134 results about "Risk profile" patented technology

The invention provides a hospital infection prevention and control related data online management method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the dynamic graph calculation of an infection transmission path through a multi-modal deep learning model, and recognizing a high-risk department, equipment and personnel interaction mode, so as to obtain a real-time calculation result; generating a dynamic risk threshold based on the real-time calculation result, and generating a risk event vector; mapping the risk event vector into an executable supervision task list, and calculating a final task allocation path through a reinforcement learning algorithm; based on the final task distribution path, constructing a department-level risk portrait and a hospital-level risk topological graph; expected loss values of different prevention and control strategies are calculated through Monte Carlo simulation, and a department specific prevention and control scheme is generated; and when a multi-drug-resistant bacterium propagation risk is detected, automatically associating historical data of related departments, and calculating a final isolation region division scheme. The false alarm rate can be reduced.

The invention relates to the technical field of food traceability, and discloses a food safety knowledge graph system, which comprises an acquisition module used for acquiring multi-modal data of food in stages to form traceability data; the risk portrait module is used for constructing a multi-dimensional risk portrait of the food; the feature fusion module is used for fusing the features of the traceability data to generate feature representation; the block chain evidence storage module is used for storing risk portraits and traceability data; the risk prediction module outputs a prediction result according to the risk prediction model; and the AI decision center module is used for optimizing a prediction result of the risk prediction model and outputting a food safety knowledge graph. According to the method, multi-modal data of links such as production, transportation and storage are collected, the feature fusion module fuses features of the traceability data based on a weighted multi-head attention mechanism, feature representation is generated, and analyzability of traceability information is enhanced. And multiple data sources are weighted and fused, so that the system can capture risks more accurately, and the omission ratio is reduced.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention provides an intelligent case division method and system for unhealthy assets, and the method comprises the steps: carrying out the preprocessing of multi-source unhealthy asset data, constructing a case feature set of the unhealthy assets based on the preprocessed data, carrying out the classification and grading, predicting the risk score of the unhealthy assets through a pre-trained dynamic repayment capability prediction model, and obtaining the risk score of the unhealthy assets. Constructing a borrower risk portrait of the unhealthy assets, determining case types of the unhealthy assets based on the borrower risk portrait and preset features, determining initial case division strategies of the unhealthy assets based on the case types and a preset collection strategy library, and optimizing the initial case division strategies through a reinforcement learning mode, and on the basis of the optimized case division strategy, performing case division on the non-performing assets. The method can reduce the manual intervention of the case division link of the unhealthy assets, improves the disposal efficiency of the unhealthy assets, greatly shortens the disposal period of the unhealthy assets, and reduces the unnecessary disposal cost of the unhealthy assets.

The invention discloses an engineering building construction quality analysis system and method based on data processing, and belongs to the technical field of commercial data processing and intelligent decision, and the system comprises a causal relationship graph construction module which is used for fusing multi-source monitoring data to construct a causal relationship graph; the risk profile construction module is used for performing reverse deduction and forward simulation and constructing a prospective quality risk profile; the multi-target collaborative optimization module is used for solving a Pareto optimal equilibrium solution set; the quality pre-control instruction generation module is used for instantiating a dynamic quality pre-control instruction; the efficiency evaluation and attribution correction module is used for updating the causal relationship graph; and the evolution auditing and causal knowledge distillation module is used for auditing historical updating of the atlas and distilling a core causal rule set. According to the method, risk deduction and simulation are carried out by constructing the dynamic causal relationship graph, and multi-objective optimization and closed-loop feedback correction are combined, so that prospective identification and collaborative intelligent pre-control of the project quality risk can be realized.

A system and method may collect telematics and / or other data, and apply the data to insurance-based applications. From the data, an insurance provider may determine accurate vehicle usage information, including information regarding who is using a vehicle and under what conditions. An insurance provider may likewise determine risk levels or a risk profile for an insured driver (or other drivers), which may be used to adjust automobile or other insurance policies. The insurance provider may also use the data collected to adjust behavior based insurance using incentives, recommendations, or other means. For customers that option to the data collection program offered, the present embodiments present the opportunity to demonstrate a low or moderate risk lifestyle and the chance for insurance-related savings based upon that low or moderate risk.

Example embodiments of the present disclosure provide for an example method including receiving, from privacy agents deployed by an agent deployment engine, system metric data. The system metric data can include signals associated with compute resources based on settings or functions of a respective compute resource of the compute resources. The method can include generating a risk profile by comparing the received system metric data to current privacy state requirements. The method can include, based on the risk profile, performing an action such as (i) updating a user interface to display a notification relating to the risk profile or (ii) generating and initiating a configuration file to adjust compute resource settings.

