53 results about "Residual risk" patented technology

The invention discloses a data text desensitization method and system for economic big data, and particularly relates to the field of text desensitization. In a data receiving stage, heterogeneous data sources are accessed through multiple channels, structured data are subjected to field-level standardization processing, and unstructured texts are subjected to OCR conversion and semantic segmentation; the method comprises the following steps: constructing a data consanguinity graph, identifying sensitive entities based on a pre-trained NER model, realizing three-layer hierarchical management in combination with dynamic weight calculation, and dividing the entities into a core layer, an association layer and a non-sensitive layer; an entity association network is constructed through a knowledge graph technology, a privacy association strength coefficient is quantified, and cross-entity desensitization consistency control is realized; a privacy risk assessment model is adopted to calculate a residual risk value, an adaptive enhancement mechanism is triggered when the residual risk value exceeds a threshold value, data availability is maintained through a utility assessment unit while privacy is protected, and dynamic balance between risk and utility is formed.

The invention provides an intelligent data security exposed surface risk assessment method, system and device and a medium, and relates to the technical field of information security, and the method comprises the steps: constructing an exposed surface asset map through an asset fingerprint recognition engine, and calculating an asset exposure index; constructing a vulnerability knowledge base, and identifying potential vulnerabilities; based on the standard vulnerability score, the vulnerability utilization tool activeness and the attack event frequency in the set time period, calculating the triggering probability of the potential vulnerability as a dynamic vulnerability score; malicious IP access frequency and leakage record matching degree are obtained through firewall logs and dark web monitoring data, and threat intelligence factors are obtained through calculation; calculating an exposed surface risk value in combination with the asset exposure index, the dynamic vulnerability score and the threat intelligence factor; and based on a time decay model, according to the exposed surface risk value and the business influence factor, calculating a residual risk value, and obtaining an exposed surface risk assessment result. The accuracy of risk assessment is further improved through multi-dimensional data fusion.

The invention provides a mining area ecological restoration guidance system based on ecological big data. Relates to the technical field of environmental engineering and big data application, and comprises a data acquisition and fusion module used for acquiring remote sensing images, unmanned aerial vehicle aerial photography, ground sensor and historical monitoring data according to a unified space-time coordinate system and outputting fusion data; the prediction and risk quantification module is used for constructing a space-time deep learning model based on the fused data and outputting an ecological index prediction value and a corresponding risk probability; and the strategy optimization module is used for generating a restoration scheme according to the ecological index prediction value and the corresponding risk probability through a multi-target reinforcement learning model. According to the mining area ecological restoration guidance system based on ecological big data, ecological benefits, economic cost and residual risks can be considered synchronously, and a quantifiable and comparable optimal parameter combination is provided for a mining area restoration scheme.

The invention discloses a sample position and patient information input system of a full-automatic blood coagulation tester, and particularly relates to the technical field of medical detection equipment. Analyzing the pressure fluctuation of the sample needle in the sample suction process at different positions through a residual risk judgment module, and judging whether a trace residual risk exists or not; the aerosol diffusion condition is dynamically analyzed through a cross contamination assessment module, and the cross contamination risk of the sample is assessed; the safety determination module determines the safety of the sample detection process according to the residual risk and the cross contamination risk; the positioning deviation evaluation module evaluates the influence of the positioning deviation of the sample by analyzing the temperature distribution gradient; the consistency evaluation module analyzes an information transmission error and evaluates the input consistency of a sample source and patient information; and the comprehensive adjustment module performs comprehensive analysis, evaluates the reliability of a sample detection result, and dynamically adjusts the sample site distribution and information input process, thereby providing more scientific and robust support for clinical diagnosis.

