33 results about "Individual risk" patented technology

An illustrative method includes identifying, based on a scan of a compute environment associated with an entity, a plurality of attack paths from one or more networks to one or more datasets associated with the entity, determining individual risk scores for each attack path indicating a level that each attack path could be exploited to access the one or more datasets, and determining, based on the individual risk scores, an absolute risk score for the entity indicating a status of a security posture for the entity. A risk mitigation operation associated with the entity is performed based on the absolute risk score.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention relates to the technical field of risk early warning, and provides an individual risk dynamic perception early warning platform, which comprises an acquisition module, an analysis module, an early warning module, a control module and an adaptive module, and is characterized in that target individual dynamic characteristic index parameters and environment characteristic parameter information in each time period in a historical period are acquired; and determining an individual risk tendency period based on variance analysis of the dynamic characteristic index factors in the time period. And determining whether the operation of the early warning platform meets the standard or not based on the difference value between the preset environment characteristic representation threshold value in a certain time period and the actual environment characteristic representation value, and further determining the adjustment amplitude of the environment characteristic representation threshold value, or determining to start the alarm device. The problems that in the prior art, single feature perception triggers alarm, individual dynamic feature index differentiation cannot be recognized, and early warning lagging is caused by multi-dimensional analysis of environment feature differentiation, so that early warning precision is low, and efficiency is low are solved.

Techniques for assessing security risk at scale for a computing environment are disclosed. In an example method, a computing system accesses a risk model specified for a computing environment including at least a set of individual risk factors, a set of composite risk factors, and a final composite function for computing an overall risk score. The computing system receives a set of one or more inputs. The computing system computes an individual risk score for each individual risk factor using at least one input. The computing system computes a composite risk score for each composite risk factor. The computing system computes the overall risk score using the final composite function using at least two composite risk scores and outputs the overall risk score.

The invention relates to the technical field of industrial safety management and control, and discloses an integrated digital twinning boiler limited space safety operation management and control system and method, and the system comprises a twinning environment building and sensing module which is used for building a three-dimensional digital twinning model, and collecting environment, personnel position and physiological signals in real time; the core calculation and analysis module is used for calculating an individual risk correction coefficient based on the physiological signal and calculating a dynamic risk potential field in a twinborn model based on the environmental data; the risk assessment and decision module is used for coupling the risk potential energy value at the personnel position and the individual risk correction coefficient to generate a comprehensive risk index; and the collaborative intervention and presentation module is used for generating and executing an active intervention instruction based on the comprehensive risk index. According to the invention, by constructing a human-loop coupled dynamic risk potential field, quantitative evaluation and active intervention of individual and system level risks are realized, and operation safety management and control from passive alarm to active prevention and from static management to dynamic closed loop are realized.

The invention belongs to the technical field of receiving-end power grid evaluation, and particularly relates to a receiving-end power grid active support capability evaluation method and system based on the total risk potential of a system, and the method comprises the steps: firstly constructing an active support capability evaluation index system of a receiving-end power grid; then calculating an individual risk potential function of each index and a coupling risk coefficient between each index and other indexes, and then calculating a system total risk potential based on the individual risk potential function and the coupling risk coefficient, the system total risk potential including a system individual risk potential and a system coupling risk potential; and finally, active support capability evaluation of the receiving-end power grid is realized based on the total risk potential of the system. According to the method, on one hand, a scientific and comprehensive evaluation system is constructed, the deep evaluation of the active support capability is realized, on the other hand, on the basis of quantifying the deviation degree by using the individual risk potential, the non-linear superposition risk additionally generated by the interaction or simultaneous degradation among multiple indexes is quantified through the coupling risk potential field, and the evaluation accuracy of the active support capability is improved. And the diagnosis precision is obviously improved.