A fraud detection system preemptively identifies and mitigates fraudulent financial transactions in real-time across a diverse range of digital and physical transaction sources. Transaction data may be acquired from various platforms and categorized into rule sets specific to transaction types. Categorized data is aligned with existing risk profiles to construct a dynamic hysteresis model. This model, evaluated by a decision tree algorithm, identifies potential fraud by integrating immediate transaction details with a comprehensive historical data analysis, thus enabling advanced trend analysis and pattern recognition. Key features identified by the decision tree are used to form a heuristics model, which is then analyzed by a transformer network risk model. A feedback loop enhances the system's effectiveness by incorporating decision outcomes back into the model training server, thus refining the training dataset and continuously improving the accuracy of the risk model.

The application belongs to the technical field of bidding risk assessment, and specifically provides a bidding risk assessment method and system based on big data, which mainly comprises the following steps: collecting basic identification data of bidders, historical bidding record data, real-time behavior log data and external environment index data, and generating an integrated initial data set through multi-source data integration processing; performing data cleaning and standardization processing on the integrated initial data set to obtain purified standard data; performing risk feature extraction on the purified standard data to form feature risk indexes; and performing risk linkage analysis by using the feature risk indexes to construct a comprehensive risk profile. The application can realize efficient integration and in-depth analysis of multi-dimensional data of bidders, accurately identify risk abnormalities, highlight key risk signals, reduce the probability of decision-making misjudgment, and provide intuitive and visual risk assessment results to assist decision-making.

Disclosed embodiments include receiving network data associated with a first system of a network. The network data may comprise first data, second data, third data, fourth data, fifth data, and sixth data. The method may quantify, the first data, the second data, the third data, the fourth data, the fifth data, and the sixth data. The method may further determine, a risk parameter based on the quantifying. The method may generate, a vulnerability risk profile for a vulnerability based on the risk parameter. The vulnerability profile may indicate a security weakness of the first system or the second system. The method may determine, based on the security weakness of the first system or the second system, a remediation protocol for minimizing the security weakness of the first system of the network or the second system of the network.

The invention discloses a special gas holder safety control method and system based on multi-parameter monitoring, and the method comprises the steps: collecting the gas concentration, environmental parameters, equipment state and other multi-dimensional data inside and outside a special gas holder through a distributed sensor network, carrying out the adaptive filtering and time sequence feature extraction, and constructing a dynamic risk portrait in combination with a process situation. An LSTM-VAE hybrid model, a gas reaction knowledge graph, an improved D-S evidence theory and a pipeline topology propagation model are utilized to calculate an abnormal score, a gas interaction risk score and a space risk score respectively, a comprehensive risk value is generated through dynamic weighted fusion, risk grades are divided, and a hierarchical control strategy is triggered. When the risk is abnormal, leakage source positioning and gas mixing traceability are automatically started, and a diagnosis report is generated. The system realizes closed-loop management of monitoring, early warning, response and traceability, integrates a data driving method and a knowledge driving method, and improves the risk identification accuracy and the disposal intelligent level.

In some implementations, a cybersecurity enforcement system may generate a synthetic phishing attempt targeting a user. The cybersecurity enforcement system may update, based at least in part on a mode of the synthetic phishing attempt, a risk profile specific to the user.

According to an embodiment, disclosed is a system comprising a processor configured to define, one or more risk categories for monitoring a risk associated with a clinical trial, wherein the risk categories comprise one or more risk elements; calculate, a first risk profile data of the risk categories based on a risk factor and a weighting assigned to the risk elements; generate, a machine learning (ML) model; train, the ML model; receive, a second risk profile data; analyse, the second risk profile data to identify a pattern based on the first risk profile data using a database; predict, an overall risk score; recommend, one or more of a type of monitoring, a level of monitoring, and the overall risk score; and wherein the ML model comprises a feed-back layer to enable continuous learning and improve the prediction of the overall risk score and monitoring decisions of the clinical trial.

