554 results about "Risk forecasting" patented technology

A Method and System for Tuning the Effect of Vehicle Characteristics on Risk Prediction is disclosed. The system many incorporate many of those driver risk assessment system features previously disclosed by Assignee's Prior Applications. The present system provides a major functional distinction from those prior systems by adding the feature of real-time tuning of the risk assessment / prediction / analysis system in response to ongoing changes in vehicle motion characteristics. Specifically, system monitors the vehicle center of gravity for changes on a real-time basis, and then adjusts the risk prediction / assessment / analysis system responsively. The system executes an initialization feature that implements an initial, or series of initial vehicular motion profiles at the commencement of either a driving trip of the system being powered up. A catalog or index of “standardized” motion profiles are accessible for initial comparison to actual vehicle motion characteristics in order to streamline the tuning of the driver risk analysis system. The system determines if and when an offset in Center of Gravity (i.e. from where initialized) has occurred, after which the system responsively tunes the driver risk assessment / prediction / analysis / reporting system.

Distributed computing methods and systems are disclosed, wherein intensive fatigue-risk calculations are partitioned according to available computing resources, parameters of the fatigue-risk calculation, time-sensitive user demands, and the like. Methods are disclosed wherein execution-cost functions are used to allocate accessible computing resources. Additional methods include partitioning calculation tasks by user-prioritized needs and by general mathematical features of the calculations themselves. Included herein are methods to calculate only prediction-maximum likelihoods instead of full probability distributions, to calculate prediction likelihoods using Bayesian prediction techniques (instead of full re-tabulation of all data), to collate interim results of fatigue-risk calculations where serial results can be appropriately collated (e.g., serial time-slice independence of the cumulative task involved), to use simplified (e.g., linear, first-order) approximations of richer models of fatigue prediction, to assign user-identified priorities to each computational task within a plurality of such requests, and the like.

A method and system of matching an investor's objectives for portfolio investment return and risk with an assessment of a range of expected returns and risks that are likely to be generated by investment portfolios consisting at least in part of alternative asset classes that involves, for example, selecting available historical data for a plurality of alternative asset classes, unsmoothing the historical data based at least in part on historical data for traditional asset classes related to the respective alternative asset classes, and correcting the historical data for the alternative asset classes for an impact of survivorship and selection biases. A forecast of an expected return and risk is computed for each of the alternative asset classes, based at least in part on the unsmoothed and corrected historical data for the alternative asset classes, and at least one of the alternative asset classes that has an expected return and risk that corresponds substantially to the investor's objectives for portfolio investment return and risk is identified for inclusion in the investment portfolio.

A method for conveying both a risk and the degree of risk of a severe weather at a particular geographic location is disclosed. The method involves receiving a location of interest from a requestor, forecasting a risk of severe weather for a specific time period associated with the location, computing a degree of severity level of the risk of severe weather for the location, and generating a report containing the risk forecast and the computed severity level that can be delivered or provided to the requestor. The severity level of the risk of severe weather is provided in an easy to understand graphic or other format.

The invention relates to a discriminant analysis-based high road real-time traffic accident risk forecasting method. The method comprises the following steps of: building a high road accident risk discrimination model for a detection area; substituting real-time traffic flow characteristic parameters into the high road accident risk discrimination model; and judging whether the risk of traffic accident exists or not. According to the method, traffic accidents can be forecasted in real time by using the real-time traffic flow characteristic parameters acquired by high road traffic detection equipment, the method has relatively high forecasting precision, and technical defects and shortages in the prior art for analyzing traffic safety by using aggregated statistics are overcome. The methodhas practical engineering application value in the aspects of discrimination of the risk of the high road traffic accidents and forecast of the traffic accidents.

A system and method for determining residual business risks by correlating threats, controls, business continuity factors, and other general risk considerations is described. Requirements of an initiative of a project are mapped to a taxonomy, and the mapped requirements are rated with respect to its importance to the project. Projected changes in the mapped requirements are forecasted over a specified period of time, such as an eighteen month period. A threat to the project is mapped to the taxonomy, and the mapped threat is rated with respect to its impact on the project. Projected changes in the effectiveness of the control are forecasted based upon historical data, a maturity rating, and the rated effectiveness of the control. Residual risk associated with the project is then determined, and adjustments to one or more resources associated with the project may be made to reduce the determined residual risk.

