264 results about "Early prediction" patented technology

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

The invention relates to the field of intelligent control, and particularly discloses a shadowless lamp control method based on behavior recognition and prediction, which comprises the following steps: S1, establishing a three-dimensional rectangular coordinate system by taking an initial mounting position of a shadowless lamp as an original point, and determining coordinates as follows by adopting image data acquired by at least two groups of directional image acquisition equipment with different visual angles; s2, capturing the real-time position of the scalpel in real time through an image acquisition device, outputting coordinates, acquiring data of a plurality of continuous durations to form a historical trajectory data set, calling an operation scene template library preset with a plurality of types of typical operation trajectory features by the control terminal for the acquisition times, matching a trajectory feature template corresponding to the current operation type, and outputting the historical trajectory data set; performing feature alignment processing on the data and the input data by a fusion module to obtain adaptive trajectory data; according to the technical scheme, pre-judgment can be carried out in advance, the scene adaptability is high, and sterile fine adjustment is facilitated.

The invention discloses an edge cloud collaborative industrial equipment health management and predictive maintenance method, and the method comprises the steps: forming a closed-loop system through four deep coupling steps: edge adaptive fusion perception, cloud knowledge enhancement reasoning, edge cloud collaborative self-evolution prediction, and risk-driven maintenance decision. The edge end dynamically adjusts a sensor acquisition and feature fusion strategy according to the equipment health state, the cloud end performs causal reasoning and situation evaluation by using a physical-data mixed knowledge graph, and the edge cloud collaboratively optimizes a prediction model through federal element learning and bidirectional knowledge distillation; maintenance decision is based on multi-objective optimization, the actual effect is fed back to the perception and prediction link, the method achieves accurate assessment of the equipment health state, early fault prediction and maintenance intelligent decision, the availability of the equipment is remarkably improved, the maintenance cost is reduced, and core technical support is provided for intelligent manufacturing.

The invention provides a screening method and application of an early prediction marker of papillary thyroid cancer, the early prediction marker of papillary thyroid cancer is P4HA2, application of a reagent for detecting the expression level of P4HA2 in preparation of a product is provided, and a P4HA2 inhibitor including a substance for knocking down P4HA2 gene expression is also provided. The invention also provides application of the P4HA2 inhibitor in preparation of a product for inhibiting migration and / or proliferation of thyroid cancer cells, and a screening method of a biomarker for predicting papillary thyroid cancer. The marker can be used for prediction and early prediction of papillary thyroid cancer, knock-down of the marker can also be used for prediction and early prediction of papillary thyroid cancer, early screening can be better completed, benign and malignant nodules are helped to be distinguished, unnecessary invasive detection is reduced, and the detection efficiency is improved. And a new view angle is provided for molecular mechanism research and early diagnosis of thyroid cancer.

The invention discloses a pre-eclampsia prediction method and system based on machine learning and early pregnancy indexes. The pre-eclampsia prediction method comprises the following steps: acquiring early pregnancy laboratory index data and FMF model evaluation parameters of a to-be-predicted pregnant woman; based on a preset preeclampsia type, performing feature screening on the early pregnancy laboratory index data by using a Boruta algorithm to construct corresponding original features; inputting the original features into a trained first-level machine learning prediction model corresponding to the pre-eclampsia type to obtain a risk score; according to the risk score and an FMF model evaluation parameter, obtaining a joint feature; and inputting the joint features into a trained second-stage machine learning prediction model corresponding to the pre-eclampsia type to obtain a corresponding pre-eclampsia risk prediction result. According to the method, information of different sources and different physiological dimensions is deeply fused, analyzed and judged, and high-precision and low-cost early prediction of different pre-eclampsia types is realized.

