72 results about "Risk exposure" patented technology

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

A method for predicting risk exposure includes receiving a particular set of data acquired via a sensor. The method for predicting risk exposure further can include analyzing, via a machine learning (ML) model, the particular set of data. The analyzing can include determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern. The ML model can be trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure also can include predicting a risk exposure for a user based on the particular set of data. The method for predicting risk exposure also can include providing a notice indicating the risk exposure.

The invention provides an investment and research transaction multi-strategy parallel execution method, and relates to the technical field of financial data analysis, and the method comprises the steps: accessing multi-source real-time data from a stock market, a future market and other financial markets, and obtaining a standardized multi-source heterogeneous data set; generating a multi-strategy transaction signal, outputting a strategy coordination instruction set, generating strategy instance operation state data, sending a transaction instruction generated by each strategy to a transaction system of a corresponding market for execution, obtaining transaction execution result data, generating risk open assessment data and a strategy consistency report, and performing risk open assessment based on an optimized strategy parameter set. And executing the steps of data access, factor analysis, strategy generation, independent execution and optimization in a loop iteration manner. Through the method and the device, the technical problems of high transaction delay and instable income caused by high coupling degree among multiple market strategies, poor execution isolation and difficulty in realizing cross-market strategy collaboration and risk unified management and control in the prior art are solved; and the transaction execution efficiency and the strategy stability are improved.

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.

The invention relates to the technical field of data processing, and discloses a pharmaceutical enterprise financial risk identification method, which comprises the following steps: constructing a dynamic panoramic financial behavior database through real-time fusion of multi-source heterogeneous data; using an intelligent algorithm to automatically detect an abnormal cash flow sequence significantly deviating from the health model; performing deep association mining on the same-period financial events and constructing a causal association map; dynamically quantifying a risk open value and a conduction path based on Monte Carlo simulation; generating a structured early warning report real-time push decision node; according to the invention, full-process dynamic management and control from data fusion, risk identification, cause tracing to quantitative early warning are realized, and the problems of low efficiency, narrow coverage, easy omission and difficulty in capturing complex and modeled anomalies due to the fact that financial cash flow anomalies are identified mainly depending on manual report checking or simple threshold alarm in traditional financial risk management are solved. And the active prevention and control capability of an enterprise on financial service risks such as cash flow breakage, debt paying crisis and operation interruption is obviously improved.

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.

The invention relates to a multi-modal active safety margin evaluation and checking method and device and a storage medium, which are applied to the technical field of electric power spot market, and the method comprises the steps: constructing a state judgment-index self-adaption evaluation framework, and when a system is in a risk state, calculating an interval overlapping index to quantify the risk exposure degree; when the system is in a safe state, a safe distance index is calculated to quantify safe redundancy, the multi-modal index system overcomes the defect that information is unilaterally evaluated by a traditional single index, refined and differentiated evaluation on the degree of risk and the degree of safe redundancy is achieved, and more comprehensive information support is provided for scheduling decision making; by comparing the calculated index with the preset threshold value, an active checking signal which can be directly fed back to a market clearing model is generated, or a clear suggestion is provided for a dispatcher, so that the problem of disjunction between evaluation and decision in the prior art is solved, and integrated intelligent cooperation from state perception, quantitative evaluation to closed-loop correction is realized.

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 embodiment of the invention provides an over-claim critical point pricing method and device, equipment and a storage medium, and the method comprises the steps: obtaining a target insurance policy, and determining a target risk exposure curve matched with the target insurance policy from predetermined risk exposure curves; according to the target risk exposure curve and the target insurance policy, determining the insurance premium of the original insurance policy; the corresponding specified item premium limit is not set for the premium of the original insurance policy; the specified premium quota at least comprises one of the claim-free quota and the responsibility quota; based on the target risk exposure curve, and a preset over-claim starting point and a preset over-claim stopping point, determining a proportion of an over-claim insurance premium in the original insurance premium in the original insurance premium; according to the average loss ratio corresponding to the target insurance policy and a preset expected profit rate, determining an actually received insurance premium coefficient; and according to the insurance premium of the target insurance policy, the proportion of the over-claim insurance premium in the insurance premium of the original insurance policy in the insurance premium of the original insurance policy, and the actually received insurance premium coefficient, determining the over-claim insurance premium of the insurance premium of the original insurance policy.

