75 results about "Risk profile" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

The invention relates to the technical field of artificial intelligence security and cloud computing resource management, and discloses an AI reasoning service elastic scaling method and system for an edge cluster, and the method comprises the steps: constructing a dynamic risk file for each hardware computing unit in a computing cluster; distributing the reasoning service request to a target hardware calculation unit of which the risk state is matched with the security demand level; instantiating an independent logic calculation container for each allocated reasoning service request, and destroying the logic calculation container after the reasoning task is executed; and monitoring the resource configuration change of the computing cluster, and updating the risk state of the computing cluster in the dynamic risk file. According to the method, a mixed protection mechanism with logic isolation as a main part and physical reset as an auxiliary part is adopted, and hardware-level state pollution attacks triggered by antagonistic input are effectively defended in a dynamic elastic edge AI reasoning scene on the premise that the elastic scalability is not obviously sacrificed.

A patient management system and method for providing a risk profile and care plan to a patient and provider. The patient management system includes a risk analysis module configured to convert unstructured data to structured data and includes a data acquisition module for collecting and transmitting unstructured data and a classifier module for analyzing unstructured data and converting the unstructured data into structured data. The data acquisition module receives the unstructured data set, and the classifier module converts the unstructured data to a structured data set. A provider interface system is in communication with the classifier module and is configured to collect a plurality of structured data sets, analyze the collected structured data sets and output a risk profile / group to a provider.

Different versions of a program are executed in a first mode of operation in accordance with normal operations with only expected behavior to generate different acceptable behavior models indicative of behavior of the program. A difference between acceptable behavior models indicates different risk profiles for the different versions of the program. Risk profiles can be generated by a model explainer. An event that attempts to execute remote code or a delayed event can be detected in a second mode of operation if the event or the delayed event is not included in the acceptable behavior model. Different sequences of events in the first or second mode can be instrumented at different levels of instrumentation to generate or use the acceptable behavior model. The sequences of events can be generated from a stacktrace in the first and second modes. The instrumentation can be performed in hardware by intercepting SYSCALLs.

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.

A cyber threat defense system can incorporate data from an instant messaging platform with multiple other platforms in a client system to identify cyber threats across the client system. The system can have one or more instant messaging modules to collect instant messaging data from one or more network entities that utilizes one or more instant messaging platforms. A user specific profile module can identify a user of the client system associated with the user account based on a composite user profile constructed from user context data collected across multiple platforms of the client system. A risk profile module can associate the user with a user risk profile based on the composite user profile. The risk profile module can apply one or more artificial intelligence classifiers to the instant message based on the user risk profile. A cyber threat module is configured to identify whether the instant messaging data corresponds to a cyber threat partially based on the user risk profile. An autonomous response module can execute an autonomous response in response to the cyber threat factoring in the user risk profile.

To provide a system for analyzing risk profiles of users by utilizing generated AI and proposing an optimum insurance plan.SOLUTION: A means for the user to input risk profile information, a means for the server to receive and store the risk profile information in a database, a means for the server to transmit the risk profile information to a generative artificial intelligence, a means for the generative artificial intelligence to analyze the risk profile information and select an appropriate insurance plan, a means for the generative artificial intelligence to return the selected insurance plan to the server, a means for the server to present the returned insurance plan to the user, a means for the user to customize the insurance plan and finalize the final insurance plan, a means for the server to receive and store the finalized insurance plan in the database, a means for the server to transmit the finalized insurance plan to the insurance company system, and a means for the server to notify the user of the completion of the insurance application.SELECTED DRAWING: Figure 1

A system for controlling over-the-air updates for a device (i) stores a plurality of version and setting information for plurality of devices; (ii) receives version and setting information for the first device of the plurality of devices; (iii) stores the version and setting information for the first device; (iv) determines a risk profile for the first device based upon the corresponding version and setting information; and (v) determines an insurance quote based upon the risk profile for the first device.

