44 results about "Risk exposure" patented technology

The invention discloses an Internet of Things monitoring management method and system based on deep learning, and relates to the related technical field of Internet of Things monitoring management, and the method comprises the steps: reading node multi-source data through an intelligent agent, constructing a deep causal structure field through cross-modal deep causal reasoning, and revealing the relationship between environmental inducements, state paths and risk propagation; constructing a space-time causal tensor representing dynamic risk distribution of a monitoring area, quantitatively evaluating monitoring income, energy consumption cost and risk exposure, executing game analysis under a multi-party game strategy, and establishing an intelligent agent behavior strategy; and updating the monitoring network topology, executing deep space-time-cause and effect joint reasoning, generating an exception explanation result, and performing exception report management. According to the invention, the technical problems of lack of causal interpretability, resource allocation rigidness and insufficient abnormal response capability of Internet of Things monitoring in the prior art are solved, and the technical effects of accurate abnormal management of Internet of Things nodes and improvement of resource utilization efficiency and risk decision interpretability are achieved.

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.

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group—regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. A solution for this failing is described wherein a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data is disclosed, specifically, a blended topic modeling and natural language processing-based approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches.

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 invention provides a global asset pricing method considering a cross-market overflow effect and heterogeneity adaptation, and the method comprises the steps: constructing a three-layer heterogeneous graph covering global macroscopic nodes, regional market nodes and individual share asset nodes, and achieving the multi-level message passing through a heterogeneous graph neural network; end-to-end modeling of nonlinear overflow effects among global markets, among large-class assets and among individual stocks is realized for the first time; by designing a feature embedding layer, an interaction adaptation layer and a normalization layer exclusive to the market, adaptive adaptation is carried out for system features, data distribution and pricing logic of different markets, and stable and accurate pricing of mature markets and emerging markets is realized; according to the method, global systematic risk information and regional specificity alpha information are effectively separated and deeply fused through a double-attention aggregation mechanism of a global attention branch and a local attention branch, so that the income prediction precision is improved, and the systematic risk exposure degree and a regional alpha score can be directly output.

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 invention discloses a water resource collaborative robust scheduling method considering supply and demand dual uncertainty, which comprises the following steps of: evaluating risk exposure contribution rates of incoming water and demand water to system risks, and constructing an asymmetric incoming water and demand water robust uncertainty set according to the risk exposure contribution rates; a supply and demand risk coupling amplification index is calculated, the joint uncertainty space is screened and reconstructed based on the index, and a joint scene tree is generated; inputting the scene tree into a water resource hierarchical collaborative scheduling model, performing cross-period optimization by using a global resource allocation layer, and determining a staged risk quantification parameter representing the risk constraint strength of each stage; and a local scheduling decision-making layer is utilized to take the parameters as cross-layer risk constraints, and a dynamic scheduling strategy meeting global risk control requirements is generated. Through risk scene construction and cross-layer risk explicit conduction driven by a physical mechanism, the problems of excessive conservative and extreme risk defense failure in the scheduling process are effectively solved.

Methods, systems, and computer storage media for providing data security posture management using a sensitive data reclassification engine in a security management system. Data security posture management provides security operations—including identifying and remediating risk exposure—to securely manage data, resources, and workloads. In operation, sensitive data scan results are accessed. A scanned data item having a first data sensitivity confidence score—and a first data classification that indicates that the scanned data item is a potentially sensitive data item—is identified in the sensitive data scan results. Based on the scanned data item having the first data classification, scanned metadata comprising a sensitive data attribute is accessed. Based on a second data sensitivity confidence score associated with the sensitive data attribute and the scanned data item, the first data classification is replaced with a second data classification that indicates that the scanned data item is a sensitive data item.

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.

Risk identification and management approaches are described. Real time risk identification and mitigation capabilities enable the provision and deprovision of access to various resources. This capability incorporates Just-In-Time (JIT) and Just-Enough-Access (JEA) elevations to adjust access permissions in real-time based on the risk landscape. In some embodiments, the risk landscape may include human and non-human users.

