15results about How to "Effective prediction" patented technology

This invention relates to the field of magnetic resonance imaging technology, specifically disclosing a method for modeling the radio frequency transfer function of active implantable medical devices. This method constructs a suitable neural network algorithm and, based on learning important features such as the geometric parameters of the active implantable medical device and the characteristics of the surrounding tissue, enables it to quickly and effectively predict the radio frequency transfer function of the active implantable medical device in homogeneous and heterogeneous tissue environments. This overcomes the limitations of traditional in vitro experimental methods, such as low efficiency, high experimental costs, and the inability to simulate homogeneous tissues, providing a new option for modeling the radio frequency transfer function of active implantable medical devices.

The invention relates to the technical field of animal breeding, in particular to an SNP (Single Nucleotide Polymorphism) molecular marker and application thereof in identifying pheasant laying number traits. On the basis of a genome version ASM 414374v1, the SNP molecular marker comprises any one or more of the following components: NW022205446.1: 5714958, NW022205446.1: 5715290, NW022205446.1: 5726623, and NW022205446.1: 5743232, and the SNP molecular marker comprises any one or more of the following components: NW022205446.1: 5743232. Four SNP molecular markers related to the pheasant laying number character are obtained through research and screening, and prediction and identification of the pheasant laying number can be achieved by detecting the polymorphism of the SNP molecular markers. The SNP molecular marker provided by the invention can be used for improving the laying number character of pheasants, and has important application value.

The application discloses a blood pressure data prediction method and device, electronic equipment and storage medium. The blood pressure data prediction method comprises the following steps: acquiring first PPG time sequence data corresponding to a photoplethysmography (PPG) signal in a first time period; acquiring first arterial blood pressure (ABP) time sequence data corresponding to an ABP signal in the first time period; acquiring second ABP time sequence data corresponding to an ABP signal in a second time period based on the first PPG time sequence data and the first ABP time sequence data and through a pre-trained blood pressure prediction model, wherein the blood pressure prediction model is obtained based on a transformer model, and the second time period is a next time period after the first time period. The method can effectively predict ABP time sequence data according to PPG time sequence data.

The present application relates to the field of oil and gas seismic exploration technology, more particularly, it relates to a high-precision velocity modeling method for deep water deep reservoirs, the present application is mainly aimed at the deep water deep reservoir depth prediction in the area with few wells, after the quality evaluation of the seismic data of the target area, the present application optimizes part of the seismic data as the basic data for research, on the basis of the fine construction of the geological model, the selection of regional velocity marker layer, well-seismic calibration, velocity main control factor analysis and seismic velocity body optimization, the high-precision velocity modeling fine research is carried out, so as to realize the accurate prediction of the reservoir depth in the deep water deep area with few wells or no well area. The present application strictly improves the fine degree of each step of modeling, the key is to add the change of reservoir physical properties as the main control factor of regional velocity into the seismic velocity body optimization work, compared with the original seismic velocity body, more velocity details matched with the logging data are added, which is beneficial to improve the prediction accuracy of the final velocity model.

The utility model relates to the technical field of monitoring devices, in particular to a pest and disease prediction and early warning monitoring device. According to the technical scheme, the device comprises a box body, a guide through groove is formed in one side of the box body, a set of sliding rails are fixedly arranged in the guide through groove, and a pasting assembly used for pasting diseases and pests is slidably arranged between the sliding rails; the air collecting hopper is arranged on the bottom face of the box body in a communicating mode, a supporting frame is fixedly arranged on the inner wall of the air collecting hopper, and a trap lamp is fixedly connected to the supporting frame in a sleeving mode; the supporting frame penetrates through the upper surface of the box body, a support is fixedly arranged on the inner wall of the supporting frame, a motor is fixedly arranged on one side of the support, and fan blades are fixedly arranged at the output end of the motor; a monitoring assembly used for monitoring the pasting assembly is fixedly arranged on one side of the box body. When the red leaf sorghum is not substantially gnawed and damaged by diseases and pests, the disease and pest damage can be found in time, so that the damage degree of the sorghum in the growth process is effectively reduced, and the yield and the quality of the sorghum are guaranteed.

