20 results about "Risk exposure" patented technology

Systems and methods provide agentless security assessment for workloads and cloud posture control across multi-cloud environments. Discovery modules are configured with collection intervals to ingest posture control data including assets, identities, configurations, activities, network flows, and build-time artifacts. A multi-cloud configuration inventory maintains current and historical states and produces misconfiguration and identity-activity findings. For workload vulnerability evaluation, an external snapshot manager obtains point-in-time root-disk state without installing an in-workload agent. A file system data processor derives operating system and package metadata, and a detector matches the metadata against a vulnerability feed refreshed on a recurring basis to identify vulnerabilities. Identified vulnerabilities are correlated with misconfiguration and activity findings to generate prioritized risk exposures. Results are stored per workload and presented through graphical user interfaces that display risk levels, timelines, alerts, and guided remediation, enabling continuous, low-overhead security coverage for cloud workloads and configurations.

This invention relates to the field of blockchain and smart contract application technology, specifically a smart contract-based anti-counterfeiting information verification system for commodity vouchers, comprising: a request receiving module for receiving commodity voucher verification requests sent by terminals; a network status monitoring module for acquiring node computational status data and consensus delay data, and quantifying and generating a consensus friction entropy index; a graph feature analysis module for extracting transaction flow graph data and generating risk exposure assessment results; a state machine decision-making module for combining the consensus friction entropy index and the risk exposure assessment results to output a target verification level; and an adaptive verification execution module for executing corresponding verification logic, updating the commodity voucher verification status, and adjusting smart contract resource consumption parameters to provide feedback control over the consensus friction entropy index of the blockchain network, thereby achieving coordinated control of verification efficiency and system operational security.

This application relates to a method and system for intelligent policy analysis and risk monitoring based on a family graph. The method includes: acquiring multi-source policy data and performing structured processing; constructing a family policy relationship graph based on the relationships between policyholders and insured persons in the dataset; acquiring a predefined risk scenario library, containing multiple preset risk scenarios and corresponding coverage types for each scenario; calculating the total coverage amount based on the family graph by identifying related policies for each risk scenario; acquiring a risk exposure benchmark value, comparing the total coverage amount with the benchmark value, and generating a quantitative result of the coverage status; combining a pre-built insurance product knowledge graph, generating coverage optimization suggestions based on the quantitative results; and re-executing the calculation and generation operations in response to user lifecycle events or periodic assessment instructions, and generating risk warning notifications based on the analysis of result changes. This application achieves intelligent analysis of family policies, dynamic risk monitoring, and coverage optimization, improving risk management efficiency.

The application provides an intelligent analysis method and system based on a WMS system and an AI large model, relates to the field of warehouse management, and comprises the following steps: converting WMS business data into description text, identifying and labeling abnormalities by using an AI model, and constructing a causal knowledge graph associated with risk weights. Based on the graph, a candidate decision scheme is generated by multi-path reasoning, and the feasibility and risk exposure of the candidate decision scheme are evaluated to select the optimal scheme. Finally, the scheme is simulated for risk propagation and a blocking measure is generated. The application realizes the automatic identification, quantitative evaluation and intelligent decision of warehouse operation risks, and improves the timeliness and accuracy of risk response.

This invention belongs to the technical field of computer network security and relates to a method and system for assessing vulnerabilities in network communication transmission nodes. The method includes: matching vulnerabilities based on static attributes of communication nodes, analyzing their exploitation prerequisites and impacts to generate an asset knowledge base; analyzing network topology and access control permissions to generate potential communication topologies; collecting information such as session connections and process activities, and encapsulating it into critical state transition events; determining whether the transition events meet the vulnerability exploitation prerequisites and updating the logical state; generating dynamic attack paths based on the updated logical state and communication topology, constructing a complete attack graph, and calculating a network-wide vulnerability risk score; simulating intervention measures for nodes with risk exposure values ​​greater than a preset threshold, and generating defense hardening strategies based on changes in the risk score. This invention addresses the problem of insufficient accuracy and timeliness in risk assessment results.

The invention relates to a resource-pooling system and to a corresponding method for risk sharing of a variable number of risk exposure components. The risk exposure components are connected to the resource-pooling system by means of a plurality of payment receiving modules configured to receive and store payments from risk exposure components for the pooling of their risks. The total risk of the pooled risk exposure components comprises a first risk contribution associated to risk exposure in relation to loan losses, and a second risk contribution associated to risk exposure based on emergency expenses. The pooled risk is divided in a parameterizable risk part and a non-parameterizable risk part by means of an indexing module. In case of triggering a loss by means of a trigger module, the suffered loss is covered by releasing associated loans and emergency expenses of the risk exposure components.