The invention discloses a power load prediction and energy storage optimization control method and system, belongs to the technical field of power system energy storage optimization control, and aims to solve the problems that the reaction is slow, the charging and discharging mode is not optimized enough and the risk of countercurrent cannot be pre-judged in advance due to the fact that reverse power transmission to a power grid is prevented through detection of an anti-countercurrent electric meter in an existing anti-countercurrent method. Based on historical load data, environmental data and time features, a time sequence deep learning model fused with an attention mechanism is utilized to output a future multi-time-scale user net load prediction result in a rolling manner, and whether a grid-connected point power prediction value is smaller than a dynamic countercurrent threshold value or not is judged so as to identify a countercurrent risk. And if the risk exists, establishing a countercurrent avoidance optimization model, otherwise, establishing a conventional scheduling optimization model, performing rolling solution by adopting an efficient solution algorithm to obtain an optimal charging and discharging power instruction sequence, performing electric energy quality evaluation and correction, and then issuing the optimal charging and discharging power instruction sequence to the energy storage converter for execution, thereby realizing pre-judgment of the countercurrent risk in advance and optimization of energy storage charging and discharging behaviors.

The invention discloses a management decision support method for intelligent operation and maintenance and fault prediction of engineering equipment, and belongs to the technical field of operation and maintenance management and intelligent decision of the engineering equipment. According to the method, equipment fault features are extracted and classified through a deep belief network (DBN), and text diagnosis is refined in combination with TF-IDF and cosine similarity; analyzing the importance and cause of the fault by using a Bayesian network; predicting a fault and a decline trajectory based on the decision tree and logistic regression; and constructing an intelligent operation and maintenance decision support system of ontology integration. According to the method, multi-source data are integrated, accurate fault diagnosis, advanced prediction and intelligent decision making are achieved, the problems that traditional operation and maintenance depend on experience, precision is low and cost is high are solved, and the operation and maintenance efficiency and reliability of engineering equipment are improved.

The invention relates to the technical field of automobile safety, and provides an electric tail gate anti-pinch control method and system, which introduces a prediction + self-adaption intelligent decision normal form, on one hand, the motion information of a tail gate is obtained, and the time sequence data of an obstacle is combined to be input into an LSTM model to predict the track of the obstacle; a collision prediction conclusion is output to be matched with a graded speed reduction strategy to execute tail gate control, and through prediction in advance, the system response efficiency is effectively improved, and the clamping damage probability is reduced; and on the other hand, when a contact signal of the tail gate is detected, real-time contact force and real-time vehicle working condition information are obtained in real time, a dynamic anti-pinch threshold value is calculated and compared with the real-time contact force for analysis so as to execute tail gate control, the dynamic anti-pinch threshold value is dynamically adjusted according to the vehicle working condition and the tail gate state, scene adaptation is carried out, the anti-pinch recognition accuracy can be improved, and the safety of the vehicle is improved. The false triggering probability is reduced; therefore, the electric tail gate anti-pinch system which is faster in response, more intelligent and more reliable is obtained.

Methods and systems including computer programs encoded on computer storage media, for a method for detecting signals related to subjects participating in a clinical trial. In some implementations, a computer collects clinical data from multiple sources. The computer standardizes and redacts personally identifiable information and determines predictive features that correspond to characteristics of the data that correlate with efficacy and safety signals. The computer obtains training data and trains machine learning models to predict one or both of a predicted efficacy signal and a predicted safety signal for a subject. The computer receives clinical data for a particular clinical trial subject enrolled in an ongoing clinical trial and predicts one or more signals and receives a review of the signals through a user interface. The computer updates the predictive components of the system based on the review.

The invention discloses an intelligent control method for a tailing filling process based on data driving, which relates to the technical field of automatic control, and comprises the following steps: collecting multi-source working condition data, and constructing a working condition feature vector after fusion denoising and normalization; splicing the working condition feature vectors in a preset time window, training a three-day age intensity prediction model and a pipe blockage risk scoring model, and generating a filling evolution state vector; clustering the filling evolution state vectors, calculating a state center, constructing a safety operation window according to a state category, and calculating a safety margin; constructing a stability margin index under the constraint of a safe operation window, and solving a stability control quantity through rolling optimization; and forming an incremental sample set through the latest operation data, and performing unified incremental updating on the prediction model and the safety operation window. According to the method, the filling evolution state vector and data-driven three-day-age intensity prediction model and the pipe blocking risk scoring model are constructed, so that the fine quantification and the advanced pre-judgment of the tailing filling working condition and the key safety performance are realized.