This invention provides a method and system for assessing the adaptability of a satellite to the mechanical environment during long-distance transportation after propellant loading, belonging to the field of spacecraft mechanical environment engineering technology. This method aims to solve the challenge of assessing the structural safety and leakage risk of propellant-loaded satellites during long-distance transportation under the coupling of static pressure stress within the propellant tank and dynamic stress during transport. The method includes: establishing a model of the satellite and packaging system; defining the transportation mechanical environment conditions; analyzing and calculating random vibration fatigue strength and impact strength, predicting propellant leakage rate, and conducting a comprehensive assessment. This method employs the mean stress correction theory and the quasi-static transformation method to solve the coupling problem of static and dynamic stresses in fatigue analysis and impact strength verification, respectively, and predicts leakage risk by comparing acceleration response with ground test data. This invention can comprehensively assess the safety of propellant-loaded satellite transportation and supports the optimization of spacecraft development processes.

This invention presents a method for predicting small-sample turbine blade damage parameters based on meta-learning, belonging to the field of turbine blade fatigue life assessment and prediction. Combining the load characteristics of typical positions on the turbine blade during service, the method treats each typical position at different cross-sectional heights as different service tasks. A meta-learning model is employed to effectively predict damage parameters at various positions on the turbine blade under different service times, improving blade utilization and reducing operating costs. Addressing the issue that turbine blade service data exhibits typical temporal correlation but has excessively short time series, an LSTM network is used as the base model. Complete time series samples from each typical position are packaged into a "pseudo-sample" for model training. This method utilizes meta-learning to solve the small-sample prediction problem while leveraging the temporal correlation of the samples to improve the model's prediction accuracy. This invention is applicable to the field of turbine blade fatigue life assessment and prediction, providing technical support for the reasonable scrapping of aero-engine turbine blades.

Provided are a method and system for generating a screening model and screening a high-risk infection population of an infectious disease. The method for generating a screening model of a high-risk infection population of an infectious disease comprises: obtaining a training data set, wherein the training data set comprises user trajectory information, wherein the user trajectory information is obtained based on mobile terminal related data of a user; establishing a sample table, wherein each sample in the sample table comprises a user identifier and a sample label, and the sample label indicates that the user is a positive sample user who has been diagnosed as infected or suspected to be infected with a specified type of infectious disease or a negative sample user who is normal; based on the training data set, extracting features for each sample in the sample table, and incorporating the extracted features into the sample table; using a machine learning algorithm, performing machine learning model training based on the sample table incorporating the features, and generating a high-risk infection population screening model for the specified type of infectious disease.

The application discloses a patient motion function rehabilitation prediction method and system, a terminal and a storage medium. The method comprises the following steps: acquiring an electroencephalogram signal and an electromyogram signal of a patient, preprocessing the electroencephalogram signal and the electromyogram signal to obtain a target electroencephalogram signal and a target electromyogram signal, analyzing the target electroencephalogram signal and the target electromyogram signal to obtain a brain-muscle coupling result, and extracting features from the target electroencephalogram signal and the target electromyogram signal to obtain a first time-frequency feature and a second time-frequency feature; inputting the brain-muscle coupling result, the first time-frequency feature and the second time-frequency feature into a trained rehabilitation evaluation prediction model, and outputting a target motion function rehabilitation evaluation prediction result. The application proposes a method for modeling the relationship between the electroencephalogram signal and the electromyogram signal of a stroke patient and the human motion function of the patient in a long time sequence, thereby effectively evaluating and predicting the evolution of the human motion function rehabilitation of the patient in the time sequence, and improving the accuracy of the human motion function prediction.

This application relates to the field of otolaryngology technology, specifically a method and system for predicting the risk of papillary thyroid carcinoma. By combining multiple sets of clinical indicators and multiple imaging feature data, this application can effectively assess and predict the risk of papillary thyroid carcinoma. The scoring data provides a more intuitive determination of the severity of papillary thyroid carcinoma, thereby enabling the differentiation between atypical subacute thyroiditis and papillary thyroid carcinoma. Compared to existing technologies, the discrimination results of the embodiments in this application are more accurate.

