15 results about "Residual risk" patented technology

The invention discloses a blind area risk defense control method based on adjacent vehicle behavior inference. The blind area risk defense control method comprises the steps that a known environment risk potential energy field reflecting current known traffic constraints is constructed; a composite risk potential energy field of the view angle of the adjacent vehicles is deduced by resolving a decision sequence of the adjacent vehicles in traffic interaction; fitting and optimizing a driving style weight coefficient in a cost function through historical observation data of an adjacent vehicle; according to the driving style weight coefficient and the actually measured motion state of the adjacent vehicle, calculating an actually predicted energy residual error, and according to the actually predicted energy residual error, inferring the position information of the blind area traffic participant under the own vehicle coordinate system; non-dynamic features are extracted, and a spatial gain mask is generated according to the non-dynamic features and the position information of the blind area traffic participants under the own vehicle coordinate system; and calculating a residual risk potential energy field according to the known environmental risk potential energy field and the composite risk potential energy field, and calculating a corrected residual risk potential energy field according to the residual risk potential energy field and the spatial gain mask so as to perform blind area risk defense control.

A computing device comprising a memory and one or more processors in communication with the memory and configured to: obtain data defining a first plurality of risks for a current host; determine, a first set of residual risk scores for each risk of the first plurality of risks; aggregate the first set of residual risk scores associated with the current host to form a first aggregate residual risk score; obtain data defining a second plurality of risks of a future host; determine a second set of residual risk scores for each risk of the second plurality of risks; aggregate the second set of residual risk scores associated with the future host to form a second aggregate residual risk score; determine whether the second aggregate residual risk score is less than the first aggregate residual risk score; and migrate assets from the current host to the future host.

This invention discloses a method for coal mine terminal risk scoring based on Bayesian networks, comprising the following steps: Step 1: Collecting coal mine terminal risk data to form a terminal risk dataset; Step 2: Extracting risk variable nodes, barrier variable nodes, and terminal scoring nodes, constructing a Bayesian network, and forming a node conditional probability table; Step 3: Performing deontization, triangulation, and clique partitioning to form a connection tree and a partition set; Step 4: Calculating the probability estimate of clique nodes and the barrier state description term; Step 5: Constructing propagation messages using an improved Shafer-Shenoy algorithm to form a penetrating residual component; Step 6: Performing state enhancement marginalization to form a partition set propagation message; Step 7: Forming the terminal residual posterior risk distribution; Step 8: Forming the coal mine terminal risk scoring result. This invention improves the ability to characterize coal mine terminal residual risk and the reliability of risk scoring.

A software based application for assessing, processing, and remediating cyber-risk in real time may comprise, without limitation, a profiling component, an analytic component, an evaluation component, and a monitoring component which may, in conjunction therewith, operate to allow an organization to adaptively adjust an organization's network security to continuously improve and mature same. Such components may operate to: (1) determine an organization's operational baseline; (2) identify risks and hazards inherent therein; (3) generate, and verify the efficacy of, remedial controls to such risks and hazards; (4) document and audit such determinations; and (5) continually monitor the organization's network security. In such a manner, the network security architecture of an organization may be remediated according to threat scenario-based control efficacy and residual risk determinations according to the agnostic, risk-focused, and system-based approach disclosed herein.

The application relates to the technical field of industrial automation and intelligent manufacturing, and particularly discloses a production process alarm rule evolution method and system based on operation context awareness. By fusing multi-source real-time and historical data, a dynamic deduction model is constructed, abnormal trigger probabilities of each production unit are predicted in advance, and a candidate set of vulnerable units is generated. Based on virtual simulation and context-weighted comprehensive production loss evaluation, core intervention units are screened out. Through enumeration combination, three-dimensional priority sorting and nested process scheduling optimization verification cycles, the minimum effective intervention combination and the matching optimization strategy capable of making the remaining risk controllable are determined. The combination is verified in an online environment, and if successful, the combination is solidified as a formal alarm rule; if failed, the situation deviation is analyzed and the model is updated, forming a self-evolution mechanism of perception-evaluation-optimization-verification-evolution, so that the alarm rule can continuously adapt to the dynamic production environment.

