378 results about "Early prediction" patented technology

The invention discloses a building crack early prediction and restoration method based on a machine learning algorithm, and the method comprises the following steps: collecting stress, displacement, temperature and image data through a self-adaptive sensor network, and enabling a sensor to dynamically adjust the collection frequency and position; preprocessing the collected multi-source data, including de-noising, data completion and image enhancement, and extracting crack features; performing cross-scale multi-dimensional feature extraction based on the preprocessed data, and generating high-dimensional features for crack prediction; inputting the high-dimensional features into a fusion quantum Bose-Einstein condensation model and a long-short term memory network model, and performing dynamic space-time prediction of the crack; according to the prediction result, generating a repair suggestion by using an intelligent optimization algorithm; after the repair plan is implemented, the repair effect is continuously monitored through the sensor network; the sensor network configuration is adjusted in real time, and the monitoring and prediction accuracy is improved. According to the invention, early prediction and effective repair of the building crack are realized.

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.

A battery life early prediction method based on pre-classification and LSTM-CNN comprises the following steps: extracting features by using capacity difference data in a discharge process, determining a classification interval of a sample through an unsupervised learning clustering method, dividing the sample into a long life group and a short life group according to the actual life distribution condition of the battery, according to the method, a brand-new superposition model of LSTM and CNN networks suitable for battery sample classification and life prediction is constructed and trained, new samples are classified through an LSTM-CNN classification model, different life prediction models are matched, and then the life of the battery is predicted through the LSTM-CNN prediction model, so that early prediction of the life of the battery by using a small amount of data is realized, and the prediction efficiency of the life of the battery is improved. The model structure is simple, and the prediction accuracy and efficiency 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 relates to the technical field of corrugated pipe detection, and discloses a corrugated pipe defect rapid detection method and device.The corrugated pipe defect rapid detection method comprises the steps that physical state information of a corrugated pipe is obtained, and physical parameters of the corrugated pipe are collected at different time frequencies through a multi-parameter sensing network; a corrugated pipe multi-time scale physical parameter data matrix is generated; the corrugated pipe multi-time-scale physical parameter data matrix is converted into unified tensor representation, a multi-scale tensor set is generated through wavelet transform, and correlation strength between different scales is calculated; according to the method, a brand new technical path is provided for the field of corrugated pipe defect detection through the space-time physical multi-scale mapping technology, advanced prediction and accurate diagnosis of corrugated pipe defects are achieved by establishing the mapping relation from microscopic physical changes to macroscopic defect expressions, effective technical guarantee is provided for safe operation of corrugated pipes, and the method is suitable for popularization and application. And a remarkable economic value is created.

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 liver blood flow and anesthesia depth combined monitoring system in a liver operation. The liver blood flow and anesthesia depth combined monitoring system comprises seven modules including a multi-source data collector, a signal preprocessor, a physiological coupling model calculator, an operation recognizer, a brain-liver information flow analyzer, a risk prediction early warning device and a clinical decision assist device. According to the system, liver blood flow parameters and anesthesia depth parameters are collected and analyzed in real time, a dynamic coupling relation model between the liver blood flow parameters and the anesthesia depth parameters is established, and in combination with automatic recognition of surgical operation and quantitative analysis of brain-liver information flow, early prediction and early warning of surgical risks are achieved, and individualized clinical decision support is provided.

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 an ICU sepsis two-stage early prediction method and device and a storage medium, and the method comprises the steps: continuously collecting a clinical data set after a patient enters an ICU, and carrying out the feature extraction, and obtaining a prediction feature vector; based on the predicted feature vector corresponding to the clinical data set at the current observation moment, determining whether the patient is a non-sepsis sample by using a preset classification model; and if the patient is not determined as a non-sepsis sample by the classification model, predicting whether the patient has a sepsis infection risk in a future preset time period or not by using a preset prediction model according to the current observation moment and the prediction feature vector corresponding to the clinical data set in the previous set time. According to the application, a two-stage mode is adopted, patients are subjected to layered screening, and samples which do not obviously belong to sepsis are excluded; and for the samples which are not excluded, more accurate sepsis risk prediction is carried out by using the prediction model in combination with the prediction feature vectors corresponding to the clinical data of the plurality of time periods, so that the prediction accuracy is improved.