The invention relates to the technical field of medical health risk assessment, and particularly discloses a coronary heart disease risk assessment method fusing a tongue diagnosis image and structured data, and the method comprises the steps: collecting the tongue image of a patient with coronary heart disease and the structured data such as age and gender; and extracting tongue image features by using a convolutional neural network, encoding the structured data through a multi-layer perceptron, and fusing the encoded structured data with the structured data. And a CNNGPT2 deep learning model and a CNNTabular deep learning model are constructed. After training, a test set prediction result is extracted to construct a metadata set, XGBoost is used as a meta classifier for training and prediction, a coronary heart disease early prediction model is constructed, and coronary heart disease risk assessment is performed based on the early prediction model. According to the method, the tongue diagnosis image and the structured data are fused, the limitation of a single data type is made up, and the comprehensiveness of evaluation is improved. According to the method, two deep learning models of CNNGPT2 and CNNTabular are constructed, association information between data is fully mined, and the processing capability of complex data is improved.

Techniques for early prediction of medical billing classification codes and associated medical billing costs using routine clinical text are disclosed. The system predicts the medical billing codes within defined hours of admission by generating vector embeddings from a set of medical notation data, bypassing the need for post-discharge medical codes. Using a novel segmentation technique, the system processes lengthy medical notation data by dividing them into smaller subsequences. These subsequences are input to a large language model (LLM) to generate a plurality of sets of probability values for a set of medical billing classifications. The system selects a particular predicted medical billing classification for the patient based on the sets of probability values. Additionally, the system estimates medical billing costs early in the admission process. The system ensures comprehensive context utilization from clinical notes, enabling hospitals to manage treatment expenses proactively and improve operational efficiency.

The invention relates to the technical field of switch cabinet state monitoring, in particular to an intelligent switch cabinet anti-explosion abnormity monitoring method and system, and the system comprises a data collection module, a data storage and management module, a correlation analysis module, a time sequence prediction module, a risk assessment and early warning module, a communication interface module and a man-machine interaction module. According to the invention, by setting a multi-source sensing and intelligent analysis integrated monitoring architecture, early prediction and accurate diagnosis of the fire blast risk of the switch cabinet are realized, the contribution weight of each inducement parameter to a fault result is quantified through a dynamic correlation analysis algorithm, and a time sequence prediction model is introduced to establish an advanced early warning mechanism of parameter change, so that the early warning of the fire blast risk of the switch cabinet is realized. According to the method, when direct fault indexes such as temperature and gas concentration do not exceed the standard, a potential fault development path can be recognized in advance by analyzing the change trend of incentive parameters such as current and voltage, so that an accident handling mode is converted from post-remedy to pre-prevention, and the active safety guarantee capability of operation of the switch cabinet is improved.

The invention discloses a power transmission line bird damage prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: analyzing multi-source heterogeneous data based on a risk prediction model to obtain a target risk probability value of bird damage in a power transmission line area; the risk prediction model comprises a first model, a second model, a third model and a fourth model; the first model is used for predicting bird damage risks based on the meteorological data and the biological characteristic data; the second model is used for predicting bird damage risks based on the power transmission line data and the geographical environment data; the third model is used for predicting bird damage risks based on meteorological data; the fourth model is used for predicting bird damage risks based on bird damage defect data; and based on the target risk probability value and a preset risk grade range, predicting a bird damage risk grade of the power transmission line area, and carrying out risk early warning based on the bird damage risk grade. According to the invention, the problems of low risk assessment accuracy and insufficient early warning timeliness are solved, and early prediction and active early warning of bird damage risks are realized.