The invention discloses a vehicle expected function safety data-driven closed-loop solving method, system and equipment and a storage medium, belongs to the technical field of vehicle expected function safety, and solves the technical problem that in the prior art, a closed-loop expected function safety problem solving method for carrying out data driving on actually occurring hazardous events is lacked. Acquiring driving data before and after the vehicle encounters the hazard event; performing functional deviation analysis and trigger condition induction on the preprocessed driving data; making a multi-dimensional system optimization scheme; and when a hazard event occurs in the vehicle, performing recharge analysis on the driving data to obtain a residual risk which cannot accept an expected response and a residual risk which can accept an expected response, updating a multi-dimensional system optimization scheme, optimizing a vehicle function module, performing hazard identification and risk assessment, and putting the vehicle into operation after the vehicle passes the hazard event. The method is used for realizing system function boundary continuous expansion and safety capability evolution, and guaranteeing the expected function safety of the vehicle in the whole service cycle.

The embodiment of the invention discloses a project strategy optimization method and system and a storage medium, and the method comprises the steps: building a multi-objective optimization model which is a constraint model of cost, time and residual risk; an improved NSGA-II algorithm is utilized to solve the multi-objective optimization model, a Pareto optimal strategy set is generated, and the improved NSGA-II algorithm comprises an elitism strategy and a Gaussian disturbance variation strategy; a screening threshold value is dynamically adjusted according to the project real-time progress deviation, and the screening threshold value is used for controlling selection of strategies in the Pareto optimal strategy set so as to adapt to the change of the project progress; the method improves the adaptability and flexibility of strategy selection, and is suitable for a dynamic strategy optimization scene under the constraint of complex project resources.

The embodiment of the invention provides a fusion security demand generation method, electronic equipment and a storage medium. According to the method, in the development process of the automatic transverse control function of the heavy vehicle, transverse control function related items including an implementation function and an expected function are defined, hazard events of the transverse control function related items are obtained through danger and operability analysis, and the risk of the transverse control function related items is analyzed through a hazard and risk analysis method. The method comprises the steps of obtaining a safety target of a hazardous event, a vehicle safety integrity level and residual risk acceptance, obtaining a target risk level in a risk level table according to the vehicle safety integrity level and the residual risk acceptance, establishing a functional safety architecture according to the target risk level and the safety target, obtaining a safety constraint condition based on the functional safety architecture, and sending the safety constraint condition to a server. And the safety constraint body condition is converted into the target safety requirement to analyze the harm caused by the transverse control failure and the expected insufficient function, so that the development efficiency of the automatic transverse control function is improved, the development period is shortened, and the reliability and the safety are improved.

The invention provides a management method for operation of a fire pump house. The method comprises the following steps: at least acquiring regional image information of a fire pump house; processing the fire-fighting water pump room area image information by adopting a fire-fighting water pump monitoring model to generate fire-fighting water pump room area monitoring information; and judging whether the fire-fighting water pump room area monitoring information accords with a preset condition, and generating a corresponding management strategy when the fire-fighting water pump room area monitoring information accords with the preset condition. Therefore, the management method ensures that the large model can correctly provide information through the professional knowledge base and the knowledge graph in the field of maintenance operation of the fire pump house. In addition, the detection rule is defined by using a natural language, so that the utilization rate of physical defense and civil defense can be improved to the greatest extent. And finally, multi-modal identification of a risk scene is realized by using computer vision and a large model, so that known risks can be effectively prevented, left risks can be found through anomaly identification, and a management system is continuously improved.

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 computer-implemented method including: obtaining, by a computing device, a plurality of risk events; mapping, by the computing device, treatment actions to each of the plurality of the risk events; determining, by the computing device, an impact of the treatment actions on each of the plurality of the risk events; determining, by the computing device, a probability of implementation of the treatment actions; calculating, by the computing device, how the treatment actions affect other risk factors based on the determining steps; and providing, by the computing device, a recommendation of optimal risk mitigation based on how the first treatment actions affect the other risk events.

The invention relates to an LOPA protective layer risk assessment method for a nuclear fuel element manufacturing facility. The method comprises the following steps: S1, constructing a risk matrix; s2, collecting data of nuclear fuel element manufacturing facilities; s3, determining nodes according to the collected data, performing HAZOP analysis on each node, identifying potential deviation and causes of deviation generation, using a risk matrix to evaluate the nodes generating deviation, and screening out dangerous scenes; s4, identifying the independent protection layer of the dangerous scene, carrying out LOPA analysis, and calculating the residual risk and grade of an accident after the independent protection layer is reduced; and S5, aiming at the residual risk and the grade of the accident, putting forward safety countermeasures until the residual risk of the accident is reduced to the lowest feasible level. According to the risk assessment method, the industrial accident risk and the nuclear accident risk of the nuclear fuel element manufacturing facility can be systematically identified, the accident risk can be semi-quantitatively judged, feasible measures for reducing the accident risk are put forward, and safe operation of the facility is guaranteed.