The invention relates to a gradient-boosted tree based power outage complaint risk prediction method, which comprises the steps of A, building a user power utilization information table; B, performing preprocessing on user power utilization information data sets in the user power utilization information table; C, performing clustering on the user power utilization information data sets by adopting a Canopy algorithm and a KMeans algorithm, carrying out sensitivity category marking on the user power utilization information data sets through client portrait analysis, then performing data processing on unbalance distribution user power utilization information data sets through a SPARK based SMOTE oversampling algorithm; D, performing gradient-boosted tree training on the user power utilization information data sets, and acquiring a power outage complaint risk model; and E, predicting the power outage sensitivity category of a user by using the power outage complaint risk model. The method facilitates to accurately judge the sensitivity degree of different users for power outage so as to adopt different pacifying and guiding strategies and reduce the number of power outage complaints of the users.

The invention discloses a regional mountain torrent risk prediction method and system. The method comprises the steps: S1, predicting the weather information of all hills in a region based on a meteorological model; S2, screening out hills needing risk prediction based on the weather information; S3, dividing hills needing risk prediction into different risk prediction levels according to the basic information of the hills; wherein different risk prediction levels correspond to different risk prediction periods; S4, preliminarily determining disaster-causing factors for mountain torrent evaluation; S5, screening the preliminarily determined disaster-causing factors to obtain main disaster-causing factors influencing the mountain torrent; S6, training and generating a plurality of mountaintorrent risk prediction models based on the main disaster-causing factors; S7, several risk prediction models with the best performance are selected to be combined to form a final risk prediction model; and S8, performing mountain torrent risk prediction on the hill to be predicted. According to the method, the risk prediction of the regional hill is realized, the realization cost is low, the coverage is wide, the processing efficiency is high, and the safety of the hill is improved.

The present application relates to a big data analysis-based early warning information push method, an early warning information push device, a computer device and a storage medium. The method includes the following steps that: the risk data of a target customer are acquired; the risk data are quantified, so that a plurality of risk indicators are obtained; the risk indicators are inputted into apreset risk prediction model, so that a first risk score is obtained; a plurality of rule expressions are obtained, the rule expressions are adopted to perform risk prediction on the risk data, so that a second risk score can be obtained; and the first risk score and the second risk score are compared with each other, so that a high score value is obtained; and an early warning prompt is generatedaccording to the high score value, and the early warning prompt is pushed to a monitoring terminal. With the method adopted, the reliability of early warning information can be improved, and the false negative rate of risks can be reduced.

The invention relates to a disease risk predication method and system. The method comprises the following steps of: S1, receiving input gene sequencing result information; analyzing the input gene sequencing result information to obtain all mutant gene information; S2, looking up disease gene mutant information of corresponding gene sites from a precision medical knowledge base according to the gene sites corresponding to the mutant gene information, so as to obtain at least one piece of disease gene mutant information; S3, matching each piece of the disease gene mutant information with all the disease gene mutant information to obtain the similarity of each piece of the mutant gene information, and all the disease gene mutant information; and S4, obtaining a risk predication table aiming at the gene sequencing result information according to the similarity of each piece of the mutant gene information, and all the disease gene mutant information. By analyzing an association relation of susceptibility genes, mutation sites, mutation types and diseases, disease risk predication is carried out on healthy people and general disease risk grade predication is provided; disease risks in the future are reduced.

An apparatus for managing risk in social web media in a prediction-based manner is provided, and includes a risk vocabulary management unit, which extracts and manages vocabulary to be managed as pertaining to risk from social web content, a risk issue prediction analysis quality extraction unit, which performs language analysis and sensitivity analysis, a risk prediction modeling unit, which models risk prediction analysis, a risk detection and notification unit, which automatically detects and notifies the risk, a risk situation monitoring unit, which monitors in real time a risk state of a risk entity when an alarm is raised with respect to the detected risk, and a risk history management unit, which receives user feedback for monitored risk information and manages a record of a terminated risk situation.

The present application relates to a risk prediction processing method and apparatus based on big data analysis, a computer device and a storage medium. The method comprises the following steps of: obtaining risk data of a target customer, wherein the risk data carries a customer identification; extracting a risk indicator from the risk data; acquiring a risk prediction model, the risk predictionmodel comprising a plurality of risk factors; screening the extracted plurality of risk indicators according to the risk factors; inputting the screened risk indicators into the risk prediction model,and outputting the risk score corresponding to the customer identification. The method can improve the efficiency and accuracy of risk prediction.

