81 results about "Risk factor" patented technology

The invention relates to the technical field of information management, in particular to an underground pipeline gallery risk monitoring method and device. According to the method, a heterogeneous sensing terminal network is used for sensing a target underground pipeline gallery according to a fast and slow time sequence, and a pipeline gallery monitoring anchor point set is determined; performing cross-modal interaction on a heterogeneous sensing terminal network based on the pipeline and pipe gallery monitoring anchor point set to obtain a pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set; performing anchor point risk assessment according to the pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set, and obtaining a pipeline and pipe gallery monitoring anchor point risk factor set according to an assessment result; and when the pipeline and pipe gallery monitoring anchor point risk factor set is greater than or equal to a preset anchor point risk factor threshold value, triggering a risk early warning instruction. According to the method, accurate identification and dynamic early warning of the key risk points of the underground pipeline gallery are achieved through fast and slow time sequence sensing and cross-modal feature fusion processing based on the monitoring anchor points.

The invention relates to the technical field of diabetes prediction, in particular to a method for predicting early diabetes based on health data, and the method comprises the steps: carrying out the data collection and preprocessing of an individual health system, and obtaining diabetes risk related data; performing off-line calculation on the data by using a feature correlation analysis technology, and extracting a diabetes prediction feature set; performing risk assessment on the feature set through a preset diabetes prediction model to generate a diabetes risk prediction result; key risk factors are extracted, a risk factor classification model based on a random forest is constructed, risk attribution analysis is carried out, and main risk factors are identified; then, comprehensive assessment is carried out on the main risk factors in combination with confidence propagation analysis and risk path analysis technologies, and a core risk source is positioned; the individual diabetes risk portrait is constructed according to the core risk source, the relationship among the risk factors is analyzed, a personalized early intervention scheme is formulated, and the comprehensiveness, precision and interpretability of early diabetes prediction are remarkably improved.

The invention relates to the field of medical health management, and discloses a thyroid-oriented whole-cycle health management method, which comprises the following steps of: firstly obtaining thyroid multi-source information data of a target population to form a data set, then extracting fusion features to generate a time sequence feature vector, and analyzing and calculating occurrence risk factors and scores of feature dimensions in a preset time window according to the time sequence feature vector; a comprehensive risk score is obtained through time sequence analysis, risk grades are divided, high-risk individuals are marked, and a personalized screening strategy is generated; then combining historical data of high-risk individuals, analyzing key driving paths with high risk scores, associating an external knowledge base to predict recurrence frequency, and finally dynamically adjusting and optimizing screening and intervention strategies according to the recurrence frequency; precise evaluation, grading and personalized management of individual thyroid health risks are achieved, then risk key driving paths are found out, recurrence is predicted, and the strategy is dynamically optimized to improve the disease prevention and treatment effect.

The invention discloses a foundation pit multi-source risk identification method and device, and the method achieves the quantitative description of a dynamic incidence relation between risk factors through the construction of a dynamic risk propagation network, the fusion of multi-source monitoring data, and the adoption of a causal and related joint modeling method, breaks through the limitation of a conventional static analysis model, and achieves the recognition of a foundation pit multi-source risk. Real-time risk identification and trend prediction in the foundation pit construction process are realized, and the foundation pit construction safety is ensured. Furthermore, according to the embodiment of the invention, a key risk factor and a high-risk propagation channel are quantitatively identified in combination with node centrality analysis and a weighted path identification mechanism, and the stability and traceability of a risk identification result are improved through a time aggregation mechanism, so that the accuracy and engineering adaptability of foundation pit risk control are remarkably enhanced.

The invention discloses a complication prediction method and system based on an SHAP-random forest, and relates to the technical field of medical data mining. The method comprises the following steps: acquiring preoperative CT images and puncture path planning data, and calculating risk factors; performing numerical value standardization, nonlinear transformation and interactive feature generation on the risk factors to obtain feature vectors, calculating mutual information scores of feature values in the feature vectors, if the mutual information scores are greater than an experience threshold, retaining the feature values, and after traversal is finished, obtaining updated feature vectors; on the basis of a random forest model, taking the updated feature vector as an input value, and calculating a complication probability; quantizing the contribution degree of the characteristic value based on a Shapley value; according to clinical indexes, the risk threshold is dynamically corrected, the complication risk level is divided, and complication prediction is completed, the problems that a static threshold ignores the blood coagulation state difference of a patient and a black box model cannot provide a decision basis are solved, and the complication misjudgment probability is reduced.

