29 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 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 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 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.

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.

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.

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.

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 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 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

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 relates to the technical field of document reply generation, and discloses a document reply generation system based on AI auditing, comprising: a document analysis module used for obtaining and fragmenting an original document, performing semantic vector conversion, and constructing an evidence graph; the key point mapping module is used for analyzing the review key point as a computable rule, calculating the matching confidence coefficient and the evidence coverage degree and outputting a rule mapping result; the fact resolution module is used for extracting fact clauses, judging and resolving contradictions and updating evidence atlas entity variables; the violation judgment module is used for substituting rules to judge violation key points and generating a severity score list; the rectification optimization module is used for selecting an optimal rectification measure and forming a rectification scheme table; the reply generation module is used for calculating residual risks and generating a reply text with law and regulation anchor points and evidence anchor points; and the evidence storage and filing module is used for writing the hash chain and filing the reply document. According to the method, document automatic auditing, replying generation and whole-process tampering prevention and trace reserving are 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.

The invention discloses an oil and gas station static equipment risk evaluation method and device, a medium and equipment. The method comprises the following steps: carrying out feasibility analysis; if the method has feasibility, determining a main failure mode of the static equipment; dividing a failure possibility grade, a failure consequence grade and a vulnerability grade of each failure mode; determining the failure possibility level, the failure consequence level and the vulnerability level based on historical failure data of the equipment; calculating a residual risk value; generating a risk matrix, and determining a risk level based on the residual intensity ratio; and analyzing the risk level, and making an inspection and detection plan based on the analysis result. According to the method, the failure risk level of the static equipment is clearly judged by accurately identifying the failure mode of the static equipment, a set of complete station static equipment risk evaluation method flow introducing system vulnerability is formed, and the purpose of better repairing and maintaining the station static equipment is achieved.

The invention relates to the technical field of industrial automation and intelligent manufacturing, and particularly discloses a production process alarm rule evolution method and system capable of operating context sensing. By fusing multi-source real-time and historical data, a dynamic deduction model is constructed, the abnormal triggering probability of each production unit is predicted in a prospective manner, and a vulnerability unit candidate set is generated. And on the basis of comprehensive production loss evaluation of virtual simulation and context weighting, screening out a core intervention unit. Through enumeration combination, three-dimensional priority ranking and nested process scheduling optimization verification circulation, a minimum effective intervention combination capable of controlling the residual risk and a matched optimization strategy thereof are determined. And placing the combination in an online environment for verification, if the verification succeeds, solidifying the combination into a formal alarm rule, and if the verification fails, analyzing situation deviation and feeding back an update model to form a self-evolution mechanism of perception-evaluation-optimization-verification-evolution so as to realize continuous self-adaption of the alarm rule to a dynamic production environment.

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.

The invention relates to a security detection method and device for a user-side energy storage scene, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring target scene elements of a to-be-tested energy storage scene; determining each potential dangerous scene existing in the to-be-tested energy storage scene according to the target scene element, and determining a risk level corresponding to each potential dangerous scene; according to the risk level corresponding to each potential dangerous scene, identifying each key potential dangerous scene from each potential dangerous scene; obtaining residual risks and tolerable occurrence frequencies corresponding to the key potential dangerous scenes; and determining whether the safety of the to-be-tested energy storage scene meets a preset safety requirement or not according to the residual risk and the tolerable occurrence frequency corresponding to each key potential dangerous scene. According to the embodiment of the invention, more accurate security detection of the to-be-detected energy storage scene can be realized, and security detection requirements in different to-be-detected energy storage scenes can be met.

To allow a worker to understand risk assessment.SOLUTION: A management system includes a storage unit configured to store 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 worker information including worker identification information of a worker at the work site, a position information acquisition unit configured to acquire position information of the worker, a mechanical equipment identification unit configured to identify mechanical equipment near the position of the worker based on the position information of the worker, and an output unit configured to output the residual risk information relating to the identified mechanical equipment.SELECTED DRAWING: Figure 11

The invention belongs to the technical field of software security, and particularly relates to a risk assessment method for a software application, which comprises the following steps: acquiring static indexes of different dimensions related to each component in the software application through a plurality of detection tools; obtaining a dynamic index of each component in the software application related to the unpublished / unrepaired vulnerabilities; obtaining time sequence data of vulnerability repair related to each component in the software application; determining a technical risk level, a threat situation level and a rectification capability level of the software application according to the static index, the dynamic index and the time sequence data; and fusing the technical risk level, the threat situation level and the rectification capability level to evaluate the risk state of the software application. According to the method, the final risk state of the software is evaluated based on the three-layer risk expression, so that the evaluation result can reflect the inherent characteristics of the software, the current external attack environment and the safety management level of a supplier, and the evaluation result is ensured to be closer to the residual risk actually undertaken by the software.