure rotor are reduced, the engineering practicability of aerodynamic design of the non-low-guide contra-rotating gas turbine and a downstream integrated transition section is guaranteed, and the low-guide contra-rotating gas turbine is improved. And the overall performance requirement of the engine and the function matching requirement of the high-low pressure rotor are met. The invention further discloses the non-low-conductance contra-rotating gas turbine adopting the non-low-conductance contra-rotating gas turbine pneumatic design method.