27 results about "Risk Estimate" patented technology

The invention relates to the technical field of machine learning training fault tolerance, and discloses a LoRA training fault tolerance method based on state awareness. The method comprises the steps that training indexes are collected through a sensor network, a time sequence knowledge base is constructed, and sampling frequency is adjusted in a self-adaptive mode; historical data is utilized to generate a state change trend baseline, and abnormity is preliminarily identified through deviation degree comparison. And for the abnormal behavior, performing verification and risk assessment by fusing the gradient frequency domain characteristics and the loss curve form of the abnormal behavior. Encoding the anomalies into a high-dimensional point set, analyzing and identifying a continuous homologous structure of the high-dimensional point set by adopting topological data, and judging a systematic gradient anomaly mode according to topological characteristics; and deducing a compensation coefficient according to geometric attributes of the topological characteristics, calibrating a risk estimation value, and dynamically reconstructing a monitoring strategy. According to the method, systematic training faults can be accurately identified from a data structure level, adaptive intelligent fault-tolerant control is realized, and the reliability and efficiency of a training process are improved.

An information providing device according to the present invention comprises: an acquisition unit that acquires sensor data including an acceleration and an angular velocity measured by a measurement device mounted on the footwear of a subject for whom credit information is to be created in order to provide credit-related information according to the mental and physical state of the subject; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease related to the subject; a credit-related information generation unit that generates credit-related information according to the estimated disease risk related to the subject and the status information of the subject registered in advance; and an output unit that outputs the generated credit-related information.

The invention discloses a dynamic risk prediction method, system and device based on a causal enhanced space-time Transform (CAST), and a medium, and the method comprises the steps: carrying out the fusion of multi-modal time series data through a causal enhanced space-time Transform (CAST) model, and obtaining a causal enhanced fusion feature representation; estimating posterior distribution parameters of generalized Pareto distribution in an extreme value theory based on the feature representation; calculating an expected tail risk estimation value as a dynamic risk prediction value; and performing early warning or dynamic pricing decision according to the risk value. According to the method, a causal inference mechanism is introduced, so that pseudo-correlation interference in multi-modal data is effectively inhibited, and high adaptability to a dynamic environment is realized; meanwhile, historical prior knowledge is combined with real-time data, so that the accuracy and stability of tail risk parameter estimation are improved; in addition, a differential privacy mechanism is introduced in a risk value output link, and it is ensured that individual privacy is not leaked. The method has wide application prospects in the fields of intelligent transportation, financial science and technology, industrial Internet of Things and the like.

Heretofore, for the measurement of creatinine, it is required to collect blood from a subject and perform a biochemical analysis of the blood. However, such a method has the problem that it is required to prick the skin of a subject with a needle or the like in an invasive manner, which places psychological and physical burdens on the subject. [Solution] The present invention generates a creatinine risk estimation model by machine learning on the basis of attribute information, non-invasive biological information, and blood test data of a plurality of subjects which have been acquired in advance, and thus makes it possible to estimate a creatinine risk in a non-invasive manner from attribute information and non-invasive biological information of a specific user.

The present invention discloses a method and system for constructing an early warning model for a city safety risk assessment benchmark. The method comprises updating historical city safety data, determining a risk threshold, determining risk data based on the risk threshold, obtaining a historical data matrix from the city database, determining an assessment benchmark value based on the historical matrix and the risk data, determining a risk estimate based on the risk data and the risk threshold, optimizing the city safety risk assessment benchmark early warning model based on risk estimate deviations, and inputting the city safety data to be assessed into the optimized city safety risk assessment benchmark early warning model for risk assessment and risk warning. This method not only improves the efficiency and accuracy of city safety risk assessment benchmark management but also exhibits good interpretability and can be directly applied to city safety risk assessment benchmark early warning model systems.

