2117 results about "Risk IT" patented technology

Certain aspects of the disclosure provide a method for generating context-augmented responses. In aspects, the method includes receiving a job application; extracting, from the job application, a question; generating, using an embedding model, a vector representation of the question; identifying, within a vector database, a most similar vector to the vector representation of the question; extracting, from a question-response database, an encoded context group; retrieving, from a job applicant database, professional context of the job applicant; generating, based on the context group and the professional context, a prompt chain for causing a language model to generate a response; processing, using the language model, the prompt chain to generate the response; calculating, using a risk scoring model, a risk score for the generated response; and in response to the risk score being below a predetermined threshold, populating a portion of the job application with the response.

A computing platform performs automated due diligence by acquiring project scope information and documents, standardizing all data across all document types into a uniform format optimized for analysis and subjecting the data to repeated querying by large language model agents to triangulate it against a body of human content expertise coded into the platform. The platform compares variables with a project proforma, verifies permits by parsing permit data and cross checking a permitting authority portal, evaluates design completeness by detecting standards of readiness status, and measures the bankability of project agreements compared to industry standards. The platform computes environmental, social, and governance metrics, assesses counterparty risk and community sentiment using searches of public sources, and validates a reported capital stack by analyzing documents and applying industry standards thereto. The platform calculates risk metrics, applies weights to produce two scores, and generates a report that includes remediation steps.

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 belongs to the crossing field of network space security, software engineering and artificial intelligence, and discloses a dynamic and static cooperative vulnerability full-link intelligent analysis and verification method based on a large language model and an MCP protocol, and the method comprises the steps: S1, task initialization and context construction; s2, target code deep analysis and static taint preanalysis are carried out; s3, carrying out Fuzzing Harness directional generation and iterative optimization on the basis of the stain paths; s4, performing dynamic and static collaborative sandbox execution and dynamic stain tracking; and S5, vulnerability accessibility judgment and report output of multi-source data fusion are carried out. According to the method, a dynamic and static combination mechanism is adopted, vulnerability analysis and verification can be automatically completed for software target codes, and accurate technical support is provided for vulnerability risk grading and repairing priority ranking.

The invention relates to a multi-factor dynamic correction investment benefit intelligent evaluation system, in particular to the field of investment evaluation, and aims to capture macroscopic event and project risk index impact in real time and dynamically generate factor impact coefficients through an event-driven factor correction module, so that the response speed and sensitivity of a model to project sudden change are remarkably improved, and the evaluation efficiency is improved. The self-adaptive weight optimization module utilizes a deep reinforcement learning framework to continuously optimize factor weight distribution based on revenue after risk adjustment in a rolling time window so as to realize self-adaptive adjustment of an investment strategy and optimization of a revenue-risk ratio, and the abnormal conduction early warning module constructs a dynamic directed graph and combines a graph neural network technology to realize early warning of the abnormal conduction. According to the method, abnormal factor conduction paths can be perspectively recognized, systematic engineering risks can be effectively warned, a strategy execution module fuses weight optimization results and risk warning signals, rebalance is executed under the condition that investment cost and risk control constraints are strictly met, and finally a project configuration scheme giving consideration to revenue, robustness and real-time performance is generated.

Embodiments described herein provide an AI guardrail system that integrates an agentic compliance evaluation framework with an agentic governance ontology framework to automate AI risk assessment and management. For the compliance evaluation framework, embodiments provide a Digital Risk and Compliance Officer (DRCO) agent built upon an LLM to verify AI compliance in an AI system based on a knowledge base managed by an AI governance ontology. For the governance ontology framework, embodiments provide an AI Governance Ontology (GO) agent built upon two LLMs that generate and update a knowledge base representing concepts, relationships, and metadata related to the governance of AI systems based on AI system generated conversation transcripts.

This invention relates to the field of construction safety monitoring and intelligent visual analysis technology, specifically a wiring-free construction safety monitoring system based on visual algorithm analysis. The system includes: an independent power supply module, a visual perception module, a scene risk topology assessment module, a computing-risk coupling scheduling module, a multi-level wake-up execution module, and a context-related early warning output module. It collects initial visual flow data, remaining power, and predicted charging power of the target monitoring area; divides the topology regions into different risk levels through semantic segmentation and assigns risk weights; dynamically generates computing power scheduling instructions containing the target sampling frame rate and target model complexity; outputs the target violation event identification results; and generates the final early warning result by combining the spatial inclusion relationship between the occurrence location and the topology region boundary. This achieves priority protection for high-risk spatiotemporal nodes and reduces ineffective energy consumption and invalid alarms in low-risk scenarios.

This application provides a data processing method, apparatus, device, and medium. The method includes: acquiring a first text containing business text data; performing a risk assessment on the first text to obtain a risk category result corresponding to the first text; if the risk category result is a first risk category, acquiring keywords of the business text data and searching for the keywords in a standard database; if a request text matching the keywords is found in the standard database, determining the feedback text corresponding to the request text as the business processing result corresponding to the business text data; if no request text matching the keywords is found in the standard database, performing text search processing on the business text data in a target knowledge graph to obtain a business processing result matching the business text data. Implementing this application embodiment can improve the security of text data.