The present invention discloses a quality verification method and system for silicon carbide purification processing based on a deep model, which relates to the field of silicon carbide inspection. The quality verification method for silicon carbide purification processing based on a deep model includes the following steps: classifying monitoring data to obtain classified data; obtaining association rules related to silicon carbide product defects; based on a deep learning model, combining the obtained association rules and parameter features in the classified data to predict the defect type and probability of the silicon carbide product; quantifying the predicted defect type and probability of the silicon carbide product to obtain a quantitative score, and combining the quantitative score with a risk index to calculate an overall risk score and construct a risk profile. The present invention combines the characteristics of the association rules and the characteristics of the classified data to improve the model's prediction accuracy for the defect type and probability of the silicon carbide product, quantifies the prediction results into a risk score, and combines them with other risk indicators to provide an intuitive quality verification evaluation indicator.

A system and a method for data protection assessment using threat modelling and adaptive optimization is disclosed. The system (100) comprising a processor (105) and memory (110) with instructions to receive a data access request (345) from entities (120) via authenticated digital interface (125) including structured and unstructured data categories (350), metadata parameters (355), initiating contextual analysis to establish baseline compliance parameters (360). The system analyses data category and metadata using adaptive classification logic and correlation metrics across stored data lineage, distinguishing privacy -critical from non- sensitive data. Threat modelling (130) evaluates correlations among privacy attributes (365), metadata, operational parameters, and regulatory requirements to identify vulnerabilities (375) and exposure points. A composite risk index (135) is derived, forming a structured risk profile (140) with quantified scores (380). Compliance recommendations (150) are generated, safeguard parameters (155) are derived, authorization validated by a compliance authority (160), and a compliance trace (388) is recorded.

A method includes: receiving a first message from a first agent, the first message comprising a request to transfer a virtual asset to a recipient identifier, associated with a second agent, from a sender identifier associated with a sender blockchain address; for the first agent, accessing a set of policies and a set of transaction characteristics of transactions associated with the first agent; characterizing a compliance state and a risk profile of the first agent based on the set of policies and the set of transaction characteristics; transmitting a second message to the second agent, the second message including the compliance state of the first agent and the risk profile of the first agent; receiving confirmation from the second agent to proceed with transfer based on the first compliance state and the first risk profile; and releasing a recipient blockchain address associated with the recipient identifier to the first agent.

There is provided a method of determining a risk profile for a dwelling. The method includes: determining whether the dwelling is being monitored, the type and extent of dwelling sensors within the dwelling, and the extent to which a security alarm system of the dwelling is activated; adjusting the risk profile in response to said determining; sending the risk profile so adjusted to an insurance provider or broker; and obtaining an insurance quote based on the same. There is also provided a security alarm assembly including dwelling sensors, a security alarm system in communication with the dwelling sensors, and a processor. The processor is configured to determine the number and type of said dwelling sensors, determine the extent to which the security alarm system is activated, determine whether the security alarm assembly and / or the dwelling sensors are monitored, and calculate a risk profile based on the same.

Systems and methods for payment token provisioning with variable risk evaluation are disclosed. In one embodiment, a method may include: an issuer backend: (1) receiving, from an electronic wallet application, a payload comprising an identification of a card to be provisioned to the mobile electronic device; (2) determining that the card is eligible for provisioning to the mobile electronic device; (3) generating a card payload and communicating the card payload to the payment network, wherein the payment network creates a payment token for the card comprising payment token origin information; (4) receiving the payment token from the payment network and generating a risk profile for the payment token; and (5) activating the payment token in response to the validation.

This invention provides a multinomial label generation method and apparatus for risk profile construction, characterized by the following steps: S1-S3, dividing all features into multiple sub-feature sets, and selecting features from each sub-feature set to construct a filtered feature set based on importance value, relevance value, and feature selection quantity; S4, performing multinomial cross-feature calculation on the features in the filtered feature set to obtain a multinomial feature set; S5, calculating the importance value, relevance value, and feature selection quantity of each feature in the multinomial feature set; S6, selecting multiple features from the multinomial feature set based on importance value, relevance value, and feature selection quantity to construct a multinomial filtered feature set; and S7, calculating the WOE value of each feature corresponding to each target in the multinomial filtered feature set. In summary, this method can describe the multidimensional risk status of a target with fine granularity.

A computer-implemented method, according to one embodiment, includes mapping hosts in communication with a storage system to compartment constructs that are logical partitions of the storage system, analyzing interoperability of the hosts and the compartment constructs and defining, based on the analysis, risk profiles for applications run on the hosts. Ownership of storage objects to the compartment constructs is assigned based on the risk profiles, where each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

Methods and systems for generating an attack path based on user and system risk profiles are presented. A method comprises determining user information associated with a computing device; determining system exploitability information of the computing device; determining system criticality information of the computing device; determining a risk profile for the computing device based on the user information, the system exploitability information, and the system criticality information; and generating an attack path based on the risk profile. The attack path indicates a route through which an attacker accesses the computing device. The system exploitability information is associated with or based on one or more of the vulnerability associated with the computing device, an exposure window associated with the computing device, and a protection window associated with the computing device. The system criticality information is associated with or based on one or more assets and services associated with the computing device.