The invention relates to the technical field of liver disease prediction, in particular to a metabolism-related fatty liver disease intelligent prediction method and system and a storage medium. The method comprises the following steps: collecting multi-source data, respectively obtaining basic demographic information, laboratory indexes and prediction indexes, and extracting quantitative and qualitative tongue picture parameters; performing variable screening on the tongue picture parameters and the clinical indexes, and determining key prediction variables; obtaining a key variable value according to the key prediction variable, obtaining a prediction result of the occurrence risk of the metabolism-related fatty liver disease, and outputting the prediction result; the system comprises a multi-source data acquisition module, a variable screening module and a prediction result acquisition module. By means of the mode, the intelligent tongue picture parameters and the clinical indexes are fused, and the effect of early prediction of the metabolism-related fatty liver diseases is achieved.

The invention discloses a gestational diabetes mellitus early prediction method, a gestational diabetes mellitus early prediction system and a computer readable medium, and the method comprises the steps: obtaining pregnant woman sample data used for researching GDM early prediction, the pregnant woman sample data for researching the early prediction of the GDM comprises a plurality of basic clinical characteristics of each pregnant woman before 16 weeks of pregnancy, a plurality of biomarkers related to the pathogenesis of the GDM before 16 weeks of pregnancy, and a diagnosis result whether the biomarkers are GDM or not; screening a plurality of basic clinical features and a plurality of biomarkers in the obtained pregnant woman sample data to obtain basic clinical features and biomarkers with the highest GDM prediction value; and performing GDM prediction on the pregnant woman which is insufficient for 16 weeks of pregnancy by using the basic clinical features and the biomarkers with the highest GDM prediction value, so as to obtain a prediction result that GDM appears or does not appear in 24-28 weeks of pregnancy. According to the method, the GDM high-risk crowd can be predicted in the early stage of pregnancy, the GDM high-risk crowd is intervened, and the occurrence risk of bad outcomes is reduced.

The invention provides a schizophrenia early recognition and risk prediction method and system based on multi-mode speech analysis, and belongs to the technical field of medical artificial intelligence. The method comprises the following steps: prospectively acquiring natural voice data of an individual in a clinical high-risk period, performing clinical follow-up visit for two years, and constructing a first Chinese schizophrenia clinical high-risk voice longitudinal data set; based on the data set, audio and text features in the voice are extracted, a layered multi-mode integrated model is constructed, semantic coherence and acoustic features are fused, and early prediction of the schizophrenia transformation risk is achieved; through feature importance analysis, key voice marks closely related to negative symptoms, thinking disorder and cognitive impairment are recognized, and non-invasive, accurate and interpretable early recognition is achieved. According to the method, the technical blank of early screening tools for schizophrenia in Chinese context is filled, and the accuracy and stability of early recognition are remarkably improved.

The invention relates to the technical field of grouting control, in particular to an intelligent grouting control method and system.The intelligent grouting control method comprises the steps that multi-parameter time sequence data in the grouting process is collected in real time, and standardization processing is conducted to construct feature vectors; inputting the feature vectors into an integrated learning model, and synchronously executing grouting effect prediction based on time sequence analysis and abnormal working condition identification based on data distribution analysis; when an abnormal working condition is recognized, a control strategy corresponding to the abnormity is executed preferentially; when abnormity is not recognized, the control parameters are dynamically optimized according to the predicted grouting effect; issuing the control strategy or the optimization control parameter to grouting equipment for execution, and collecting feedback data after execution; on the basis of feedback data, online incremental learning is carried out on the integrated learning model, model parameters are adaptively optimized, prediction can be carried out in advance, collected data and final control form a closed loop, meanwhile, the method can also adapt to complex working conditions, and the intelligent grouting control efficiency is improved.