This invention provides a method for calculating the risk level of online individuals based on multi-source data fusion and dynamic perception, relating to the fields of cyberspace security and social governance. By integrating online and offline behavioral data, this invention extracts behavioral frequency, violation records, trajectory features, and emotional characteristics to construct a basic risk level. This risk level is then dynamically corrected by incorporating time decay, event sensitivity, emotional polarization, and platform amplification effects, outputting the risk level of online individuals. This achieves a quantitative assessment of individual online individual risk, providing decision support for public opinion command and online risk early warning.

A system and process for dynamic security monitoring of test taking. The system combines historical information about the test taker or relevant conditions, and includes sensors which objectively monitor test taker's actions, inactions, and involuntary response to the test given before and during the test, and at a specific test location. The information collected from the sensors is compared to a plurality of predetermined individual risk factors, which indicate a possibility of test fraud by the individual test taker to create a test event risk profile. The individual risk profile is combined with non-individual specific risk factors to create a holistic test event security profile. Security resources are then dynamically assigned to or removed from the test event based on the unique test event security profile.

The application discloses a kind of environmental component detection data processing method and system in occupational health, belong to environmental data processing technical field, it includes obtaining the original detection data of multiple-source detection equipment and obtaining the real-time position information of staff and matching, generate structured monitoring matrix and calculate dynamic risk value, generate real-time risk vector, identify complex pollution scene and calculate enhancement effect, generate superimposed risk index and compare with occupational exposure limit standard, generate risk classification signal and carry out short-term exposure trend analysis, predict future risk level change, generate early warning decision instruction containing current risk level and predicted risk level and execute double-path intervention strategy, generate device control signal driving physical device action.The application dynamically associates multiple-source monitoring data with personnel position, and carries out multidimensional risk calculation, trend prediction and closed-loop control, and can realize accurate dynamic evaluation and forward-looking early warning intervention on individual risk.

The application discloses a method for evaluating the risk level of the privacy security of a flight path of a UAV, and belongs to the technical field of intelligent control of UAV flight. In order to solve the problem of accurately evaluating the privacy security of the UAV flight, the application establishes a ground visible area model of the UAV, and determines the ground visible area of the UAV; a two-dimensional Gaussian distribution model is adopted to calculate the observation probability of the UAV, and the privacy sensitivity of the ground visible area of the UAV is constructed in combination with GIS data; a directional modulation mechanism is utilized for correction; an individual risk evaluation model is constructed, including calculation of a basic individual privacy exposure value, correction of the basic individual privacy exposure value based on a periodic behavior modulation function and accumulation of individual privacy risk integral; a regional risk evaluation model is constructed, including construction of a regional privacy risk density and a regional privacy risk gradient vector, and identification of a high-risk region; and a double-channel risk level judgment and early warning method is constructed. The application realizes the overall evaluation of the flight path through path privacy risk integral.

The invention provides a vehicle control method, a vehicle and a storage medium, the method is applied to the technical field of intelligent cabins, and the method comprises the steps that environmental data in a vehicle cabin, allergic sign information of at least one passenger and sensitive object data are acquired; based on the environment data, calculating an allergy risk index of each passenger according to the allergy sign information and the sensitive object data of the at least one passenger; and target air quality parameters of the position where each passenger is located are determined according to the allergy risk index of each passenger, a target control instruction of the air conditioning system is generated according to the target air quality parameters of the position where each passenger is located, and the air conditioning system is controlled according to the target control instruction. Therefore, through environment-physiology collaborative perception and personalized risk assessment, the problem that dynamic and closed-loop active protection based on individual risks cannot be realized due to the lack of real-time collaborative perception capability for sensitization substances in a cabin and physiological states of passengers in the prior art is solved, and the effectiveness of allergy protection and the comfort level of the passengers are remarkably improved.