This invention relates to the field of CNC turning technology, specifically to a method and system for planning variable depth of cut turning paths to suppress chatter in thin-walled ring turning. The method includes: constructing a variable network path model integrating four modes: constant depth of cut, linear gradual change, sinusoidal variable depth of cut, and stiffness feedback adaptive variable depth of cut; generating vibration response using a digital twin of the thin-walled ring-tool system and extracting a risk profile containing a comprehensive chatter risk index and modal risk boundary parameters; optimizing the depth of cut parameters of key nodes using a path value function and a three-layer nested evolutionary game of macro-meso-micro levels; and performing local reconstruction iteration on brittle node groups to obtain a variable depth of cut path that balances agility and robustness. This achieves precise suppression of chatter in thin-walled ring turning, significant improvement in machining stability, and high efficiency.

A system and method for a risk management visualization system comprises a computer having 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, and display a visualization of a risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

The application discloses a kind of hierarchical authority management method and anti-copy U disk based on double sequence number establishment, comprising: in response to equipment connection, the first sequence number of user identification and the second sequence number of equipment identification are obtained;Key is generated by combination, and operation authority is determined;Receive operation request and judge type;Sensitive operation triggers multi-factor authentication, obtains real-time geographic positioning and real-time operation sequence, input behavior characteristic analysis model calculates and the deviation degree of user history mode, risk score is generated in combination with positioning, when reaching high risk level, refuse operation and record;Non-sensitive operation is directly allowed;After allowing operation, real-time monitoring data flow rate and target address are matched with normal transmission mode library, when finding continuous data flow to unauthorised address, session is interrupted and user behavior risk profile is updated.The application realizes strong binding of identity and equipment, hierarchical verification and dynamic risk assessment, while ensuring the safety of high-sensitive operation, it also takes into account the smooth experience of routine operation.

The invention belongs to the technical field of warehouse logistics, and discloses an AGV carrying path planning method based on an intelligent forklift and a control system. Comprising the following modules: a data acquisition module used for acquiring related parameters of the intelligent forklift and a carrying task in real time; according to the method, the average speed prediction model is set, the model not only considers the bending degree and complexity of the path, but also introduces the risk composition of the path and the influence of physical properties of cargoes on the forklift dynamics, so that the average speed of cargo loading and no-load of the forklift in the path can be accurately predicted; based on the average speed of the intelligent forklifts, the total time required by a single intelligent forklift to complete a carrying task is calculated, whether the single forklift can independently complete the task within the time limit or not is judged, if not, multi-forklift cooperative scheduling is automatically started, firstly, the minimum number of needed forklifts is calculated according to the predicted total time gap and the safety coefficient, and then the minimum number of needed forklifts is calculated; and the overall throughput capacity and strain capacity of the system are improved.

The invention relates to the technical field of major activity safety guarantee, and particularly discloses a major activity safety guarantee risk management system and method based on risk portray.The system comprises a data footstone layer, an analysis, study and judgment layer, a decision action layer and a verification optimization layer, and the data footstone layer constructs a dynamic hidden danger library following the four-dimensional principle; multi-source data are gathered through a standardized model, risks are quantified based on a weighted RPN formula, and a data injection and dynamic updating mechanism is matched. And the analysis, research and judgment layer generates a risk portrait report containing a structured list through scene modeling and multi-dimensional aggregation analysis. The decision action layer converts the portraits into tactical actions such as special plans, resource presetting and special drilling. And the verification optimization layer realizes risk monitoring regulation and knowledge precipitation through closed-loop management. According to the invention, the conversion from passive response to data-driven active defense is realized, the risk identification and pre-judgment precision is improved, the resource configuration and emergency efficiency are optimized, the system self-learning evolution is driven, and the risk recurrence rate of the same kind is reduced.

This invention relates to the fields of information processing and risk management technology, specifically to a method, system, and computer-readable storage medium for dynamic risk perception based on multi-source risk-related data. The method acquires multi-source risk-related data associated with a risk subject, extracts features from the risk-related data and converts them into standardized risk feature units, vectorizes these standardized risk feature units within a preset time window to generate a risk state vector representing the current risk status of the risk subject, and updates the risk profile of the risk subject based on the risk state vectors generated at different times, thereby achieving continuous perception of the risk status.