The application discloses a kind of compliance verification method and system of financial service process, it is related to financial data compliance verification technical field, receives the service innovation core of financial service party and risk self-assessment report, extracts business uncompromising service innovation point, adjustable business operation details, mark potential compliance risk exposure of business and corresponding associated regulatory policy range;Based on public regulatory documents and historical regulatory decision data, extract regulatory core intent and distinguish red line intent and flexible intent, form regulatory intent list.The application realizes the accurate alignment of regulatory core intent and service innovation core, breaks through the limitation of traditional technology only matching the surface of regulatory clause, solves the problem of innovation business compliance judgment ambiguity from the root.The potential conflict intensity and compliance comprehensive matching degree are clearly defined by quantitative formula, subjective experience judgment is replaced, the objectivity and accuracy of compliance verification are improved, and human error is reduced.

The present application relates to a kind of intelligent futures and options fusion risk control analysis system and method, method includes: risk factor identification module real-time analysis collected data, identify the main risk factor that current influence market, and pass through AI risk prediction engine operation prediction model;Risk prediction index is calculated, and Monte Carlo simulation method is used to evaluate futures and options combination risk;The exposure of each risk dimension is calculated, risk exposure analysis is carried out, and key indicators are monitored in real time to give early warning;When triggering early warning, automatically execute emergency response.The present application realizes the combination risk evaluation of futures and options fusion, the combination risk of futures position and spot position is calculated by Monte Carlo simulation method, accurately assesses hedging effect, avoids the risk underestimation or overestimation problem caused by separation evaluation.Test shows that the VaR accuracy of combination risk evaluation is improved by 35% than separation evaluation, and effective risk capital occupancy is reduced by 20%.

This invention relates to the field of real-time audit monitoring technology and discloses an adaptive learning real-time audit monitoring system. The system includes a data acquisition module for determining the operational stability of audit objects based on operational behavior characteristics; a pattern recognition module for determining a learning adaptation strategy based on operational stability and determining the correlation between behavior and risk based on rule matching degree; a rule adaptation module for determining the determination method of rule optimization value based on risk exposure rate under the corresponding strategy and determining its compliance based on response lag; an anomaly determination module for determining the compliance of behavior and risk based on the risk conversion ratio of the monitoring node; and a system calibration module for determining whether to increase the optimization value, increase behavioral constraints, or decrease the risk threshold when the rule optimization value or behavior does not comply with the risk, based on the difference in response lag, the ratio of the risk conversion ratio to the preset conversion ratio, or the degree of difference. This system achieves real-time and accurate audit monitoring, improving risk identification and response capabilities.

A plurality of climate models for a variety of hazards and / or initial conditions to produce a collection of Hazard Exposure (Risk) Maps. The Hazard Exposure Maps are transformed to high resolution (and may be filtered) and convolved with auxiliary data related to one or more hazards. The now super resolution maps are input to a learning engine along with spatiotemporally harmonized historic events and active near real-time events to produce a calibrated model (Asset Level Exposure Risk Estimation Model) that utilizes the super resolution maps and asset location(s) to project risk for each asset. The projected risk may be provided to asset operators / owners, initiate signal alerts and other messages, invoke automated responses to protect / preserve assets. The risk results may also be grouped in risk valuation datasets.

This invention discloses a method for assessing health risks of outdoor thermal environment in urban residential areas during summer, comprising the following steps: S1: Obtaining microclimate parameters for the assessment period through on-site testing or theoretical calculation; S2: Calculating key thermophysiological variables of heat exchange between the human body and the environment; S3: Using the microclimate parameters and the key thermophysiological variables as inputs, obtaining the instantaneous thermal risk index T-HRI, and classifying health risk levels according to a preset threshold; S4: Using the microclimate parameters and the key thermophysiological variables as inputs, obtaining the cumulative thermal risk index C-HRI, and classifying health risk levels according to a preset threshold, while simultaneously estimating the cumulative high-risk exposure probability P. This invention can comprehensively consider the intensity of human metabolism and thermophysiological regulation processes, identify the moment of irreparable thermal stress generation, and classify and evaluate the instantaneous and cumulative thermal risks in the outdoor thermal environment.

The present application belongs to the technical field of quantum communication, and particularly relates to a QKD remote key distribution method, system, device and medium based on a PQC channel. In view of the fact that the prior art fails to coordinate performance overhead and risk exposure, the present application adopts the following technical solution: a QKD remote key distribution method based on a PQC channel, comprising: pre-storing a parent key in a service end in a hardware security module; establishing a temporary, forward-secure, anti-quantum-attack PQC authentication key exchange channel between a client and the service end, the service end performing identity authentication by using a standard PQC digital signature, and the client indirectly implementing identity authentication of the service end through a key encapsulation mechanism; the remotely distributed key is a one-time business key; and the business keys are used for encrypted communication between clients. The present application has the beneficial effect of resolving the contradiction between performance overhead and risk exposure through an asymmetric authentication protocol and a strict risk isolation architecture.