The invention relates to an enterprise operation risk prediction method. The method includes collecting the information data inside and outside an enterprise, and performing the data fusion processingon the collected information data; constructing and drawing a knowledge graph by utilizing the processed information data; extracting operation risk characteristic information data of an enterprise needing risk prediction from the knowledge graph by using a graph embedding method, and constructing an operation risk model by using the operation risk characteristic information data; and inputting the enterprise information data of which the risk needs to be predicted into the operation risk model, so that the development risk of the enterprise can be predicted and judged, correct decision on risk management of the enterprise of which the risk needs to be predicted is facilitated, and the asset safety of the enterprise of which the risk needs to be predicted is protected.

The invention discloses a system and method for predicating risks of a drilling construction working site of an oil-gas well. The system comprises a data collection module, a data management module, a data application module and a decision supporting module, wherein the data collection module is used for collecting data of field operation, the data management module is used for storing the data collected on site and sampling data, the data application module is used for achieving information issue, data query, data statistics and report output, and the decision supporting module is used for carrying out risk analysis on data collected on site and alarming according to the sampling data. The method comprises the steps of collecting data of a working site, carrying out risk analysis on collected monitoring data according to the sampling data to obtain a risk analysis result, determining pre-warning levels by combining a preset pre-warning threshold value, and carrying out corresponding risk pre-warning actions. The system and method for predicating the risks of the drilling construction working site of the oil-gas well aim at the data collected on a well drilling site, a risk bayesian network and an ARIMA model are combined together, effective analysis and prediction of risk probability of accidents are achieved, analysis accuracy is high, and authenticity is strong.

The invention relates to a major hazard recognition method, a major hazard recognition system, a storage medium and a computer device. The method comprises steps: the state information of a hazard is acquired; the state information is inputted to a pre-built accident evolution model, and an accident type evolved by the hazard and the happening possibility of each accident type are obtained; according to the preset criterions, a disaster bearing carrier corresponding to the hazard is determined, and an exposure assessment model and a vulnerability assessment model of the disaster bearing carrier are acquired, wherein the number of preset criterions is at least two; the output result of the accident evolution model is read, and according to the output result, the exposure assessment model and the vulnerability assessment model, a risk prediction value corresponding to each criterion is calculated; and the risk prediction value of each preset criterion is compared with a corresponding risk threshold, and according to the comparison result, whether the hazard is a major hazard is judged. Thus, the major hazard can be effectively recognized, and happening of a major accident is prevented.

The invention discloses a risk prediction method and system. The method comprises the following steps of collecting the basic data related to the cardiovascular and cerebrovascular diseases from a regional medical information system; establishing a generalized linear logistic regression model based on the basic data; and predicting the personal cardiovascular and cerebrovascular disease occurrenceprobability according to the generalized linear logistic regression model. Therefore, the conditions that time and manpower are consumed for deliberate data collection for questionnaire survey and the physical examination can be solved, and the prediction accuracy of the cardiovascular and cerebrovascular diseases can be remarkably improved.

Systems and methods for enrichment of data associated with risk prediction. Data may be enriched by incorporating one or more aspects of consumer credit, collateralized debt, mortgage, real property, and loan data for use in systems, products, and methods relying on risk prediction models to maximize the effectiveness of the risk prediction. A time-series of consumer credit data may be provided to provide historical context to data. In a particular application, enriched data is leveraged to predict occurrence of an event relating to underlying assets of a structured security, such as a mortgage-backed security. Other systems and methods are disclosed.

The invention discloses an internet open information-based event occurrence risk prediction and early-warning method. The method comprises the following steps: (1), carrying out spam filtering on webpage information; (2), parsing words expressing location in the filtered webpage information to obtain a place name word; and carrying out processing on the parsed webpage information based on an established information body and classifying the webpage information into a matching region; (3), filtering the webpage information so as to obtain webpage information related to food safety, and carrying out processing on the filtered webpage information by using a regression analysis model and determining an object type related to each piece of webpage information; (4), determining webpage information sets of events of a set region and a set object and establishing event feature parameters and calculating parameter values regularly, and carrying out early warning on an event whose feature parameter exceeds a set threshold value; and (5), on the basis of a matrix analysis and a regression prediction model, carrying out different early warning on a risk of set event occurrence at a target region. According to the invention, the efficiency of risk early warning is improved.