The present application relates to a geological disaster monitoring and early warning system applied to the field of disaster early warning, comprising a monitoring module for collecting rainfall, vegetation development, soil moisture content, ground crack development and human activity data, an analysis and judgment module comprising a preliminary analysis and dynamic correction unit, an early warning module for sending an alarm and an escape path, and a simulation module for predicting a disaster path, wherein the vegetation monitoring not only considers soil fixation, but also introduces root system damage effect; the human activity monitoring distinguishes between aboveground engineering and underground engineering, the dynamic correction unit solves the influence of historical early warning residual risk on current evaluation through time decay and main control factor superposition, the system innovatively integrates the two-way influence of engineering efficiency and natural factors, and introduces building impact resistance evaluation, significantly improves early warning accuracy, and is especially suitable for progressive disaster prediction in engineering activity intensive areas.

An information processing device, an information processing method, and a program are provided that enable appropriate understanding of risks related to the security of a target entity. [Solution] The information processing device comprises a receiving unit that receives answers to security-related questions from a target entity, a control unit that calculates a security countermeasure score for the security measures based on the compiled results of the answers, and based on this, calculates a security risk score for the residual security risk, and an output unit that outputs a security score determined based on at least one of the security countermeasure score and the security risk score, wherein the security countermeasure score is higher the lower the security risk, and the security risk score is higher the higher the security risk.

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.

The invention relates to the technical field of financial risk analysis, in particular to a risk analysis method and system for financial data. Comprising the following steps: S1, collecting internal financial data of internal enterprises, and performing related enterprise list identification according to the internal financial data; s2, performing external financial data collection and external enterprise information collection according to the related enterprise list, and screening the internal financial data in combination with the related enterprise list to obtain related financial data corresponding to each external enterprise; by dynamically calculating a risk compression resistance residual value and monitoring the overall risk openness of the enterprise in real time, if the amount of a single risk transaction exceeds the residual compression resistance value, the risk level is automatically improved to the highest, the transaction is frozen, and the management layer needs to perform approval personally, so that the facture of an enterprise fund chain and the improvement of the overall risk tolerance caused by a single high-risk transaction are avoided.

To enable efficient implementation of measures against vulnerabilities in a manner that suppresses adverse effects on a computer system.SOLUTION: A security measures management device comprises: a configuration information storage unit that stores information related to a management target system being a computer system to be managed; an analysis result storage unit that stores information related to results of analyses of respective threats that may be input to the management target system; an emergency response necessity determination unit that acquires vulnerability information being information related to a vulnerability, identifies a countermeasure-required threat caused by an attack utilizing the vulnerability on the basis of the information related to the management target system and the information related to the analysis results, and classifies the countermeasure-required threat into ones requiring an emergency response and ones not requiring an emergency response; and a residual risk monitoring unit that determines a degree of priority regarding implementation of measures at a current time for the countermeasure-required threats classified as not requiring an emergency response.SELECTED DRAWING: Figure 1

The application discloses a distributed system fault rehearsal control method and related device, and relates to the field of computers, which determines the deviation degree of the running index of the distributed system relative to the preset performance baseline parameter. At least based on the deviation degree and the change rate of the current fault injection intensity, the instantaneous risk consumption rate is determined. According to the instantaneous risk consumption rate, the total consumed risk amount is determined, and based on the total consumed risk amount and the preset total risk budget in the whole cycle, the remaining risk pre is determined. Finally, based on the remaining risk budget, the fault injection intensity is adjusted to match the fault injection intensity and the remaining risk budget. By introducing the quantifiable risk budget, the change rate of the fault injection intensity is included in the risk consumption, the risk of the whole rehearsal process is uniformly measured and constrained, and the continuous, smooth and adaptive adjustment of the fault injection intensity is realized, and the safety and effectiveness of the rehearsal are considered.