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 provides an application of a T lymphatic activation related cytokine as a biomarker in prediction of children immune globulin non-reactive Kawasaki disease. The team finds that in patients of an IVIG sensitive group and an IVIG non-response group, differences of expression levels of IL-6 and IL-17 before and after treatment have statistical significance; meanwhile, ROC curve analysis shows that IL-6 and IL-17 serve as early warning signals, and the prediction efficiency of the IVIG-unreacted Kawasaki disease is remarkably superior to that of existing prediction factors. Particularly, the levels of IL-6 and IL-17 are re-evaluated after initial treatment, so that the early prediction accuracy of the IVIG unresponsive Kawasaki disease can be further improved. By introducing IL-6 and IL-17 as novel biomarkers, early accurate prediction of the IVIG unresponsive Kawasaki disease is realized. Compared with the prior art, the method can provide a quicker and more accurate risk assessment basis for clinicians, so that early recognition of high-risk patients, optimization of subsequent treatment schemes and reduction of serious complications are facilitated, and a new thought and direction are provided for clinical diagnosis and treatment.

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 electrocardiosignal detection, in particular to a leukemia child cardiotoxicity prediction system based on electrocardio dynamic evolution characteristics, which comprises the following steps: acquiring electrocardiosignals of a leukemia child in a whole chemotherapy cycle; performing empirical mode decomposition on the preprocessed electrocardiosignal, and performing dynamic modeling on an empirical mode decomposition result to obtain time domain representation and dynamic domain representation under different scales; performing cross-domain fusion on the time domain representation and the dynamic domain representation under different scales to obtain multi-scale dynamic and static characteristics; on the basis of the multi-scale dynamic and static characteristics, electrocardio time-varying dynamic evolution characteristics of the child patient in the whole chemotherapy period are calculated; based on the electrocardio time-varying dynamic evolution characteristics of the whole chemotherapy cycle of the child patient, predicting an early prediction result of cardiotoxicity of the child patient; and explaining the prediction result to obtain the causal relationship between the cardiotoxicity and the electrocardio time-varying dynamic evolution characteristics. And the accuracy of cardiac toxicity prediction is 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.

The invention discloses a method for constructing an offshore wind turbine fault sensitivity evaluation knowledge graph. The method comprises a method for avoiding limitation caused by lack of rapid diagnosis and advanced prediction of similar faults of a wind turbine generator. The structured marine meteorological data and the fan SCADA operation data are processed, fault sensitive feature factors are extracted, and feature factors with high fault correlation are comprehensively extracted; abstraction fault sensitivity characteristic factors are used as nodes, unstructured information in operation and maintenance data is abstracted into entity nodes or attribute nodes, the relation between the nodes is established, the incidence relation of fan faults is understood and analyzed, a graphic structure is presented through a visual tool, and the fault diagnosis efficiency is improved. A user can visually carry out query and reasoning operation, and the situation that personnel lack rapid diagnosis and advance prediction on similar faults of the wind turbine generator through an SCADA system and working experience of the personnel, and limitation occurs is avoided.

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.

Predicting cardiovascular diseases (CVD) may be preceded by subtle changes in various health indicators that may make early predictions useful for timely intervention. The present disclosure relates to prediction of one or more diseases, particularly CVD, for a given subject by leveraging one or more machine-learning models based on longitudinal multimodal data. The techniques, as disclosed herein, may utilize the one or more machine-learning models including one or more feature generators, a temporal aggregator and a prediction model to process the longitudinal multimodal data, generating relevant features, a temporal feature vector and one or more metrics corresponding to one or more diseases associated with the subject. The generated metrics based on the current time and previous time points may be analyzed to assess whether the subject currently has or is at risk of developing one or more diseases.

Provided are a method, device, and recording medium of early prediction of sepsis or septic shock through bio-data analysis on a computing device. In an embodiment, the method comprises training one or more time series deep learning models by: encoding sepsis or septic shock patient vital signs as time series-based bio-data to generate embedded time series patches; encoding associated patient medical histories utilizing a pre-trained language encoder to generate encoded patient medical history patches; combining the embedded time series patches and the patient medical history patches into an array; and randomly masking the embedded time series patches and asking the model to reconstructing the masked patches based at least in part on the associated patient medical histories; and utilizing the one or more trained time series deep learning models to predict sepsis or septic shock for a new patient.