The invention discloses a lung cancer high-risk group screening questionnaire cognitive calculation correction method and system. The method comprises the following steps: acquiring questionnaire text data of a subject; constructing a BERT-QA model based on the clinical term knowledge graph, analyzing questionnaire text semantics, and extracting structured risk factor data; mapping the risk factor data to a topological space, and constructing a risk topological representation; calculating a dynamic weight coefficient of each risk factor based on an information entropy principle, and generating a risk assessment value of entropy weight optimization; carrying out calibration in combination with regional pollution map data; according to the method, deep learning, topology and fuzzy logic technologies are fused, intelligent analysis of questionnaire texts and dynamic weight calculation of risk factors are realized, environmental pollution factors are effectively integrated, the screening accuracy and individuation level are improved, and the method is suitable for large-scale popularization and application. The method provides technical support for accurate identification and early intervention of lung cancer high-risk groups, and has important clinical application value.

The invention discloses a schizophrenia concurrent metabolic syndrome independent risk factor screening method and system, utilizes a binary logistic regression analysis method to analyze relevance between multi-source factors and concurrent MetS and screen out independent risk factors, and relates to the technical field of medical data processing. According to the schizophrenia concurrent metabolic syndrome independent risk factor screening method and system, the relevance between multi-source factors and concurrent MetS is analyzed through a binary logistic regression analysis method, the independent risk factors are screened out, and a decision tree model is constructed based on the reserved independent risk factors and used for visually displaying and predicting the risk of concurrent MetS of a patient. By comprehensively considering multi-aspect information of the schizophrenia patient and adopting independent risk factor determination and model construction operation, the independent risk factors causing MetS occurrence of the schizophrenia patient are accurately identified, the screening accuracy is improved, meanwhile, the level of MetS concurrent by the patient is predicted, subsequent clinical timely intervention is facilitated, and the risk of MetS occurrence of the schizophrenia patient is reduced. And the MetS concurrent probability of the patient is reduced.

The application provides a model construction and verification method and nomogram for predicting the clinical prognosis of SFTS patients complicated with AP. By collecting the clinical data of SFTS patients, the independent risk factors related to adverse prognosis are screened out by using LASSO regression analysis, a multi-factor Logistic regression model is constructed, and the model is visualized by nomogram for predicting the adverse prognosis of SFTS patients complicated with AP. The model has high discrimination and calibration, and can provide an effective prediction tool for clinicians to optimize clinical decision-making.

This invention relates to the field of information platform construction technology, and more particularly to a method and system for constructing a sports information intelligent service platform. The method includes the following steps: collecting user motion image data and user basic health data; extracting key body node position information during the user's movement; generating a user movement trajectory curve based on the key node position information, and calculating the deviation angle and distance from a standard trajectory template to obtain movement correctness data; constructing a user fitness risk assessment model based on the movement correctness data and user basic health data, and analyzing the difference from a preset safety threshold to generate tiered health guidance data; decomposing the tiered health guidance data into quantifiable progress units to generate progress tracking data. This invention, by combining user motion image data and basic health data, constructs an assessment model capable of identifying individualized risk factors, achieving more accurate safety guidance.