The invention provides a method for predicting risks of orthopedic operation of old people. The method comprises the following steps of primarily collecting physiology risk data and operation risk data of a patient from historical data; by studying complication or not and death or not in the result, performing dichotomy Logistics regression analysis on the collected data, and performing x2 inspection and t inspection analysis on single factors; screening the physiology risk factor and operation risk factor which obviously affect the complication generation rate and death rate; using the screened physiology risk factor and operation risk factor as variables, and obtaining a risk parameter predicting model by a multi-factor Logistics regression analysis predicting method; according to the predicting model, predicting the main dangerous factors related with the death and complication after operation, and the corresponding relative risk degree value. The method has the advantages that the method is used for predicting the complication generation rate and death rate of the old patient after orthopedic operation; the more accurate predicting model is realized, and the effect of predicting the orthopedic operation of the old patient is greatly improved.

The invention discloses a disease risk predicting method, a device and a computer storage medium, wherein the disease risk predicting method, the device and the computer storage medium relate to the field of data analysis technology. More comprehensive analysis can be performed on the disease data for improving disease risk predicting precision. The disease risk predicting method comprises the steps of acquiring disease sample data with relevance, wherein the disease sample data comprises the disease data of each disease type and disease related data, and furthermore each disease type label isrecorded in the disease sample data; inputting the disease sample data with relevance into a deep learning model for training, constructing a disease risk predicting model which is used for predicting the risk predicting result of the disease data in each disease type; and when to-be-predicted disease data are received, based on the disease risk predicting model, outputting the risk predicting result of the to-be-predicted disease data in each disease type.

The invention provides a prediction method based on a knowledge map and a complex network combination. The method comprises the following steps: acquiring a plurality of software fault cases of different types; anyalzing Multiple fault phenomena and fault causes in fault cases by clustering analysis. The key words of phenomenon clustering and cause clustering are extracted as clustering labels ofeach category to generate knowledge map. Wherein Clustering labels are respectively corresponding to a plurality of functional modules of the software; Acquiring a mapping relationship between each functional module and the software code; Setting up code network; mapping The functional modules corresponding to the clustering label to the code network under each version, and the corresponding codepart is marked to predict the location of the code network risk of the unknown version software. The invention can effectively mark the specific software fault correspondence into the code network, and then predict the risk of the unknown version of the software code network, and then implement effective risk avoidance measures.

An embodiment of the invention provides a driving risk assessment method and a driving risk assessment device based on driving behaviors, and the driving risk assessment method and the driving risk assessment device belong to the field of risk prediction. The driving risk assessment method comprises the steps of: acquiring first driving behavior parameter data; inputting the first driving behavior parameter data into a pre-acquired risk prediction model for risk prediction; and acquiring an assessment result according to the first driving behavior parameter data and the risk prediction model. According to the driving risk assessment method and the driving risk assessment device, the assessment result is high in precision, and can be identified by insurance companies through data output and used for screening driving risks, thereby reducing a combined ratio thereof, and realizing a win-win situation; meanwhile, the driving risk assessment method and the driving risk assessment device can provide technical support and data support for the innovative differential pricing in the car insurance industry, and can earn fairer and more reasonable commercial insurance premium pricing for the good vehicle owners who drive safely.

The invention discloses a drilling well risk prediction method based on a Markov chain and a Bayesian network, which employs the Markov chain and Bayesian network to perform comparatively comprehensive analysis and prediction on drilling risks from vertical and horizontal aspects, and overcomes the deficiency of treating lack of indicators by the Markov chain. The Markov chain is a vertical prediction method to be used for detecting variable probability distribution determined by samples in future time. The Bayesian network is a horizontal prediction method and displays an index mutual influence relation. The combination of horizontal prediction and vertical prediction methods can solve the problem of lack of non-underlying index data of a multilayer index system to realize risk prediction of macroscopical meaning. The backstepping function of the Bayesian network also provides a basis for risk control.