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 the technical field of risk assessment in automatic safety detection, and discloses a construction engineering potential safety hazard detection method and system, and the method comprises the steps: selectively defining a detection blind area or point-by-point path planning according to the definition of data sensed by a sensor, and determining an undetected key check point set which cannot be reached; the method comprises the following steps: selecting a physical blocking state, further accumulating a basic risk weight of the physical blocking state, dynamically generating a quantitative total risk score in combination with peripheral high-risk operation types and risk exposure duration, generating a visual risk report, and pushing the visual risk report to a management terminal. The physical blocking state is coupled with the importance of structural key points and the dynamic influence of external high-risk operation, so that an invisible detection blind area is converted into a quantifiable decision basis capable of reflecting the process that risks are accumulated along with time, and the crossing from physical perception to risk semantics is realized.

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 risk assessment method for the public security industry, and belongs to the technical field of public security management. The method is cyclically executed in a preset time period, and comprises the following steps of: constructing a multi-dimensional dynamic risk map: gridding an activity site, converging multi-source real-time data associated with geographic grids, and calculating a basic risk value of each grid through a multi-factor risk contribution model; a risk exposure degree deduction step: acquiring a real-time police force position, calculating a police force response delay index of each grid, and performing coupling calculation to obtain a risk exposure degree index of each grid; and a police scheduling suggestion generation step of generating a police scheduling suggestion aiming at minimizing the total risk exposure degree of the whole field through a resource optimization allocation algorithm based on the risk exposure degree index. The technical problem that in the prior art, risk assessment and police dispatching are separated from each other, and on-site dynamic changes cannot be handled is solved.

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.

A method for predicting risk exposure can include receiving data from a sensor. The method for predicting risk exposure also can include analyzing the data via a machine learning (ML) model. The analyzing can include determining that the data represents a light exposure pattern correlated with a risk pattern. The ML model can be trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure further can include predicting a risk exposure for a user based on the analyzing the data. The method for predicting risk exposure further can include providing a notice indicating the risk exposure, as predicted. Other embodiments are disclosed herein.

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 a huge disaster risk data processing method and device and electronic equipment, and is applied to the technical field of data processing. According to the invention, by acquiring the reinsurance huge disaster risk detail data for the specified dangerous unit dimension at the specified deadline, the target insurance data of which the original self-remaining amount is greater than the preset amount threshold can be analyzed and screened out in detail, and then the address and the price of each subject matter are accurately acquired. And reasonable splitting is performed according to the insurance policy product type, so that each insurance object data can reflect the real risk exposure condition, and the accuracy and processing efficiency of the huge disaster risk exposure data are remarkably improved.

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 invention discloses a logistics storage resource integration system and method based on big data, and relates to the technical field of logistics storage analysis, and the method comprises the steps: collecting the historical operation data of a warehouse, analyzing the accident characteristics between two regions, analyzing the risk association strength according to the accident characteristics between the regions, and constructing an undirected graph based on the risk association strength; analyzing the risk exposure degree and the inherent vulnerability degree of each region; combining the centrality status and hub attribute of each region and the risk profile vector of each region; identifying operation activities to be performed in each area in the planned period; simulating the propagation process of the risk impact event to the associated region along the edge, and predicting the accumulated wave and risk amount received by each region in the whole plan period; adaptively correcting the weight of the risk feature of each region; analyzing the comprehensive risk evaluation value of each region, generating a recommendation sequence, and sending the recommendation sequence to an administrator; and the analysis accuracy 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.