The invention discloses a postoperative shoulder pain occurrence risk prediction method. The method comprises the following steps: S1, obtaining risk factors of postoperative shoulder pain of each patient sample; s2, using a GA algorithm to process the data set I by taking maximization of AUC as a target, and searching an optimal variable combination; s3, obtaining each core sub-factor; s4, outputting a column graph model; s5, substituting the data set II into the column graph model, calculating the predicted occurrence rate of each shoulder pain, and then calculating the AUC2 and the calibration curve of the column graph model in the data set II; when the AUC2 is not lower than the preset threshold value and the calibration curve meets the calibration slope range, the column graph model is used for postoperative shoulder pain prediction of the new patient. According to the method, the risk factors are integrated, the genetic algorithm is used for intelligent feature screening, the prediction model with high distinction degree and calibration degree is constructed, a strict data set verification and loop optimization mechanism is adopted, it is ensured that the model is stable and reliable, and finally risk visualization is achieved through a visual column graph.

This invention provides an improved FMEA device that considers the relationship between uncertainty and failure modes. It features the following components: an SVTraNN scoring conversion module for obtaining the SVTraNN evaluation matrix; an expert weight calculation module for calculating expert weights; an expert evaluation summarization module for calculating the SVTraNN decision matrix; a subjective weight calculation module for calculating the final subjective weight of each risk factor; an objective weight calculation module for calculating the objective weight of each risk factor; a comprehensive weight calculation module for calculating the comprehensive weight of each risk factor; a comprehensive score calculation module for calculating the comprehensive score of each risk mode; a risk ranking module for obtaining the risk ranking result of each risk mode; and a risk level module for obtaining the risk level of each risk mode. In summary, this method can effectively assess the risk level of risk modes.

The invention provides a three-high index personalized threshold generation system and method, and relates to the technical field of risk prevention, and the method comprises the steps: integrating and standardizing patient data from a multi-information system through a data acquisition module, and constructing a feature vector; the dynamic threshold generation module selects any two of blood sugar, blood fat and blood pressure in the feature vectors as fixed indexes, traverses possible values of a third dynamic index, inputs the possible values into a pre-training model to predict the plaque progress risk, and takes a dynamic index value domain lower than a safety threshold as a personalized dynamic threshold range; the threshold adjustment module further calibrates the range according to the interaction relationship between the features. The method has the beneficial effects that a complex nonlinear relationship among multiple indexes is described by means of machine learning, and the individualized dynamic index threshold value is dynamically generated according to the index level, so that the core problem that the individualized control threshold value of a certain risk factor is difficult to dynamically adjust by comprehensively considering the actual levels of other indexes is solved.

The invention discloses a PICC intelligent maintenance management method and system based on the Internet of Things, and belongs to the technical field of medical equipment management, and the method comprises the steps: obtaining fixed state data F of a catheter puncture area, including catheter tension, skin fitness and local temperature and humidity; extracting a catheter loosening risk factor R based on the data; collecting the temperature of the skin around the puncture port, the change of the infrared thermogram and the pH value information to form physiological parameter data P; fusing the risk factor R and the physiological parameter P, calculating an infection trend coefficient I, and constructing a catheter abnormality early warning model W1; dynamically evaluating the model to obtain a state change feature vector T; a personalized intervention strategy is generated in combination with individual basic data M of the patient, and is pushed to the nursing terminal through wireless communication, so that intelligent reminding and guidance are realized; according to the invention, real-time monitoring and early warning of the state of the conduit are realized, and the intelligent and precise level of conduit maintenance is improved.

The invention provides a risk factor quantitative analysis method and system applied to a sewage treatment system and a medium. The method comprises the following steps: acquiring an adjacent matrix representing a direct influence relationship among a plurality of risk factors in the sewage treatment system; performing interpretation structure model analysis based on the adjacent matrix, and determining a hierarchical structure relationship of multiple levels of risk factors; based on the hierarchical structure relationship, automatically generating a judgment matrix for an analytic hierarchy process through a preset rule; and calculating to obtain respective risk weights of the plurality of risk factors based on the judgment matrix. According to the embodiment of the invention, an objective algorithm is adopted to replace subjective expert scoring, the objectivity, repeatability and logic consistency of the risk weight analysis result are remarkably improved, the automation of the analysis process is realized, and the evaluation efficiency is improved.