A fall risk estimation method comprising: a step for extracting a feature quantity of movement of an evaluation target portion of a subject from motion data of the subject when the subject is moving the evaluation target portion in a state in which the center of gravity of the subject is stable within a predetermined range; a step for calculating a variation index of the feature quantity; and a step for estimating a fall risk of the subject on the basis of the variation index.

The invention belongs to the technical field of energy system digitization and risk assessment, and particularly relates to a virtual power plant operation risk assessment method and equipment based on adaptive important sampling. According to the scheme, through introduction of the relaxation probability distribution sequence and parameter optimization of the adaptive important sampling density function considering the risk level, the improved important sampling method can more accurately and rapidly approach optimal sampling distribution of the virtual power plant operation risk, the evaluation efficiency is improved, and through construction of the multi-stage fusion risk estimation function, the risk estimation efficiency is improved. All sample information generated in a pre-simulation iteration process and a main simulation stage is comprehensively utilized, so that the accuracy and the stability of evaluation indexes are remarkably improved; therefore, the problem of'computational disaster 'faced by traditional Monte Carlo simulation is effectively solved, the defects that a traditional important sampling method is low in sample utilization efficiency and does not distinguish risk levels are overcome, and key risk scanning data support can be provided for real-time market decision and internal resource scheduling of the virtual power plant.

To identify a risk from a target object for supporting risk assessment.SOLUTION: A target object information acquisition unit 111 acquires a target object which is a character string or an image of a target object for which a risk is identified. An information processing unit 112 generates a prompt to identify a risk estimated from the target object acquired by the target object information acquisition unit 111, and supplies the generated prompt to a generative AI to generate identification result information indicating a result of identifying a risk.SELECTED DRAWING: Figure 2

Embodiments present techniques for determining a list of recommended items in response to a user query. An embodiment can determine a first ordered list of items including a plurality of items stored by a content platform. Based on a reward discount parameter, a first total discounted future reward for the first ordered list of items can be determined. Based on a risk discount parameter, a first risk estimate for the first ordered list of items can be determined. Similarly, a second ordered list of items can have a second total discounted future reward and a second risk estimate. The second ordered list of items can be the list of recommended items when the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate.

Embodiments of the present specification provide a risk detection method and device, wherein the risk detection method comprises: obtaining feature data generated by a target software product in a plurality of target feature dimensions, the plurality of target feature dimensions being obtained by analyzing historical risk data of the target software product; for any target feature dimension, using a risk analysis strategy corresponding to the target feature dimension to analyze the feature data generated in the target feature dimension, and obtaining a risk estimate of the target feature dimension; and determining a risk detection result of the target software product according to the risk estimates of the plurality of target feature dimensions. The above method can improve the accuracy of risk detection by obtaining target feature dimensions with a relatively high correlation with the risk of the target software through analysis of the historical risk data of the target software product, and determining the risk detection result of the target software product through the feature data generated in the plurality of target feature dimensions.

The invention provides a traffic risk estimation method and system based on splicing frames and causal attention, and relates to the technical field of traffic scene risk assessment. The method comprises the following steps: acquiring RGB space video data of a traffic scene, obtaining an original space diagram through frame-level segmentation, preprocessing the original space diagram to obtain a gray scale weight map and a single-channel depth map, and splicing based on a frame splicing and supplementing strategy to obtain five-dimensional input data; inputting the five-dimensional input data into the improved ResNet-50 network for risk estimation; according to the improved ResNet-50, an SCA module is added behind each residual error layer, and dimension remodeling is carried out before a feature map is input into the SCA module and when the feature map is transmitted between the residual error layers so as to ensure that frame sequence information is correctly processed; and finally obtaining a risk classification result through a global average pooling layer and a full connection layer. Through combining a frame splicing and complementing strategy of strong spatial information and sparse causal attention, the space-time correlation consistency capability of the model is enhanced, and the accuracy and robustness of risk estimation in a complex traffic scene are improved.