The present application is applicable to the technical field of artificial intelligence. Provided are a method for training a driving risk recognition model, and a driving risk recognition method and apparatus. The method comprises: acquiring data to be processed of a plurality of drivers; determining corresponding events on the basis of the data to be processed; textualizing identifiers of the drivers and the events corresponding to the drivers to obtain textualization results; and on the basis of the textualization results, training a driving risk recognition model to be trained to obtain a trained driving risk recognition model. The driving risk recognition model to be trained is a large language model, and the trained driving risk recognition model is used for outputting a driving risk. The model trained by means of the above method is more accurate.

The application discloses a failure analysis method and related device, electronic equipment and storage medium, wherein the failure analysis method comprises: detecting based on the description document of the target agent to obtain the design elements of the target agent; analyzing based on the failure mode knowledge base and each design element to determine the first element suspected of existing failure risk and the first risk degree of the first element in each design element; selecting the first element as the second element based on the first risk degree of each first element; generating the test session and the adjudication rule of the second element based on the attack strategy knowledge base and the second element; determining the test reply of the second element based on the adjudication rule of the second element to obtain the second risk degree of the second element. The above scheme can identify the failure risk of the agent in advance before the agent goes online, and quantize the failure risk.

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.

The application discloses a kind of energy router cascade failure defense and recovery method, comprising: obtaining the operating state data of multiple ports of energy router, wherein current control mode of each port is contained;Based on operating state data, by constructing a dynamic coupling matrix dependent on real-time control mode, to assess the cascade failure risk of system level, obtain system cascade failure risk index;In response to risk index over-limit or actual failure occurs, by solving an optimization problem with control mode as the core decision variable, generate and execute control instructions for suppressing cascade failure propagation;In response to fault clearance, execute risk-aware ordered recovery process, and perform closed-loop safety review after each step of the recovery sequence to ensure the stability of the recovery process.The application can improve the accuracy of cascade failure risk prediction, the effectiveness of active failure defense and the safety of system failure recovery.

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 present application relates to the technical field of geotechnical slope safety, and particularly relates to a soft interlayer-containing bedding slope displacement dynamic safety early warning method and system based on time sequence LSTM. By constructing the step-by-step excavation process of the slope as a time sequence and introducing a long short-term memory neural network model capable of effectively learning long-term dependencies, the accurate simulation of the displacement dynamic cumulative response of the soft interlayer-containing bedding slope during the excavation process is realized. The method effectively overcomes the shortcomings of the existing technology, such as the inability of the limit equilibrium method to reflect time sequence effects, the low calculation efficiency and difficulty in real-time application of the numerical simulation method, and the inability of the static machine learning model to capture the step-by-step excavation path dependence characteristics. The advantage lies in short calculation time, fast iteration speed, and the ability to realize real-time and dynamic prediction of the displacement at each step during the excavation process, thereby providing timely and reliable technical support for slope safety management and risk early warning decision-making during engineering construction, greatly improving the timeliness and accuracy of early warning.

Embodiments of the present application provide a smuggling transit risk assessment method and an electronic device, the method comprising: determining a first risk corresponding to each target object; determining at least one region condition that is satisfied by a target region among a plurality of preset region conditions, and determining a second risk coefficient; scaling the second risk coefficient to obtain a second risk after increasing; and aggregating the second risk to obtain a third risk of smuggling occurring in the target region. The application can dynamically adjust the risk coefficient according to the region condition of the target region when the target object appears in the target region, so as to improve the accuracy of the determined second risk coefficient, and further make the scaled risk coefficient more consistent with the real smuggling scene, so that the risk of smuggling occurring in the target region can be more accurately evaluated.

This application relates to the field of data security protection technology, specifically a dynamic data protection method based on intelligent classification and risk adaptation. The method includes: acquiring the data flow chain, recording the data flow relationship between the source Topic, target Topic, and processing nodes, and generating a link identifier; performing field parsing and two-level sensitivity identification on the input data to obtain the field sensitivity level and generate an initial residual risk vector at the field level; calculating the effective residual risk at the field level node by node during the data flow process and aggregating it to form a data risk label; determining policy entries and target processing operators based on the data risk label, path length, node processing type, and local link summary characteristics; and performing at least one operation on the target field: character replacement, interval truncation, tokenization, and envelope encryption. This method enables dynamic risk characterization and adaptive protection during the data flow process.

This invention discloses an intelligent document generation system and method for the judicial field, relating to the field of document analysis technology. The generation method includes the following steps: performing structured analysis on historical judicial documents and extracting elements from the documents; establishing relationships between different elements; constructing a logical chain for the document based on the relationships between elements in any judicial document; comparing the logical chains of different judicial documents and identifying elements that exclude logical chains; generating relevant legal provisions matching the elements based on real-time collected case elements and logical chains; identifying the most applicable provisions and generating an initial document based on the exclusion of logical chains; evaluating the confidence level of each element in the initial document; optimizing the text of the initial document based on the evaluation results and providing risk warnings for potential elements; thereby improving efficiency while ensuring the professional quality and judicial security of the final document.