The invention provides an information system asset risk assessment method and device and a medium, and relates to the technical field of computers. The method comprises the following steps: acquiring multi-dimensional risk assessment information of information system assets, wherein the multi-dimensional risk assessment information comprises open port information and component protection information; obtaining the risk value of each dimension of the information system asset according to the risk assessment information of each dimension, including obtaining the asset exposure risk value according to the open port information and obtaining the component protection risk value according to the component protection information; and obtaining a comprehensive risk score of the information system assets according to the multi-dimensional risk value, and carrying out risk early warning on the information system assets according to the comprehensive risk score. According to the method, two dimensions of the asset exposure degree and the asset protection condition are introduced into asset risk calculation, risk quantification is realized from a two-way perspective of risk exposure and protection offset, and the reliability of asset risk assessment is improved.

The application discloses a kind of water resource collaborative robust scheduling method considering supply and demand double uncertainty, comprising: the risk exposure contribution rate of risk of system risk of inflow and water demand is evaluated, and asymmetric inflow and water demand robust uncertainty set is constructed accordingly;Supply and demand risk coupling amplification index is calculated, based on the index, the joint uncertainty space is screened and reconstructed, and a joint scenario tree is generated;Scenario tree is input into water resource hierarchical collaborative scheduling model, inter-period optimization is carried out using global resource allocation layer, and stage risk quantification parameter representing risk constraint strength of each stage is determined;The parameter is used as cross-layer risk constraint by using local scheduling decision layer, and a dynamic scheduling strategy that meets the requirements of global risk control is generated.The application effectively solves the problems of over-conservatism and extreme risk defense failure in the scheduling process through risk scenario construction driven by physical mechanism and cross-layer risk explicit conduction.

The invention discloses a full-dimension asset liquidation method and system, and the method comprises the steps: building an enterprise group topological relation graph, comprehensively integrating the equity control and non-equity association relation, and carrying out the layer-by-layer penetration verification of the internal transaction flow, quantification of the guarantee responsibility expected present value, and restoration of the real contribution structure of a fund pool based on the equity control and non-equity association relation. A multi-level debt conduction path model is established, and the asset and liability conditions of the individual enterprises are calibrated and reconstructed; on this basis, a value backtracking and responsibility fulfillment program is executed, and finally an assignable net value result reflecting real risk distribution is generated; the problem of unclear hidden risk open identification is effectively solved, the authenticity and traceability of the liquidation result are significantly improved, and it is ensured that the calculation of the whole net assets of the group is established on the basis of fully revealing the internal economic relation.

Techniques are described herein for reducing a risk exposure related to a software application. The techniques may comprise receiving an indication of a software application, identifying a number of components associated with that software application as well as a number of vulnerability scores corresponding to the number of components, determining, based on the number of vulnerability scores, a risk score associated with the software application, and identifying one or more updates associated with the number of components and determining a change to the risk score associated with those one or more updates. In embodiments, the techniques may further comprise receiving an indication of a threshold risk score and determining at least one of the one or more updates that, when applied to at least one of the number of components, is determined to lower the risk score to below the threshold risk score.

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.

There is provided a multi-layered risk mitigation system. The system may receive TCP packets and / or TCP streams in real-time and process packets. Packets may be enriched with metadata and additional data. A real-time risk score may be subsequently calculated using ensembles of machine learning models trained using narrow, focused datasets. Domain dataset attributes may be asynchronously analyzed and refreshed as new packets are received. ML models may be continually updated and / or refreshed. Layers of controls may be added or removed depending on the determined level of risk to dynamically balance processing efficiency with performance.

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.