The present invention provides, in order to provide a health measure according to a disease risk of a management target person engaged in daily work, an information generating device comprising: an acquisition unit that acquires sensor data including an acceleration and an angular velocity measured by a measurement device installed in a footwear of at least one management target person; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease concerning the at least one management target person; a proposal information generation unit that generates proposal information including a health measure according to the disease risk concerning the at least one management target person; and an output unit that outputs the generated proposal information.

The present invention provides a vehicle safety risk assessment method and device, the method comprising: performing risk assessment on each first node of an acquired target object to obtain a risk assessment result corresponding to each first node; wherein the first node is a bottom-level node of the target object; based on the risk assessment result of each first node and the target node of the target object acquired in advance, obtaining a risk assessment result of the target node; wherein the target node is a top-level node of the target object. The present invention performs risk assessment on the first node of the acquired target object, and then obtains the risk assessment result of the corresponding target object according to the risk assessment result obtained by the estimation, thereby improving the efficiency of risk assessment, and then improving the accuracy and reliability of risk assessment, so as to facilitate the subsequent determination of the corresponding risk solution according to the risk assessment result, and improve the efficiency and reliability of risk management.

The invention discloses a multi-sensor long-term scheduling method based on bilateral joint risk control, and the method comprises the steps: firstly constructing a multi-sensor multi-target scene, and obtaining data related to sensors and targets; secondly, on the basis of data related to a sensor and a target, a bilateral joint risk control model is constructed by integrating a target identification risk and a target long-term tracking cost; and finally, solving the bilateral joint risk control model by using an improved artificial bee colony algorithm, and obtaining a multi-sensor long-term scheduling distribution scheme through one-to-one correspondence of obtained solutions. According to the method, the situation that the measurement information of the sensors is affected due to the fact that accurate risk assessment among the targets cannot be achieved due to the fact that the number of the targets is variable is made up, the risk detection capacity of the sensors and the risk estimation calculation efficiency are improved, and the optimal multi-sensor scheduling scheme is obtained.

The present invention provides a program for estimating a risk that may occur in the future to a subject, on the basis of time-series information relating to the subject.　A risk estimation assistance program according to the present disclosure includes an information acquisition procedure, a risk calculation procedure, and a risk output procedure, and is for causing a computer to execute the procedures. The information acquisition procedure acquires time-series information indicating a feature of a subject, the risk calculation procedure calculates a risk that may occur in the future to the subject on the basis of the time-series information, and the risk output procedure outputs the risk.

Embodiments present techniques for determining a list of recommended items in response to a user query. An embodiment can determine a first ordered list of items including a plurality of items stored by a content platform. Based on a reward discount parameter, a first total discounted future reward for the first ordered list of items can be determined. Based on a risk discount parameter, a first risk estimate for the first ordered list of items can be determined. Similarly, a second ordered list of items can have a second total discounted future reward and a second risk estimate. The second ordered list of items can be the list of recommended items when the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate.

The embodiment of the invention provides a risk detection method and device.The risk detection method comprises the steps that feature data generated by a target software product under multiple target feature dimensions are obtained, and the multiple target feature dimensions are obtained by analyzing historical risk data of the target software product; for any target feature dimension, performing risk analysis on feature data generated under the target feature dimension by adopting a risk analysis strategy corresponding to the target feature dimension to obtain risk estimation of the target feature dimension; and determining a risk detection result of the target software product according to the risk estimation of the plurality of target feature dimensions. According to the method, the historical risk data of the target software product is analyzed to obtain the target feature dimension with relatively high risk correlation degree with the target software, and the risk detection result of the target software product is determined through the feature data generated under the plurality of target feature dimensions, so that the risk detection accuracy can be improved.