A method for predicting risk exposure can include receiving a set of data collected via a smart ring. The method also can include analyzing, via a trained machine learning (ML) model, the set of data collected via the smart ring to determine whether the set of data collected via the smart ring (a) represents an impairment pattern or (b) correlates to a high-risk pattern, wherein at least one of the impairment pattern or the high-risk pattern correlates to a risk exposure. The method for predicting risk exposure further can include generating a notification to alert a user of the risk exposure. Other embodiments are disclosed.

This invention relates to the field of power asset management technology, and in particular to a power asset management method and system based on an event-equipment correlation model. It collects multi-source heterogeneous data from the power asset operating environment, generates a standardized event-equipment interaction dataset, constructs a dynamic causal correlation graph of events and equipment, employs a temporal causal hypergraph attention network combined with counterfactual interference minutiae, outputs a pure causal correlation weight matrix, and generates an event-equipment correlation model. This model deduces the equipment performance degradation trajectory and remaining useful life probability distribution under target event-driven conditions, quantifies the marginal benefits and risk exposure values ​​of assets at different operation and maintenance intervention nodes, solves for the optimal asset scheduling sequence through deep reinforcement learning, generates power asset management decision schemes, and achieves collaborative closed-loop evolution of the model and decisions based on online meta-learning. This invention effectively improves the intelligence level of power asset management and ensures the safe, stable, and economical operation of the power grid.

The application relates to the field of business prediction and management, and provides an industry chain intelligent correlation analysis early warning method based on multi-source heterogeneous data and a corresponding product. The method comprises the following steps: generating a standardized data set from collected multi-source heterogeneous data; based on the standardized data set, identifying entities and the relationship between entities in the industry chain through naming entity recognition and relationship extraction technology, and dynamically constructing and updating an industry chain knowledge graph taking entities as nodes and relationships as edges; taking the industry chain knowledge graph as input, combining the space-time attribute characteristics attached to the entities and the relationships, and outputting simulation results containing the risk exposure value of each node and the risk transmission path; according to the risk exposure value, combining the time sequence index and the centrality index, and calculating the comprehensive risk vulnerability index of each industry chain node through a multivariate regression model; and based on the comprehensive risk vulnerability index and the simulation results, generating differentiated early warning signals and pushing the signals through a visual interface.

The application belongs to the field of computer and Internet finance, and discloses a digital financial fraud risk intelligent identification method based on space-time feature driving, which comprises the following steps: S1. Collecting credit original data of a target user; S2. Building a "six-level data governance and real-time mapping" mechanism for pre-processing combined features derived from step S1 to solve the problems of multi-source heterogeneity, field missing, distribution drift and dynamic evolution of graph structure in the credit scenario; S3. Building a three-channel heterogeneous model; S4. Inputting the credit data pre-processed in step S2 into the three-channel heterogeneous model built in step S3 to complete the default probability inference, and then outputting the final risk score through the "strong leading type weighted fusion" strategy. The method can analyze the financial indicators, behavior sequence and potential associated risks of the borrower in real time, pre-identify high-risk individuals and fraud gang networks, advance the risk discovery to the pre-loan approval link, and reduce the credit loss and risk exposure of financial institutions from the source.

The application discloses a kind of based on power distribution network security situation Multistage fuzzy comprehensive evaluation method, it is related to power grid safety evaluation technical field, including acquisition power distribution network real-time operation data, environmental information and topological structure, combine history operation sequence to construct time series prediction model, output contains the dynamic safety margin sequence of key security index;Based on dynamic safety margin sequence, time attenuation cumulative operator is applied to the historical fuzzy membership degree of bottom layer security index, and the cumulative membership vector representing risk exposure intensity is generated;Call pre-built power distribution network knowledge graph, according to the semantic scene recognition of current operating state of power distribution network and the dynamic safety margin sequence, generate the multistage safety evaluation index subset adapted to current running context;Cumulative membership vector is mapped to the bottom layer security index of the multistage safety evaluation index subset, and the membership function with adjustable boundary parameter is constructed, and the fuzzy membership vector of bottom layer security index under preset comment set is output.

This invention discloses a method for constructing a dynamic model for value assessment and risk pricing of urban renewal asset securitization, including acquiring underlying asset data of target urban renewal projects. The invention employs machine learning algorithms to capture the nonlinear characteristics of cash flows and utilizes the Copula function to characterize the correlation between default rate, prepayment rate, and recovery rate. Monte Carlo simulation is used to generate cash flow paths that conform to actual risk distributions, overcoming the limitations of traditional linear models and independent assumptions, and significantly improving prediction accuracy. A time-varying correction function is constructed by introducing macroeconomic stress test factors and policy adjustment coefficients, enabling the pricing model to adjust in real time according to market conditions and policy guidance. A risk sensitivity index matrix is ​​calculated to comprehensively quantify risk exposure, and Bayesian posterior probability methods are used to verify the model's confidence using real market transaction data, ensuring the robustness and reliability of the value assessment results and providing a scientific basis for investment decisions.