The application discloses a kind of based on measurement and knowledge water surface unmanned system operating state early warning method, belong to unmanned system safety monitoring technical field.To solve the problem of insufficient knowledge utilization, poor dynamic adaptability of existing early warning method, the method of the application includes: collecting position, attitude, ship body state and environment multi-source time series data, and obtaining text form expert knowledge;Adopt sliding window and standardization preprocessing data;Expert knowledge semantic vector is extracted using BERT model;Through Crossformer model coding time series features, knowledge vector is spliced and fused, and system state features are output;Risk membership degree is calculated based on fuzzy set theory, and early warning is triggered according to the maximum membership principle.The application realizes the deep integration of measurement data and expert knowledge, improves the early warning accuracy, robustness and generalization ability.

Provide a system. 【Solution means】 Means for analyzing information generated by an information sender by an information analysis device and evaluating risks based on legal and ethical standards; Means for presenting an amendment to the information sender based on the risk assessment; Means for continuously monitoring external reactions to information transmitted by an information transmission device and detecting abnormal reactions; Means for generating a response text based on past data and providing it to the information sender when an abnormal reaction is detected; Means for analyzing the collected external reactions and providing a summary thereof to the information sender; Means for enhancing natural language processing using a generation AI model for the analysis of posted information and the generation of amendments; Means for dynamically adjusting the input prompt text to the generation AI model to improve analysis accuracy; A system including the above.

This application discloses an automatic scheduling optimization method for engineering project inspection plans, relating to the field of scheduling optimization. The method includes: when the risk accumulation density exceeds a first preset threshold, splitting the original global inner wall inspection task into an emergency local inspection task covering the corresponding continuous pipe segment interval and a regular inspection task covering the remaining pipe segment, and generating split task data; when the risk accumulation density in multiple consecutive outer wall inspection cycles is lower than a second preset threshold, updating the trigger priority of the global inner wall inspection task and postponing its execution time, generating postponed task data; acquiring inspection cost data, including detection tool call cost, task waiting cost, and risk retention cost; and outputting a dynamic scheduling scheme including execution coordinate range, execution time, task priority, and detection tool type based on the split task data, postponed task data, and inspection cost data. This application has the effect of improving scheduling optimization efficiency.

An example computer system for providing a communication system can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: monitor criteria associated with the computer system; calculate a risk score associated with the communication system; and automatically change one or more endpoints of the computer system when the risk score exceeds a threshold.

The application relates to the technical field of operation and maintenance cross-domain access, in particular to a cross-domain access control method of a distributed operation and maintenance audit system, which comprises the following steps: obtaining a high-risk degree according to the high-risk confidence of an instruction and a global consequence performance value, and then obtaining an asset sensitivity degree; obtaining a real-time abnormality index according to the current high-risk prominence degree of a security domain and the mean value of the high-risk degrees of various instructions within a first preset time; obtaining a real-time sensitivity degree based on the real-time abnormality index and the asset sensitivity degree; obtaining a cross-domain sensitive change degree based on the real-time sensitivity degrees corresponding to the initiation domain and the target domain of an instruction; obtaining a cross-domain risk degree based on the cross-domain sensitive change degree, the maximum value of the real-time sensitivity degrees in the security domains involved by an access link corresponding to the instruction and the historical maximum high-risk degree of the instruction; and executing a corresponding level of release strategy on the instruction according to the cross-domain risk degree. The application can significantly enhance the overall security and active defense capability of cross-domain access.

This invention proposes a multi-energy safety assessment method and system considering multiple time scales and coupled faults. The method includes: establishing an intermediate time-scale model and a long-time-scale model of the integrated energy system; conducting a safety assessment for each fault scenario in a fault scenario set; wherein, using the long-time-scale model to perform an N-1 safety scan of the fault scenarios considering coupled faults, calculating system risk assessment indicators; when the system risk assessment indicators reflect that the fault scenario may lead to water hammer consequences, using the intermediate time-scale model to calculate the overpressure value generated during the water hammer process; and generating the final safety assessment result of the integrated energy system based on the safety assessment results of each fault scenario. This invention can capture the water hammer effect of the heating network and the cascading faults caused by the coupling of the logical and geographical layers, which are often overlooked in traditional assessments, achieving a more comprehensive and accurate safety assessment of the integrated energy system and helping to improve the operational safety of the system.

The present application relates to a kind of methods for realizing network content risk analysis based on key points driving, comprising the following steps: depth semantic analysis is carried out to original text, and risk key points are identified and extracted;According to the preset confidence threshold filtering low reliability key points, calling big model intelligent screening risk key points;Multi-source evidence online retrieval is carried out, and multi-source search results are integrated and risk tendency determination;Multi-dimensional risk research and judgment is carried out.The method for realizing network content risk analysis based on key points driving, device, processor and computer readable storage medium thereof of the present application are adopted, by reconstructing analysis process with risk key points as core, and comprehensively deciding by depth fusion local semantic knowledge and external real-time information, surpassing existing technology in precision, explainability and flexibility etc.Dimensions, provide solid technical foundation for constructing next-generation intelligent, efficient, reliable network content risk prevention and control system, and have important practical value and innovation.