The invention relates to the technical field of medicine, in particular to an early evaluation method for endocrine remission of pituitary growth hormone adenoma based on low Knosp grading, which comprises the following steps: acquiring preoperative and postoperative image data and preoperative and postoperative clinical data of a patient with low Knosp grading pituitary growth hormone adenoma; extracting imaging features used for early evaluation of postoperative endocrine remission from the preoperative and postoperative image data; extracting clinical features for early evaluation of postoperative endocrine remission from the preoperative and postoperative clinical data; and performing statistical analysis on the iconography characteristics and the clinical characteristics to obtain an early evaluation result of postoperative endocrine remission. According to the method, risk factors related to whether low-Knosp graded pituitary growth hormone adenoma postoperative endocrine remission can be relieved or not are screened out, then a prediction model is established through a statistical method, and then early prediction of low-Knosp graded pituitary growth hormone adenoma postoperative endocrine remission is achieved.

The invention discloses an early prediction model construction method for senile sarcopenia and metabolic high-risk phenotypes. The method comprises the steps that 3D scanning data, body composition data and health phenotype data of senile patients are acquired, and data preprocessing is carried out; respectively extracting feature vectors of the three types of data after data preprocessing, and performing alignment and weighted fusion to obtain fusion vectors; inputting the fusion vector into a subtype classification model for training to obtain a preliminary prediction model; in the training process of the subtype classification model, an unsupervised learning method and a supervised learning method are adopted at the same time, and a staged freezing strategy and a five-fold cross validation method are adopted; the preliminary prediction model is verified and finely adjusted by using a verification data set prepared in advance, so that a final early prediction model of senile sarcopenia and metabolic high-risk phenotypes is obtained, and accurate diagnosis of sarcopenia and intelligent identification of metabolic high-risk subtypes can be realized through the constructed prediction model.

A method for predicting early onset of mental health issues in work environments using artificial intelligence (AI) comprising: a) collecting data from two sources: (1) derived from employee survey an

The invention discloses a cross-battery few-sample early prediction method and system based on physical knowledge, and the method comprises the steps: obtaining the test data of a target battery through an accelerated aging experiment, and training a general life prediction model based on an iTransform architecture through a public data set; an improved dynamic time warping algorithm is provided to realize capacity ratio alignment of degradation tracks of the target battery and the reference battery, and a characteristic difference value sequence representing systematic deviation is extracted; further constructing a deviation prediction model of multi-task learning, and synchronously optimizing three sub-tasks of feature reconstruction, physical feature prediction and health state deviation estimation through an adaptive loss weight adjustment mechanism; and finally, carrying out personalized correction on the general prediction result through a double-model fusion strategy. According to the invention, the cross-battery degradation track prediction of the lithium titanate battery of the high-speed train is completed, the prediction accuracy and generalization are improved, and a reliable technical means is provided for the health management of the battery.

The invention provides an application of a pair of immune costimulatory factors TNFSF9 / TNFRSF9 as markers in preparation of a product for early prediction of preeclampsia, and the immune costimulatory factors comprise a tumor necrosis factor superfamily member 9 (TNFSF9) and a tumor necrosis factor receptor superfamily member 9 (TNFRSF9). The product is used for carrying out early warning on the preeclampsia occurrence risk 20 weeks before pregnancy. The product is used for detecting the immune costimulatory factor through at least one of serum, plasma, whole blood and placental tissue. The invention aims to improve the early prediction efficiency of preeclampsia by detecting the abnormal expression level of the pair of markers.

According to the helicopter thunder and lightning indirect effect simulation evaluation method provided by the invention, a simplified method of a complex aircraft model is provided, early prediction and protection design optimization of the thunder and lightning indirect effect of the aircraft are realized, the test cost is reduced, and the digital design and verification capability is improved.