The present application relates to an AI-assisted real-time tissue recognition and cutting path planning system for microsurgery, which includes multi-modal acquisition, registration fusion, tissue segmentation, risk assessment and path planning output modules. Microscopic videos, fluorescence images, OCT depth maps, etc. are collected, and heart rate, blood pressure, instrument pose and force feedback are synchronously acquired; after camera calibration, registration and time alignment, a fusion input is formed. The tissue recognition CNN uses spatio-temporal aggregation and cross-modal attention fusion, and sets a differentiable safety distance field layer between fusion and decoding, generates a safety distance field based on instrument embedding and OCT depth gradient, as attention bias and skip-connection gating, outputs class map, boundary map, confidence and uncertainty. Based on the confidence and uncertainty, structure, mis-cut and residual risk maps are generated; under the constraints of kinematics and cuttability, trajectories containing path points, depth and speed are output, and superimposed acoustic and light warnings or robot instructions are output.

The invention discloses an agricultural residue risk assessment method based on a Bayesian network, and relates to the technical field of agricultural residue risk early warning. The method comprises the following steps: pre-selecting risk sources and risk indexes, and obtaining expert scores of the influence degree and occurrence probability of each risk index; calculating to obtain the risk level of each risk index and the risk level of each type of risk source; constructing an initial model: constructing a Bayesian network topological structure by taking the overall risk level, the risk source and the risk index of the region as nodes; converting the calculated data into an initial probability, and importing the initial probability into a modeling tool to obtain an initial prior probability; the actually measured data is preprocessed and then combined with the initial prior probability, network parameters of the initial model are learned by using an expectation maximization algorithm to generate an optimization condition probability table, and the optimization condition probability table is imported into the initial model to obtain an early warning model; and inputting the detection data to the early warning model for risk assessment. According to the invention, scientific field risk assessment can be carried out, the method has the potential of dynamic early warning, and the pertinence and effectiveness of supervision are improved.

This invention discloses a coal mine disaster risk prevention and control platform based on multimodal perception and AI video recognition, relating to the field of video recognition technology. It includes a multi-source data acquisition module, a micro-scene construction module, a feature extraction module, a pseudo-anomaly removal module, and a linkage control module. The platform acquires video stream V, gas concentration G, wind speed W, temperature and humidity T, acoustic and vibration signals A, and roadway topology parameters L. It constructs a micro-scene unit U, extracts visual disturbance features Fv, gas response features Fg, thermal inertia features Ft, and acoustic and vibration mutation features Fa, generates a cross-modal causal consistency matrix C, and a non-disaster disturbance baseline B, obtains residual risk features R, and outputs the risk source location, propagation direction, and risk level result Y. This enables graded power outages, localized ventilation adjustments, directional spraying, personnel evacuation, and model self-updating, improving the accuracy of identifying precursors to minor coal mine disasters and enhancing linkage prevention and control capabilities.

The application relates to the technical field of document reply generation, and discloses a file reply generation system based on AI auditing, which comprises the following modules: a document analysis module, which is used for obtaining an original document, performing fragmentation, carrying out semantic vector conversion, and constructing an evidence graph; a gist mapping module, which is used for analyzing review gists into computable rules, calculating matching confidence and evidence coverage, and outputting rule mapping results; a fact resolution module, which is used for extracting fact claims, determining and resolving contradictions, and updating evidence graph entity variables; a violation judgment module, which is used for substituting rules to judge violation gists and generating a severity score list; a rectification optimization module, which is used for selecting optimal rectification measures and forming a rectification scheme table; a reply generation module, which is used for calculating residual risks and generating a reply text with regulation anchor points and evidence anchor points; and an evidence archiving module, which is used for writing into a hash chain and archiving the reply document. The application realizes automatic document auditing, reply generation and whole-process tamper-proofing and trace-keeping.

This invention discloses a SOTIF data acquisition method and related equipment. In this invention, the cloud has a large data acquisition scope, thereby reducing reliance on closed sites or professional data collection fleets, and lowering the acquisition cost of multi-source heterogeneous data. Furthermore, First Automobile Works filters its collected multi-source heterogeneous data using first Shannon entropy and residual risk values. The filtered multi-source heterogeneous data corresponds to traffic environments with high levels of disorder. Therefore, by calling First Automobile Works to collect and upload multi-source heterogeneous data, a large amount of multi-source heterogeneous data that meets the application conditions of SOTIF can be easily obtained, providing data support for SOTIF. Moreover, the SOTIF data acquisition method is executed on the vehicle side, and by uploading the filtered multi-source heterogeneous data instead of the original full-volume data, the data processing burden on the cloud is significantly reduced, improving the implementation efficiency of SOTIF. This invention has wide applications in the automotive technology field.