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.

A safety control method for an autonomous driving assistance system includes: receiving a status signal regarding a driver so as to determine a reasonably foreseeable indirect misuse (RFIM) event; receiving a particular system event and / or system fault; and calculating, with reference to a degree of severity of the particular system event and / or system fault, a failure rate related to the reasonably foreseeable indirect misuse (RFIM) event, wherein it can be determined, on the basis of the failure rate, whether a safety of the intended functionality (SOTIF)-related residual risk in the autonomous driving assistance system is acceptable.

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.

A risk-oriented internal audit system, hosted on a server, is used to conduct risk assessments on multiple audited entities. The system includes: a risk data reporting module for receiving risk data input from reporting units; a control measures and audited entity assignment module for receiving control measures data provided by data providers, including review and audited entity assignment; a risk data review module for reviewing, correcting, or returning risk data; a risk score calculation module for calculating the inherent risk score, control measure effectiveness score, and residual risk score and risk level of each audited entity based on the risk index scores corresponding to risk factors; and a risk assessment result reporting module for outputting a residual risk result report. This invention improves the efficiency and consistency of risk assessment.

The invention discloses a method and a device for determining market risk capital data, relates to the technical field of data processing, and mainly aims to solve the problem of poor determination accuracy of existing market risk capital data. Comprising the steps that in response to a capital data determination instruction of a financial product, a risk configuration file of the financial product is loaded, and the risk configuration file comprises a risk group, a risk factor and a risk impact coefficient; calling a risk calculation tool, and importing the risk configuration file through the risk calculation tool to generate a sensitivity data file of the financial product; respectively carrying out intra-group risk hedging and inter-group risk hedging based on the sensitivity data file to obtain sensitivity risk capital data and default risk capital data, and determining residual risk capital data based on the risk factor; and determining market risk capital data of the financial product according to the sensitivity risk capital data, the default risk capital data and the residual risk capital data.

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 present invention discloses a method and system for evaluating the residual risk of expected functional safety. The method formulates three-layer acceptance criteria. The first-layer criterion is based on the SOTIF standard, that is, all known hazard scenarios should be tested and pass the test after optimization and improvement. The second-layer criterion derives corresponding acceptance criteria according to different autonomous driving hazard behaviors. When the test results meet the acceptance criteria for the corresponding hazard behaviors, it is considered that the second layer passes. The third-layer criterion is the overall acceptance criterion. By comparing the accident statistics of human drivers in the same ODD scenario of intelligent driving functions and according to the GAMAB criterion, that is, the residual risk of any new system is not higher than that of existing systems with similar functions or hazards, the overall acceptance criterion for intelligent driving functions is obtained. If all three layers of criteria are passed, it can be considered that the residual risk of the expected functional safety of the intelligent driving function is low enough.

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.

To allow a worker to understand risk assessment.SOLUTION: A work management system includes a storage unit that stores mechanical equipment information including at least residual risk information based on risk assessment relating to a work site or mechanical equipment installed at the work site, and floor information of the work site, a map information generation unit that generates map information including a residual risk mark indicating a part having a residual risk based on the mechanical equipment information, and the floor information, and an output unit that outputs residual risk information corresponding to a residual risk mark selected by a worker from the map information.SELECTED DRAWING: Figure 2

The present invention discloses a residual risk assessment method and system for safe production risk zoning, which relates to the field of safe production technology. A comprehensive assessment model combining static analysis and dynamic analysis can more accurately reflect the risk level of risk zoning, and has better application effect. At the same time, this scheme is divided into dynamic and static risk control assessment services. Static risk control outputs the original inherent risk, and dynamic risk control outputs the residual risk. In the application of the algorithm model, the level of residual risk is calculated, and risk control warnings and recommended preventive measures are carried out. In this way, inherent risk control and risk control measures for residual risks are distinguished. The standardized risk assessment process and clear risk level display are helpful to improve employees' safety awareness and promote the construction of corporate safety culture. Through systematic analysis and evaluation of risks, corporate management can make more reasonable decisions based on data and improve the overall safety management level.