The invention relates to the field of risk assessment, in particular to a water conservancy project quality safety supervision risk assessment system. The method comprises the specific implementation steps that firstly, nonlinear interaction of risk factors in multi-source heterogeneous data is converted into quantitative risk numerical values through a risk quantification model, and the quantitative risk numerical values and context factors are packaged into risk state vectors; then, weights are distributed for the risk state vectors of all the completed engineering stages before, risk fusion vectors are constructed by fusing the weighted risk state vectors and heterogeneous data of the current engineering stage, and then the future risk evolution trend of the water conservancy project structure is predicted. When the prediction trend deviates from the safe trajectory, the system triggers early warning in advance and automatically backtracks the model to find out the main risk factor. In order to ensure the reliability of early warning, the system generates supervision robustness at the same time, and when the supervision robustness is lower than a preset threshold value, secondary assessment of the risk is automatically initiated according to the main risk factors and the robustness.

The invention relates to the cross technical field of artificial intelligence and healthy diet management, in particular to a healthy diet intelligent recommendation system and method based on a large model and an ai agent, multi-dimensional data of plateau environment, human physiology, storable food materials and cooking conditions are collected through a multi-modal module, a standardized data set is formed through preprocessing, and on the basis of the data set, an ai intelligent agent is obtained; the dynamic energy demand is accurately evaluated in combination with an altitude influence coefficient, a physical power coefficient and the like, meanwhile, a nutrient supply gap is calculated, an optimal storable food material combination with the minimum resource consumption is screened by establishing a nutrition standard constraint equation and inventory constraints, and a dietary scheme is generated in combination with plateau cooking evaluation. The health standard-reaching rate and the food material utilization rate are calculated based on the body feedback data, the scene, health and resource risk factors are fused to obtain the early warning index, grading intervention is triggered, intelligent and dynamic adaptation of plateau diet supply is achieved, the health of the garrison is effectively guaranteed, the food material utilization rate is increased, and the scene adaptation risk is reduced.

The invention discloses a recurrent abortion risk analysis knowledge graph construction method. A data acquisition unit, an evidence-based medicine evidence evaluation unit, a knowledge reasoning unit, a knowledge fusion unit, a classification attribute layer construction unit, a risk factor layer construction unit and an evaluation index layer construction unit are included. The method has the advantages that the description model of the factors causing the recurrent spontaneous abortion and the onset risk of the recurrent spontaneous abortion is constructed based on the knowledge graph, auxiliary guidance opinions are provided for clinical risk screening and diagnosis and treatment thoughts of the recurrent spontaneous abortion, risk factors of the recurrent spontaneous abortion can be comprehensively checked, and decision data basis is provided for doctors and patients.

The application provides a public safety multi-source risk factor correlation identification analysis method based on a knowledge graph, relates to the technical field of risk identification, and comprises the following steps: obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial correlation network, performing feature analysis and calculating a similarity matrix, identifying closely correlated subgroups through community discovery, constructing a multi-level correlation network, analyzing a transmission path and calculating a weight, and finally generating risk warning information. The application can effectively identify the complex correlation between public safety risk factors and improve the risk prediction accuracy.

The application discloses a user health data monitoring and positioning system based on a 4G smart watch, comprising: a data acquisition module for generating a watch monitoring data set; a time sequence correction module for generating a health positioning time sequence; a scene recognition module for generating a scene label sequence; a baseline construction module for establishing a scene health baseline and generating a health deviation amount; a risk generation module for generating a health risk factor and a positioning communication risk factor; a model construction module for constructing an improved Page-Hinkley detection model; a drift detection module for generating a health state recognition result and a risk early warning level; and a result uploading module for generating and uploading a user health data monitoring and positioning result. The application improves the health early warning accuracy and the positioning linkage response timeliness.