A risk assessment method, device, equipment and storage medium based on engineering projects, relating to the technical field of risk assessment. In this method, multiple estimated risks of the project are obtained, and based on a preset method, multiple first occurrence probabilities and multiple first impact levels of the first estimated risk are obtained, and multiple second occurrence probabilities and multiple second impact levels of the second estimated risk are obtained; the first estimated risk and the second estimated risk are combined to obtain a risk group; the occurrence impact rate of the risk group is determined according to the multiple first occurrence probabilities and multiple first impact levels, and the multiple second occurrence probabilities and multiple second impact levels; it is determined whether the occurrence impact rate is less than or equal to the preset occurrence threshold; when the occurrence impact rate is less than or equal to the preset occurrence threshold, it is confirmed that there is a correlation between the first estimated risk and the second estimated risk in the risk group. Implement the technical solution provided by the present application, analyze the correlation between risks, and obtain a more accurate risk estimation result.

The invention discloses an asset loss rate estimation method, and relates to the technical field of financial risk management, and the method comprises the following steps: S1, data acquisition: obtaining internal data and external market data of a target customer, and obtaining a guarantee mode, pledge details and present value data of a target debt item; s2, determining default probability and subject rating: based on the internal data and the external market data of the target customer in the step S1, calculating future one-year default probability (PD) of the target customer by using a Merton model; mapping the default probability to a standard Puer subject rating, and outputting an internal subject rating according to an internal and external rating mapping rule; according to the technical scheme of the invention, the default probability prediction based on the Merton model and the default loss rate calculation based on multi-model fusion are organically combined through the integrated automatic evaluation process, and the full-link automatic measurement from data input to risk output is realized by means of a standardized rating mapping mechanism, so that the accuracy and efficiency of risk estimation are remarkably improved.

The invention discloses a training sample data set construction method with privacy protection and a multi-classifier, sensitive information in training data is protected by constructing a multi-hidden-label mechanism, firstly, a label space division module is utilized to divide a label set into sensitive labels and non-sensitive labels, and the sensitive labels and the non-sensitive labels are classified according to a multi-hidden-label strategy; and dividing the labels into disjoint hidden label groups to form hidden label input. And then, an unbiased risk estimation module is adopted to calculate model loss, and effective training of a multi-class classifier is realized on the premise that real label explicit exposure is not needed. Furthermore, a learning strategy based on two-step optimization is provided, the first step is to optimize the positive risk item, and the second step is to optimize the negative risk item, so that the model recognition capability is improved. According to the method, a multi-hidden label mechanism is introduced, while a plurality of sensitive labels are effectively hidden, clear supervision information is still provided for part of non-sensitive labels, a plurality of sensitive and non-sensitive class labels can be effectively recognized, and privacy protection and model learnability are both considered.

The invention relates to the field of data management, in particular to an enterprise risk early warning method and system based on multi-source data fusion. Comprising the following steps: receiving an internal document and a knowledge base, extracting an enterprise data set, and constructing a dynamic knowledge graph; in the influence propagation model based on the time-space diagram neural network, executing multi-layer iterative message passing based on a double-layer attention mechanism, and generating a health state vector; inputting the health state vector into a multi-task decoding framework, and purifying the features of each risk task through an advisory depolarization network to generate a risk specific vector; enabling the risk specificity vector to pass through a plurality of task solution terminals to obtain a risk estimation value; and the influence propagation model is updated according to the difference value between the risk estimation value and the real loss through a conflict resolution optimizer based on gradient projection. According to the method, systematic risk opening quantification is carried out through a double-layer attention mechanism, and pseudo-correlation interference in multi-source data is reduced through an adversarial depolarization network.

The invention discloses a stomach cancer postoperative prognosis evaluation system based on nonlinear Cox, and relates to the technical field of medical information, and the system comprises a data acquisition module which is used for obtaining stomach cancer postoperative pathological characteristic data of a target object; the nonlinear survival risk modeling module is used for processing the pathological feature data on the basis of a nonlinear Cox proportional risk model fused with Gaussian process prior and generating scores representing individualized continuous survival risks; the risk curved surface construction and boundary extraction module is used for constructing the continuous survival risk scores into a three-dimensional risk curved surface or a contour map and extracting an equal-risk contour boundary with clinical stable boundary significance; and the prognosis result output module is used for mapping the continuous survival risk score into risk layering information and outputting a prognosis evaluation result. According to the scheme, the complex nonlinear coupling effect between the lymph node load and the tumor infiltration depth is finely depicted, and the accuracy of risk estimation is improved.