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 simulation forecasting method suitable for urban rainstorm waterlogging in areas lacking pipe network data, which is characterized in that for areas without pipe network data, rainwater inspection well information is acquired by calling a Baidu map, pipeline drainage flow is generalized based on a rainwater well equivalent drainage method, and for areas with pipe network data, pipeline drainage flow is generalized by calling a Baidu map. Constructing a drainage pipe network model based on a one-dimensional Saint-View equation by adopting a finite difference method; then, constructing a hydrodynamic model, coupling a rainwater well equivalent drainage method and a drainage pipe network model with an FVCOM model, simulating an earth surface ponding evolution process, and obtaining a rainstorm waterlogging data set; and training and establishing a water depth prediction model based on the obtained data, then predicting the water depth of each prediction point after a specific time in an actual rainfall event, and giving an alarm in advance when a waterlogging early warning range is reached. According to the method, the submerged water depth of a waterlogging-prone point can be predicted in advance, waterlogging forecasting of a city lacking pipe network data can be achieved, and rainstorm disaster loss is greatly reduced.

The invention relates to the crossing field of agricultural disease monitoring and plant pathology, in particular to an alfalfa root rot prediction method based on pathogenic bacteria classification and data fusion. The method comprises the following steps: separating and purifying alfalfa root rot pathogenic bacteria and identifying types to construct a pathogenic bacteria database; carrying out an indoor artificial contamination test, and collecting hyperspectral data and binocular depth camera point cloud structure characteristics at different disease stages; in combination with multispectral / hyperspectral and point cloud data of a field unmanned aerial vehicle, multi-temporal information of healthy and diseased plants is tracked and marked. After vegetation index screening and principal component analysis dimensionality reduction, a multi-classification model is used for training, spectrum-structure feature generality of indoor and outdoor diseased plants is mined, and an early prediction model is constructed. According to the method, pathological diagnosis, spectral remote sensing and machine learning technologies are fused, cross-scale correlation analysis of pathogenic bacteria classification and field phenotypic characteristics is achieved, technical support is provided for accurate early warning of alfalfa root rot in the early stage, and the problems of traditional detection lagging and data isolation are solved.

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 belongs to the field of cerebral apoplexy prediction, and relates to a self-optimization edge intelligent cerebral apoplexy early prediction method and system, and the method comprises the steps: S1, collecting the marked data of cerebral apoplexy, and carrying out the preprocessing of the marked data, and obtaining a marked feature set D; s2, training the prediction model according to D to obtain a latest prediction model; initializing a pseudo mark feature set; s3, collecting data in real time and preprocessing the data to obtain a feature ft; s4, inputting ft into the latest prediction model to obtain a prediction result; combining the feature ft and a prediction result thereof to obtain a pseudo-mark feature sample ft '; s5, updating the newest pseudo-mark feature set according to ft'and judging whether a retraining condition is met or not, and if yes, executing the step S6; otherwise, returning to the step S3; s6, retraining the latest prediction model in combination with D and the updated pseudo-mark feature set Ft ', updating the feature set Ft' according to the latest prediction model to obtain a latest pseudo-mark feature set, and returning to the step S3; according to the method, the performance of the prediction model is improved by self-optimizing the pseudo-mark feature set.

The invention discloses a Parkinson's disease prediction method based on adaptive federal learning and related equipment. The method comprises the following steps: step 1, initializing and broadcasting a global model; 2, user local training; step 3, the user uploads the local convergence speed of the local training to the server; step 4, the server adaptively calculates and adjusts the participation rate of the current round of communication, the selected users and parameters of the next round of local training of the users according to the local convergence speed, and sends calculation results to all the users; 5, updating the global model and broadcasting again; step 6, performing iterative training and model optimization; and step 7, predicting new patient data by using the global model obtained by training to realize early prediction of Parkinson's disease. According to the method, on the premise of ensuring the global model precision, the equipment with higher convergence speed and higher contribution degree is adaptively selected to participate in training, so that unnecessary communication overhead is reduced, and the model convergence efficiency is improved.

The invention discloses an application of sIL-2R alpha as a marker in a product for early prediction of acute respiratory distress syndrome. The product for early predicting the acute respiratory distress syndrome comprises a reagent or a medicine for detecting the expression level of sIL-2R alpha in a patient with the acute respiratory distress syndrome. The sIL-2R alpha is used as a biomarker, and experiments prove that the sIL-2R alpha level of the plasma of the ARDS patient is obviously higher than that of a patient without ARDS in the plasma of the ARDS patient. By detecting the sIL-2R alpha level of plasma or serum of an ARDS patient, the ARDS occurrence risk is predicted in an early stage, so that individualized intervention treatment is given, the occurrence rate of ARDS can be reduced, and the kit has important clinical application value for improving prognosis of the ARDS patient.