The invention belongs to the technical field of quantum communication, and particularly relates to a QKD remote key distribution method, system and device based on a PQC channel and a medium. In order to overcome the defect that performance overhead and risk exposure cannot be coordinated in the prior art, the technical scheme adopted by the invention is as follows: the QKD remote key distribution method based on the PQC channel comprises the following steps: completing storage of a mother key in a server in a hardware security module in advance; a temporary PQC authentication key exchange channel with forward confidentiality and quantum attack resistance is established between a client side and a server side, the server side adopts a standard PQC digital signature to perform identity authentication, and the client side indirectly realizes identity authentication on the server side through a key packaging mechanism; the remotely distributed key is a one-time service key; and carrying out encrypted communication between the clients by using the service key. The method has the beneficial effects that the contradiction between the performance overhead and the risk exposure is solved through an asymmetric authentication protocol and a strict risk isolation architecture.

There is provided a multi-layered risk mitigation system. The system may receive TCP packets and / or TCP streams in real-time and process packets. Packets may be enriched with metadata and additional data. A real-time risk score may be subsequently calculated using ensembles of machine learning models trained using narrow, focused datasets. Domain dataset attributes may be asynchronously analyzed and refreshed as new packets are received. ML models may be continually updated and / or refreshed. Layers of controls may be added or removed depending on the determined level of risk to dynamically balance processing efficiency with performance.

There is provided a multi-layered risk mitigation system. The system may receive TCP packets and / or TCP streams in real-time and process packets. Packets may be enriched with metadata and additional data. A real-time risk score may be subsequently calculated using ensembles of machine learning models trained using narrow, focused datasets. Domain dataset attributes may be asynchronously analyzed and refreshed as new packets are received. ML models may be continually updated and / or refreshed. Layers of controls may be added or removed depending on the determined level of risk to dynamically balance processing efficiency with performance.

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 application relates to the technical field of data monitoring. A risk monitoring method based on a dynamic threshold early warning comprises the following steps: acquiring risk trigger information in multiple scenes, acquiring credit type scenes, transaction type scenes and association type scenes according to the risk trigger information, judging whether the credit type scenes, the transaction type scenes and the association type scenes enter a risk monitoring state, acquiring structured monitoring data of the multiple scenes if the risk monitoring state is entered, and acquiring historical credit change data of the credit type scenes, real-time transaction flow data of the transaction type scenes and associated party risk exposure data of the association type scenes according to the structured monitoring data; and acquiring cross-scene risk characteristics according to the historical credit change data and the real-time transaction flow data. The application integrates credit type scene data, transaction type scene data and association type scene data, covers multiple-dimensional risk sources, avoids risk omissions caused by single data source analysis, and comprehensively controls systemic risks.

A system and method for managing financial data using a context-aware data framework for real-time capital control are disclosed. The method includes receiving, at a plurality of distributed processing nodes (106a, 106b, 106c . . . 106n), a plurality of financial transaction data streams from a plurality of distributed data sources (104a, 104b, 104c . . . 104n). The method includes generating the context-aware data framework. The method includes detecting one or more variations in one or more transaction context, assessed risk exposure, and capital flow conditions. The method includes updating one or more database states. The method includes executing one or more real-time capital control operations within the context-aware data framework. The method includes replicating, execution of the one or more real-time capital control operations, the updated one or more database states across the plurality of distributed processing nodes. The method includes generating one or more immutable contextual log records within the context-aware data framework.

The present application relates to the technical field of risk assessment in automated safety detection, and discloses a construction engineering safety hidden danger detection method and system, comprising the following steps: according to the sensor sensing data definition, selectively determining a set of unexamined key check points which cannot be reached by defining a detection blind area or point-by-point path planning; then, accumulating the basic risk weight thereof, combining the surrounding high-risk operation type and risk exposure time, dynamically generating a quantitative total risk score, and generating a visual risk report and pushing it to a management terminal, wherein the present application couples the simple state description of physical obstruction with the importance of the structural key points and the dynamic influence of external high-risk operation, so as to convert an invisible detection blind area into a quantifiable decision basis which can reflect the risk accumulation process over time, and realizes the leap from physical perception to risk semantics.

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.