The embodiment of the present application relates to the technical field of communication, and discloses a kind of interference prediction methods, the method comprises: collection space-time interference data set, the space-time interference data set includes time information, spatial information, network side information, external interference information;The space-time interference data set is input into interference prediction model, and node feature matrix and inter-node correlation matrix are obtained;The node feature matrix is used to represent the characteristics of the spatial information and the network side information;The inter-node correlation matrix is used to represent the interference characteristics of the external interference information on the network side information;According to the node feature matrix and the inter-node correlation matrix, determine the target node and the corresponding target interference source node that exist interference.Through the above mode, the embodiment of the present application realizes the effective prediction of interference.

The application discloses an olfactory perception prediction method based on path enhancement and olfactory relationship fusion. The steps include determining a molecular graph input representation based on a molecular graph of a target molecule, modeling a multi-layer self-attention of the molecular graph input representation to obtain node-level structure features; wherein an inter-atomic distance bucket bias, a molecular fragment bias, and a fusion atomic pair path structure feature are introduced in the modeling; further, a label-specific molecular representation is obtained by performing label condition attention aggregation on the node-level structure features with an olfactory attribute label as a condition; finally, a prediction score is calculated based on the label-specific molecular representation and the olfactory attribute label representation, and an olfactory perception prediction result of the target molecule is output. The application can simultaneously utilize molecular structure information, path topological semantics and the correlation between olfactory attributes in a multi-label olfactory prediction task, and effectively predict the olfactory perception attributes of molecules.

The application discloses a data-driven runner blade surface static stress neural network prediction method, which comprises the following steps: establishing a full-flow passage model of a Francis turbine unit, performing grid division on the model, importing the grid corresponding to a working condition point into a numerical simulation software, and obtaining runner blade surface pressure data; establishing a water turbine unit shaft system model, performing non-structural grid division on the model, inputting the runner blade surface pressure data into an interface between a fluid domain and a solid domain, obtaining runner blade surface static stress data and corresponding three-dimensional coordinates, and dividing the static stress data into a training set and a test set; training a prediction model by using the three-dimensional coordinates, water head and opening degree corresponding to the training set, inputting the three-dimensional coordinates, water head and opening degree of the working condition point into the trained prediction model, and obtaining runner blade surface static stress under the working condition point. The application can correctly reflect the static stress distribution trend of the blade under all working conditions and has high precision.

The application discloses a kind of based on digital twinning numerical control machine tool machining precision intelligent control method.The present application includes: firstly, the main shaft digital twin of integrated digital three-dimensional model, temperature information remodeling and incremental prediction function is established;Then, in the hot machine stage, temperature, displacement sensor is arranged, the main shaft temperature information is remodeled using digital twin, the synchronous measuring point that satisfies preset thermal error condition is determined and the temperature change thereof is predicted;Temperature information remodeling and incremental prediction process are repeated, and when the temperature of synchronous measuring point reaches the processing permission threshold, processing instruction is given, the linear regression model of synchronous measuring point temperature and thermal error is established and packaged in digital twin for thermal error prediction;Finally, into processing stage, synchronous measuring point temperature prediction information is integrated into main shaft thermal error prediction model, and main shaft thermal error is predicted in real time and compensated in time.The present application provides theoretical guidance for the hot machine process of machine tool, and controls the machining precision of machine tool under the condition that sensor cannot be arranged in the machining process of numerical control machine tool.

The application provides a cervical cancer brachytherapy segmentation mode prediction method based on a graph neural network. The method comprises the following steps: obtaining a cervical cancer data set; the cervical cancer data set is an electronic medical record containing the clinical treatment information of each cervical cancer patient; preprocessing the cervical cancer data set; inputting the preprocessed cervical cancer data set into a GPT2 model for data generation to generate synthetic data; evaluating the quality of the synthetic data and selecting the synthetic data with the highest similarity to the preprocessed cervical cancer data set; selecting the synthetic data with the highest similarity to the preprocessed cervical cancer data set as the basis data, using a dynamic feature aggregation graph neural network to capture the treatment feature relationship of the cervical cancer patient, and predicting the corresponding treatment scheme. The application uses synthetic data generated based on the GPT2 model to solve the problem of insufficient medical data, and uses the method of dynamically capturing the relationship between patient features by the graph neural network to more accurately and effectively predict the treatment scheme of the patient.