This invention discloses a postoperative prognostic assessment system for gastric cancer based on a nonlinear Cox proportional hazards model, belonging to the field of medical information technology. The system includes: a data acquisition module for acquiring postoperative pathological feature data of the target subject; a nonlinear survival risk modeling module for processing the pathological feature data based on a nonlinear Cox proportional hazards model incorporating Gaussian process priors, and generating a score characterizing individualized continuous survival risk; a risk surface construction and boundary extraction module for constructing the continuous survival risk score into a three-dimensional risk surface or contour map, and extracting isorisk contour boundaries with clinically stable demarcation significance; and a prognostic result output module for mapping the continuous survival risk score to risk stratification information and outputting the prognostic assessment result. This application's solution achieves a fine characterization of the complex nonlinear coupling effect between lymph node burden and tumor invasion depth, improving the accuracy of risk estimation.

A computer-implemented method, an apparatus, and a computer program product for estimating an aortic rupture risk are disclosed. The method comprises: obtaining imaging data comprising three-dimensional imaging data representing an aorta of a subject; obtaining additional data related to the subject, wherein the additional data comprises multi- dimensional stress data of the aorta of the subject; fitting an aortic structural model to the imaging data to determine a geometry of the subject's aorta, and processing the determined geometry and the multi-dimensional stress data by a first estimator configured to determine a first aortic rupture risk estimate; processing the imaging data by a second estimator configured to determine a second aortic rupture risk estimate, wherein the second estimator has been trained using training data comprising three-dimensional imaging data of a plurality of aortas; determining a combined aortic rupture risk estimate based on the first aortic rupture risk estimate and the second aortic rupture risk estimate; and outputting the combined aortic rupture risk estimate and including the indicative rupture direction of the aorta.

Disclosed techniques include receiving a user model as a computational graph and a list of risk metrics indicating types of risk factors to assess, and selecting at least one composable wrapper for wrapping the user model based on a risk metric(s) in the list. The techniques further include determining a set of graph-level transformations to apply to the computational graph representation that implement a risk estimate for the user model, applying the set of graph-level transformations to modify the operations performed by the user model, and generating, based on the transformed computational graph, a modified executable variant of the user model that, when executed within a machine learning framework, produces: outputs that preserve the structure and interpretation of the user model outputs; and corresponding uncertainty estimates. Still further, the techniques include executing the modified executable variant risk-aware variant of the user model to produce prediction output(s) and / or corresponding uncertainty estimate(s).

In order to estimate the risk of occurrence of a disease using sensor data measured in accordance with leg movement, this disease risk estimation device comprises: an acquisition unit that acquires sensor data measured in accordance with movement of the legs of a subject person who is the subject of disease risk estimation; a risk estimation unit that estimates a disease risk related to a specific disease using the acquired sensor data; and an output unit that outputs disease risk information corresponding to the estimated disease risk.

The invention relates to the technical field of medical data processing, and discloses a diabetic nephropathy risk assessment method and model. The method includes collecting time sequence medical data of a diabetic patient, including blood detection values, anthropometric data, and diagnosis and treatment records. For each point in time, feature elements are extracted and a temporary risk estimate is calculated. And connecting a historical medical database, matching similar patient categories, and correcting the temporary estimation value by using the risk history to obtain a unified risk estimation value. And calculating a difference value between the temporary estimation value and the unified estimation value as an accuracy index. And predicting the future nephropathy risk grade of the patient in combination with the accuracy index and the characteristic time sequence change. According to the method, individual assessment is calibrated through group historical data, the built-in accuracy index is generated, and the accuracy of risk assessment and the reliability of clinical decision making are improved.