A method includes: receiving a first message from a first agent, the first message comprising a request to transfer a virtual asset to a recipient identifier, associated with a second agent, from a sender identifier associated with a sender blockchain address; for the first agent, accessing a set of policies and a set of transaction characteristics of transactions associated with the first agent; characterizing a compliance state and a risk profile of the first agent based on the set of policies and the set of transaction characteristics; transmitting a second message to the second agent, the second message including the compliance state of the first agent and the risk profile of the first agent; receiving confirmation from the second agent to proceed with transfer based on the first compliance state and the first risk profile; and releasing a recipient blockchain address associated with the recipient identifier to the first agent.

The present application relates to the technical field of surface defect detection, and particularly relates to a surface detection method for shaft part machining, which screens a defect suspected area according to a cross-section diameter parameter obtained in each detection area along a characteristic direction, screens a risk profile according to a distribution coefficient of a texture profile, marks a first risk sub-profile or determines a characteristic risk profile based on a texture representation value of the risk profile, determines a second risk sub-profile according to a characteristic profile point and screens the characteristic risk profile, constructs a defect trend vector according to the risk profile to determine a profile trend representation coefficient, adjusts an illumination parameter for defect detection of the characteristic risk profile, and performs defect detection on the characteristic risk profile again based on the adjusted illumination parameter. The present application realizes rapid identification of a region with a risk of shadow obstruction, adaptively adjusts a detection mode according to actual defect characteristics of shaft parts, and improves the precision and reliability of surface defect detection.

The present invention provides a label generation method and device for risk profile construction, which has the following characteristics: Steps S1-S3: dividing all features into multiple sub-feature sets, selecting features from the sub-feature sets based on importance values, correlation values, and feature screening amounts, and constructing corresponding screening feature sets; Step S4: for each sub-feature set, inputting the values ​​of all features corresponding to each target in the sub-feature set into a machine learning model, and combining the risk judgment label corresponding to the target to obtain the predicted value corresponding to each target in the sub-feature set; Step S5: for each sub-feature set, merging the features of the corresponding screening feature set and the predicted features to obtain the corresponding model feature set; Step S6: calculating the woe value of each feature corresponding to each target in the model feature set. In short, this method can generate multiple labels and corresponding label values ​​for each target to improve the accuracy of risk profiles.

A digital system development lifecycle system processes approval requests for applications. The system generates a risk profile using a questionnaire and selects a set of deliverables based on the risk profile. The set of deliverables is tuned based on regulatory or enterprise policies. The system provides a user interface via which users can view progress towards the tuned set of deliverables.

A computer system for analyzing and mitigating risks associated with an event is provided. The computer system is configured to: (i) receive at least one of a city risk profile and a building risk profile from a database; (ii) receive city systems data from the city services computer system; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the event; (iv) generate an event risk profile that includes the at least one potential risk associated with the event; and / or (v) generate a risk mitigation output based upon at least one of the city risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a building, and a user are also provided.

A system and method for a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

Systems and methods for payment token provisioning with variable risk evaluation are disclosed. In one embodiment, a method may include: an issuer backend: (1) receiving, from an electronic wallet application, a payload comprising an identification of a card to be provisioned to the mobile electronic device; (2) determining that the card is eligible for provisioning to the mobile electronic device; (3) generating a card payload and communicating the card payload to the payment network, wherein the payment network creates a payment token for the card comprising payment token origin information; (4) receiving the payment token from the payment network and generating a risk profile for the payment token; and (5) activating the payment token in response to the validation.

A method includes: receiving a first message from a first agent, the first message comprising a request to transfer a virtual asset to a recipient identifier, associated with a second agent, from a sender identifier associated with a sender blockchain address; for the first agent, accessing a set of policies and a set of transaction characteristics of transactions associated with the first agent; characterizing a compliance state and a risk profile of the first agent based on the set of policies and the set of transaction characteristics; transmitting a second message to the second agent, the second message including the compliance state of the first agent and the risk profile of the first agent; receiving confirmation from the second agent to proceed with transfer based on the first compliance state and the first risk profile; and releasing a recipient blockchain address associated with the recipient identifier to the first agent.