The application discloses a kind of risk factors prediction model of occurrence of initial treatment bacterium positive pulmonary tuberculosis lung cavity, belong to medical data processing technical field, the clinical data of initial treatment bacterium positive pulmonary tuberculosis patient is collected in the application, using single factor analysis, Lasso regression analysis and multivariate Logistic regression analysis, male, diabetes, smoking, white blood cell count and platelet / hemoglobin ratio are screened as the independent risk factors of initial treatment bacterium positive pulmonary tuberculosis causing lung cavity, and based on this, nomogram prediction model is constructed.H-L goodness-of-fit test, receiver operating characteristic curve and decision curve analysis are verified, the model has higher prediction efficiency and clinical practical value.The application can help clinical advance to predict and intervene the cavity caused by high-risk population of pulmonary tuberculosis, provide new ideas for the diagnosis and treatment of bacterium positive pulmonary tuberculosis causing cavity.

The invention discloses a dairy product risk management and control method and system based on a knowledge graph and attention cross recommendation model, and belongs to the technical field of food safety and intelligent recommendation. The method comprises the following steps: constructing a risk knowledge graph integrating dairy product whole industry chain multi-source data, and structurally representing risk factors, management and control measures and association relationships thereof; a KG-ACR (Known Graph-Attention Cross Recovery) model is proposed, high-order semantic association among entities is mined through a graph attention mechanism, and nonlinear feature fusion of risks and management and control measures is realized by using a cross compression unit; and recursively updating entity representation by adopting an attention embedding propagation mechanism, and capturing multi-scale semantic information in combination with multi-layer aggregation to realize accurate recommendation of management and control measures. The method effectively solves the problems of sparse interaction data, insufficient semantic association capture, dependence on manual rules and the like in the prior art, and is suitable for dairy product full-chain risk intelligent management and control.

The application provides a food safety monitoring method and system based on big data, comprising constructing alliance chain nodes in a food supply chain, uploading full life cycle data to a distributed ledger of a block chain for notarization to obtain traceability notarization data; establishing a risk factor correlation graph based on a time-space dimension, extracting a risk feature vector; based on a dynamic game model, monitoring the risk feature vector in real time, and judging whether an abnormal risk signal appears; if yes, triggering an early warning logic, the early warning logic combining a multi-party game mixed strategy Nash equilibrium solution to calculate an optimal early warning threshold under a current monitoring environment, and generating a response strategy; based on the optimal early warning threshold and the response strategy, calculating a safety risk index, and sending different levels of early warnings to relevant parties according to the safety risk index. The application can identify implicit risks that cannot be found by traditional threshold alarms, significantly improving the accuracy and forward-looking nature of risk early warning.

The invention discloses a Bayesian-based electric power service risk level prediction method, device and equipment, and relates to the technical field of risk assessment, and the method comprises the steps: carrying out the coding and splicing of a plurality of heterogeneous features of a target electric power service work order, and obtaining the fusion features of the target electric power service work order; inputting the fusion features of the target power service work order into the trained risk prediction model; quantifying the causal contribution degree of each risk factor to the risk level through a parameterized time sequence Bayesian network, taking the causal contribution degree as a feature weight, weighting the fusion feature of the target power service work order, and obtaining a causal perception feature vector of the target power service work order; and inputting the causal perception feature vector of the target power service work order into a risk level prediction network, and outputting the risk level of the target power service work order. According to the method, the causal contribution degree of the risk factor is quantified through the time sequence Bayesian network, and the causal association between the feature and the risk level is distinguished, so that the prediction accuracy is improved.

The invention provides a diet guidance system and method for medical interaction of a hyperlipemia patient, and is applied to the technical field of medical data processing. The method comprises the following steps: firstly, collecting demographic, clinical index, medical history and risk factor data for a hyperlipemia patient, and constructing a training sample set; dividing risk levels by using a Python model based on a guide, and screening intervention crowds; according to consensus and guide, energy intake is customized according to BMI and physical labor intensity, a dietary structure is constructed and verified, and a personalized dietary scheme is generated; furthermore, VR education content is developed, and fat visualization is realized through 3D modeling, physical attribute configuration and a rendering scheme; meanwhile, standardizing 9 types of food materials by taking 5g of fat as a unit, converting intake exchange parts and calculating the total fat amount; and finally, the compliance of the patient is improved by means of a VR gamification module.