The embodiment of the invention discloses a processing method and device and processing equipment for insurance service risk prediction. According to the method, the gradient boosted decision tree canbe pre-used to construct a risk prediction module, and the risk prediction module can be trained by using marked risk associated data related to an insurance service; when the risk prediction moduletraining reaches prediction requirements, the risk prediction module can be used for online risk prediction, insurance service risk prediction is carried out on a to-be-tested user, and a prediction result is output. The provided method can reasonably effectively use multidimensional nonlinear variables in the insurance service; the risk prediction module based on a nonlinear relationship of the gradient boosted decision tree can have good compatibility of linear and nonlinear variables; compared with a conventional linear module, the accuracy of a prediction result is markedly improved, the deficiency of the conventional linear module is effectively remedied, and the service experience of the insurance service is improved.

The invention discloses a perioperative risk assessment and clinical decision intelligent auxiliary system. The system comprises a perioperative period risk structural evaluation and prediction subsystem, a perioperative period decision intelligent auxiliary subsystem, a postoperative follow-up visit and quality management subsystem, a cloud server and an intelligent terminal. The perioperative period risk structural evaluation and prediction subsystem comprises a structural clinical data acquisition device and a risk analysis device; the perioperative period decision-making intelligent auxiliary subsystem comprises a preventive intervention plan decision-making intelligent auxiliary platform and a perioperative period management cruise and intelligent decision-making auxiliary platform. According to the method, a structured and standardized acquisition and risk prediction report platform for preoperative-intraoperative-postoperative full-cycle data parameters of individual patients isconstructed, the problems of fragmentation, heterogeneity and large deviation degree of a current risk assessment tool are solved, and butt joint with clinical decision management is realized.

The invention provides an overdue risk prediction method and device of credit application. The method can comprise that original characteristic is extracted from credit application data of a user to be predicted; a characteristic compressed coding model is used to process the original characteristic to obtain a corresponding compressed coding characteristic; the characteristic compressed coding model is obtained by training a mark sample characteristic corresponding to mark sample data and a mark-free sample characteristic corresponding to mark-free sample data without monitoring, the mark sample data is form credit application users who succeed in application, and the mark-free sample data is form credit application users who fail in application; and a characteristic set related to the compressed coding characteristic is generated, and an overdue risk prediction model is used to process the characteristic set to obtain a corresponding overdue risk prediction probability.

The invention discloses a regional bridge risk prediction method and system. The method comprises. The method comprises the following steps: S1, collecting the basic information of all bridges in a region; S2, screening out a bridge needing risk prediction based on the basic information; S3, dividing the bridges needing risk prediction into different risk prediction levels according to the basic information of the bridges; wherein different risk prediction levels correspond to different risk prediction periods; S4, preliminarily determining disaster-causing factors for bridge collapse evaluation; S5, screening the preliminarily determined disaster-causing factors to obtain main disaster-causing factors influencing bridge collapse; S6, training and generating a plurality of bridge collapserisk prediction models based on the main disaster-causing factors; S7, selecting the risk prediction model with the best performance as a final risk prediction model; and S8, performing bridge collapse risk prediction on the to-be-predicted bridge. According to the invention, the risk prediction of the regional bridge is realized, the realization cost is low, the coverage is wide, the processing efficiency is high, and the safety of the bridge is improved.

The invention discloses a stock market risk prediction platform, comprising: a data collection module; a data preprocessing module; a text mining module; a stock market prediction module; a risk assessment module; and a result output module. The present invention also provides a text mining method for a stock market risk prediction platform, which is a method for converting unstructured text data into structured data to analyze the viewpoints, attitudes or sentiments contained in the documents. The invention has a reasonable design and converts unstructured text data into structured data to analyze the views, attitudes or emotions contained in the document, and evaluates the risk level of the stock market according to the results obtained by data analysis. The risk level of the stock market is not only It can serve investors' decision-making, and can also provide a basis for the government to formulate relevant policies and enterprises to implement corresponding strategies.

System and method to predict risk of re-identification of a cohort if the cohort is anonymized using a de-identification strategy. An input anonymity histogram and de-identification strategy is used to predict the anonymity histogram that would result from applying the de-identification strategy to the dataset. System embodiments compute a risk of re-identification from the predicted anonymity histogram.

Embodiments of the invention disclose a processing method, device and apparatus for predicting insurance business risks. By utilizing the processing method, device and apparatus, a calculation gradient decision-making tree is imported in insurance business risk prediction, so that risks of insurance business data with nonlinear relationships in insurance businesses can be predicted, relative risksize relationships after risk prediction can be output, sorted risk prediction results represent relative sizes of risks between different users, and another more reliable insurance business risk prediction scheme can be provided.