The invention discloses an autism early prediction method and system based on multi-modal feature fusion and a bidirectional attention mechanism, and the method comprises the steps: carrying out the parallel extraction of an original video, carrying out the data enhancement through a segmentation recombination technology, and obtaining a training set, which comprises the enhanced behavior and physiological time sequence signals; double-flow deep feature learning is carried out on the training set by using a parallel flow frame, spatial-temporal features and time-frequency features are obtained, and the parallel flow frame comprises spatial-temporal feature extraction flow and time-frequency feature extraction flow; performing deep interaction and adaptive weighting on the spatial-temporal features and the time-frequency features by using a bidirectional cross attention mechanism to obtain fusion features; and inputting the fusion features into a classifier to obtain a final autism risk prediction probability. According to the method, the defects in the prior art are effectively overcome, and the accuracy and reliability of early prediction of autism are remarkably improved.

The application provides a crane health assessment method and system, the method comprising collecting data of a working state of the crane; performing health assessment based on the collected data and a health assessment model; obtaining quantitative data based on a result of the assessment and performing maintenance guidance based on the quantitative data. The application can monitor the use of the crane in real time, analyze and assess the state of main structural parts and key components of the crane, assess the health of the crane, provide quantitative data guidance for equipment maintenance, and realize early prediction of the state of the equipment by the production and maintenance departments, so as to arrange maintenance or component plans in a targeted manner.

The present invention discloses a high-frequency electrotherapy protection device and method. This device can directly sample data from the treatment circuit, providing direct data support for treatment efficacy. This allows for differentiated treatment of different treatment sites, enabling precise, quantified treatment of diseased areas, ensuring both treatment effectiveness and accuracy. It also enables early prediction of treatment mishaps, providing dual protection against potential safety hazards. The device comprises a high-frequency electric field therapy unit, a treatment energy monitoring unit, and a control unit.

The invention discloses a preeclampsia biomarker and an application of the preeclampsia biomarker. The preeclampsia biomarker comprises at least one of a fusion gene PSPC1-MRPS31P2 and a fusion gene LINC00630-AL035494. The preeclampsia biomarker can be used for detecting preeclampsia. The preeclampsia biomarker provided by the invention can provide a non-invasive diagnosis scheme for clinical prenatal preeclampsia, and preeclampsia specific high-expression fusion genes (PSPC1-MRPS31P2 and LINC00630-AL035494) in peripheral blood of a pregnant woman are detected through real-time quantitative PCR (Polymerase Chain Reaction), so that preeclampsia early prediction, disease diagnosis and monitoring of the state of a patient in the illness period are realized; and in combination with the existing clinical diagnosis scheme, the accuracy of pre-eclampsia diagnosis is improved.

A wireless transmit / receive unit (WTRU) may be configured to perform measurements and measurement predictions in an idle state or an inactive state (e.g., low-power operation mode). The WTRU may perform measurements for the configured idle measurement durations. If the WTRU stays in the idle state or the inactive state for more than a validity duration after it has stopped performing measurements, the WTRU may perform measurement predictions. Upon transitioning to a CONNECTED state (full-power operation mode), the WTRU may report the actual measurements, if they are still valid, or otherwise, report the predicted measurements.

The invention discloses a prediction model of a glomerular filtration rate after cardio-pulmonary resuscitation, a construction method and application thereof. The method comprises the following steps: collecting and preprocessing sample data; determining an independent variable, a dependent variable and an end variable; screening independent influence factors, and evaluating a relationship between the independent influence factors based on a variance expansion factor; constructing a column graph prediction model based on the independent influence factors; performing accuracy evaluation on the prediction model based on the accuracy and the mean absolute error; performing calibration degree analysis on the prediction model through a calibration curve, Bland-Altman consistency evaluation and pairing T test; evaluating the prediction efficiency of the prediction model through an ROC curve and an AUC; and verifying the universality and extrapolation of the prediction model. The early prediction model of the glomerular filtration rate after cardio-pulmonary resuscitation is constructed, so that the probability of chronic kidney diseases can be predicted within 24 hours, early discovery and early intervention are realized, and the disease progress is delayed.