Embodiments of the present application provide a kind of fusion security demand generation method, electronic equipment and storage medium.The method is in the development process of heavy vehicle automatic lateral control function, define the lateral control function related item including implementation function and expected function, obtain the hazard event of lateral control function related item by hazard and operability analysis, obtain the safety target of hazard event, vehicle safety integrity level and residual risk acceptance by hazard and risk analysis method, according to vehicle safety integrity level and residual risk acceptance, obtain target risk level in risk level table, establish function safety architecture according to target risk level and safety target, obtain safety constraint condition based on function safety architecture, and safety constraint body condition is converted into target safety demand, to analyze the hazard caused by lateral control failure and expected function deficiency, improve the development efficiency of automatic lateral control function, reduce development cycle, improve reliability and safety.

The invention discloses a vehicle commercial insurance premium pricing method, system and device, a storage medium and a product, and relates to the technical field of automatic vehicle insurance, and the method comprises the steps: matching a basic rate corresponding to an automatic driving vehicle from an insurance contract library; obtaining SOTIF data of the autonomous vehicle; determining a main engine plant responsibility coefficient and a residual risk coefficient through a responsibility actuarial engine; calculating a total loss measure according to the SOTIF data; and on the basis of the basic rate, the main engine plant responsibility coefficient, the total loss measurement and the residual risk coefficient, calculating to obtain the commercial insurance expense of the autonomous vehicle. According to the method, the automatic driving commercial insurance premium actuarial model based on the SOTIF is constructed through the basic rate, the main engine plant responsibility coefficient, the total loss measurement and the residual risk coefficient, the accident responsibility can be accurately defined, the multi-dimensional loss can be quantified, the matching precision of the insurance premium and the real risk is remarkably improved, and accurate pricing is achieved.

The invention provides an industrial control system residual risk assessment method combined with a multi-attribute decision model, and solves the technical problems of inaccurate industrial control system protection and unreasonable resource allocation caused by incapability of identifying key atomic attacks, difficulty in distinguishing contribution of the atomic attacks to system risks and lack of dynamic updating capability in existing residual risk assessment. The method comprises the following steps: constructing risk measurement index comparison information and risk measurement index initial information; calculating a risk measurement index weight and a risk measurement index value, and sorting the attack atoms according to the risk measurement index weight and the risk measurement index value to obtain an atomic attack sorting result; extracting an attack path according to the topological information, the atomic attack sorting result and the influence factor, performing normalization to obtain an initial global risk value, judging whether the attack path exists or not, and if so, removing the attack path and recalculating the global risk value; otherwise, outputting the atomic attack and the system risk degree. The method can be widely applied to the technical field of residual risk assessment.

An institutional risk assessment method and apparatus are disclosed. The institution uses known inherent risk rules to assess customer data, obtaining first-risk customer data that matches the rules, and calculates known inherent risk index values. Next, based on risky customers and / or risk events not directly related to the institution contained in acquired external intelligence, a second-risk customer data set is selected from the customer data that did not match the rules, and an unknown inherent risk index is constructed, calculating its probability score. An inherent risk value is determined based on the known inherent risk index value and the unknown inherent risk probability score. Residual risk indicators are extracted from the acquired risk cases, and corresponding index values ​​are calculated; the remaining risk value is determined based on these index values. A risk assessment result for the institution is determined based on the inherent risk value and the remaining risk value. The risk cases include risk events directly related to the institution that have occurred or been discovered. The customer data contains private data, which requires privacy protection.

A computer-implemented risk management system (100) for projects with cognitive artificial intelligence (AI) and data-driven applications, wherein the system includes the following: • a project context input module (110) configured to receive project context data including AI model information, data inputs, infrastructure features and organizational context; • a risk identification module (120) that is operationally coupled with the project context input module (110), wherein the risk identification module (120) is configured to identify a variety of project risk factors based on the project context data; • a KPI mapping module (130) configured to assess each identified risk factor against several Key Performance Indicator (KPI) dimensions, the KPI dimensions including at least the severity of impact (131), frequency of occurrence (132), difficulty of mitigation (133), cost risk (134), organizational dependency (135) and long-term residual risk (136); • a KPI weighting calculation engine (140) configured to apply weighting coefficients to the KPI dimensions; • an assessment algorithm module (150) configured to calculate a composite weighted risk assessment for each identified risk factor based on weighted KPI values; • a risk prioritization module (160) configured to rank the identified risk factors according to their weighted overall risk values; and • an agile sprint reassessment module (170) configured to iteratively update KPI values, composite risk assessments and risk prioritization results in response to changes in project conditions.