The present application relates to a system and a method for predicting a disease risk of a companion animal and providing insurance based thereon, the system comprising: a data collection unit for collecting data on a companion animal; a data preprocessing unit for preprocessing the collected data; a model training unit for training a machine learning- or deep learning-based disease risk prediction model by using the preprocessed data; a risk prediction unit for predicting the disease risk of the companion animal by using the trained model; an insurance premium calculation unit for calculating an insurance premium suitable for the current health condition of the companion animal on the basis of the predicted disease risk; and an insurance proposal unit for proposing an insurance product on the basis of the calculated insurance premium, and providing an insurance premium discount according to removal of confirmed health risk factors or efforts to remove such health risk factors.

The invention provides a prediction system and a prediction method for postoperative delirium of people living in plateau for a long time and a storage medium, and belongs to the technical field of artificial intelligence target detection. According to the method, dangerous factors of delirium occurring after general anesthesia of people living in the plateau for a long time are collected, and a prediction model of delirium occurring after general anesthesia is established according to the number of the dangerous factors obtained through multi-factor Logistic regression analysis and correlation coefficients of the dangerous factors. According to the method, a prediction scoring system for delirium and cognitive function impairment after general anesthesia of plateau patients is established, and quantitative reference is provided for clinicians; accurate intervention can be started before an operation, postoperative cognitive impairment is reduced to the maximum extent, plateau crowds are assisted to rapidly recover, and life and working posts are returned as soon as possible.

The invention discloses a water conservancy risk prediction method based on evolution characteristics, and belongs to the technical field of water conservancy projects. The method comprises the following steps: dividing a to-be-detected area into a plurality of grids, collecting and normalizing surface and internal water conservancy risk factors of each grid, constructing a surface and internal evolution incidence matrix, extracting collaborative strength and total evolution strength, and splicing to form an evolution characteristic matrix; determining an evolution characteristic transfer coefficient based on the distance between the to-be-predicted grid and the neighborhood grid, and obtaining a neighborhood evolution characteristic transfer matrix; and inputting the multi-moment evolution characteristic three-dimensional matrix and the neighborhood evolution characteristic transmission three-dimensional matrix into a water conservancy risk prediction network, and outputting a grid water conservancy risk score. According to the method, high-precision prediction of the water conservancy risk is realized by capturing a risk factor space-time evolution rule and a neighborhood transfer effect.

The invention discloses a spine health risk assessment method and system based on individual features, and the method comprises the steps: obtaining the basic feature data and spine form measurement data of an individual, and constructing an individual feature portrait through occupational posture mode matching and posture deviation degree calculation; performing risk factor extraction on the individual feature portraits to form a candidate risk factor set, establishing a risk factor association map by adopting causal relationship identification, and performing intervention assessment to generate intervention tags; performing risk grading based on the risk factor association map and the dry label to generate a risk grading parameter, determining a risk grade and extracting a key risk factor set; performing risk classification on the key risk factor set according to spine partitions to obtain risk values of the partitions, identifying risk concentration areas through difference analysis, and identifying and obtaining risk distribution types in combination with a distribution mode; and a spinal health risk assessment result is output by integrating the partition distribution characteristics and the risk levels, and a scientific basis can be provided for personalized health management and precise rehabilitation scheme formulation.

The application relates to the technical field of biomedical engineering, and discloses a VTE system execution method and system combined with a standardized scale, wherein the method comprises the following steps: performing standardized processing on original data to obtain standardized data; constructing a risk intensity flow; performing integral operation to obtain a cumulative integral value of a risk factor; calculating a dynamic risk trajectory; fusing a baseline score value of the standardized scale with the dynamic risk trajectory to obtain a total risk trajectory; calculating an instantaneous risk probability, and judging whether a VTE risk early warning is triggered according to an adaptive threshold value; the four-dimensional risk intensity flow of a body position, anesthesia, hemostatic drugs and circulation parameters is constructed, continuous quantitative monitoring of VTE risk factors is realized, early identification and timely early warning of VTE risks are realized through a dual mechanism of continuous triggering and acute triggering, and the accuracy, real-time performance and individualization level of risk assessment are significantly improved.