Systems and methods for securing data for outbound communication may include: determining a risk profile for the outbound communication based, the outbound communication comprising data modules including at least a payload, an origination endpoint, and a destination endpoint; determining a scanning policy from a plurality of scanning policies, based on the determined risk profile; determining a secure machine learning model from a plurality of secure machine learning models, based on the determined risk profile, wherein the secure machine learning model is determined based on an authentication level corresponding to the determined risk profile; providing one or more data modules to the secure machine learning model, based on the determined scanning policy; receiving a sanitized version of the payload based on an output of the secure machine learning model; and providing the sanitized version of the payload for the outbound communication.

This application relates to the fields of insurance technology and data processing technology, and discloses an intelligent claims risk control method and computer equipment for internet health insurance based on multi-source data fusion. The method includes responding to claims applications and calculating historical and recent behavioral risk scores based on a customer risk profile knowledge graph; reviewing claims materials using a medical knowledge graph and object detection model, and outputting scores for unreasonable medication use and invoice compliance; obtaining relevant clauses based on a retrieval-enhanced generation framework and outputting liability determination indicators; aggregating the above scores and indicators to construct multimodal features; using an ensemble learning classifier to determine the risk level and generate a risk warning summary. By integrating knowledge graph, computer vision, and large language model technologies, it achieves deep correlation analysis of structured profiles, unstructured documents, and insurance clauses, solving the problems of sparse and poor interpretability of traditional risk control features, and improving the accuracy of risk identification and the efficiency of automated processing in claims cases.

This invention relates to the field of talent data security technology, specifically a talent data information security management system based on a human resources platform, comprising: data processing, security assessment, risk assessment, and access control modules. The data processing module integrates structured talent profiles, unstructured attachments, operation logs, and sensitive tags to form a unified record library. The security assessment module performs sensitive field extraction, privacy content identification, abnormal behavior detection, and tag compliance verification. The risk assessment module performs quantitative analysis based on the above results to generate a dynamic security risk profile. The access control module constructs and executes fine-grained access control policies in real time based on this profile, including roles, data objects, operation types, and time periods. This system achieves multi-dimensional integrated assessment of talent information security and dynamic, precise control based on real-time risks.

A method for determining a risk profile of an entity of interest, comprising interrogating a social media platform to determine a social media account corresponding to the entity of interest and generating a linked social network data structure linking the social media account corresponding to the entity of interest to one or more other social media accounts on the social media platform corresponding to other 10 entities, where an individual link is based on one or more social media interactions between the social media account corresponding to the entity of interest and each of the one or more other social media accounts corresponding to other entities. The method further involves assigning an individual link risk measure to one or more of the individual links of the linked network data structure, the individual link risk measure based on a risk assessment of the one or more social media interactions upon which the 15 individual link is based on and a risk profile of the entity of interest based on an aggregated risk measure based on the linked social network data structure comprising the individual link risk measures. 20 24 27 83 66 12 D ec 2 02 4 1 2 D e c 2 0 2 4 1 0 2 0 2 4 2 7 8 3 6 6 1 5

The invention provides a risk discovery method and device based on large model rule self-generation, and the method comprises the following steps: firstly, inputting an initial risk feature described by a natural language into a pre-trained large language model, and generating an executable risk judgment rule; secondly, performing semantic analysis and scoring on the real-time text information by utilizing the generated rule; then, establishing a dynamic risk portrait for each involved person, and calculating a personal comprehensive risk score; then, submitting the high-risk information for manual verification, generating a structured case summary, and storing the structured case summary into a knowledge base; and finally, on the basis of case summary in the knowledge base, a generation mechanism is enhanced through retrieval, and closed-loop feedback and self-iterative optimization are formed. Through the technical means of deep semantic understanding, rule self-iteration, dynamic quantitative evaluation and the like, full-process intelligentization from rule generation, individual evaluation to task backtracking optimization is achieved, and the intelligent level and efficiency of public safety early warning intervention are remarkably improved.