The invention discloses a risk personnel analysis method and system based on grassroots governance big data, and the method comprises the steps: collecting multi-source heterogeneous grassroots governance data, executing entity alignment and semantic standardization, and generating a standardized personnel entity table; on the basis of personnel attributes and labels, in combination with ternary coupling of space-time co-occurrence, dominant social relationships and behavior pattern similarities, weighted association edges among personnel are constructed, and an initial risk personnel association map is formed; according to the graph node connection relation and the multi-dimensional individual risk index, combining with the time decay factor, calculating a dynamic risk score and embedding the dynamic risk score into the graph; and identifying high-risk connected subgraphs through multi-round propagation iteration of risks from high-risk nodes to adjacent nodes. Automatic expansion identification from individual risks to group risks is realized, hidden gang, propagation chain or aggregation risks can be found in advance, and intervention is intervened in the early stage of risk evolution.

1. Name of the product in this design: Graphical User Interface for Loggers of Electronic Devices that Allows for Personal Risk Assessment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Uses of the graphical user interface: The personal risk view can be used as part of a risk assessment and management tool to help organizations comprehensively assess individual risks and develop corresponding risk management strategies. 6. Human-computer interaction method of graphical user interface: The main view is the initial interface; the interface change state diagram 1 is the downward interface of the main view; the interface change state diagram 2 is the downward interface of the interface change state diagram 1.

The invention discloses a student psychological state assessment and risk early warning system based on multi-source behavior data, and relates to the technical field of data processing. The student psychological state assessment and risk early warning system based on the multi-source behavior data comprises the following steps: a data acquisition and context binding module used for acquiring a multi-class behavior data set and performing normalization processing; the pressure dose table construction module is used for constructing a pressure dose table and generating individual exposure trajectory data frames; the behavior response fingerprint construction module is used for constructing a behavior response fingerprint data frame; the grading early warning decision module is used for constructing a risk evidence stream and outputting a risk trajectory data frame; and the closed-loop calibration module is used for compiling the early warning decision and constructing a receipt closed-loop verification data frame. According to the method, the computability, the interpretability and the cross-scene generalization stability are effectively improved, and the problem that the model learns a calendar effect instead of an individual risk due to the fact that pressure exposure is only marked by an examination week and DDL coarse granularity is solved.

The invention provides an intelligent early warning and protection method and system for falling of an inpatient based on multi-modal data fusion. According to the method, individual risk data, behavior posture data and equipment state data of an inpatient are collected, a tumble risk index of the patient is calculated from the three aspects of potential risk, behavior risk and environmental risk by using a tumble risk prediction model, the tumble risk level of the patient is comprehensively evaluated, and then corresponding protection measures are taken. Specifically, each risk factor is quantified through a weight coefficient and a risk mapping function, and finally, an S-type normalization function is utilized to obtain a comprehensive risk index, so that accurate and personalized early warning is realized. The system comprises a sensing layer, a data processing layer, an intelligent analysis layer and an intervention execution layer, and can realize full-process automation and intellectualization. According to the invention, the problems of inaccurate evaluation, incapability of personalized early warning, lack of active intervention and the like in the prior art are effectively solved, and the occurrence rate of tumble of the inpatient is remarkably reduced.

The present application relates to the field of offshore engineering and FLNG, and discloses a FLNG upper module safety space optimization method and system based on risk assessment, a medium and equipment, which comprises: obtaining the process flow and functional partition of the FLNG upper module, dividing it into multiple independent process sub-modules, performing multi-disaster risk identification and frequency analysis, simulating consequences and analyzing the contribution of multi-disaster superposition effect to personal risk, and determining the influence range of accident consequences; a functional relationship between safety distance and accident consequences and personal risk is established, and the minimum safety distance between each process sub-module is determined by taking the acceptable standard of personal risk as a constraint; a plurality of preliminary arrangement schemes containing the relative positions of each process sub-module and the parameters of passive fire protection measures are generated, and the overall risk level of each scheme is calculated; the plurality of preliminary arrangement schemes are screened with the optimization objectives of minimizing the overall risk and maximizing the space utilization, and an optimal arrangement scheme set meeting the overall risk acceptable standard is output.

The application discloses a behavior intervention system for juvenile traffic safety education and relates to the technical field of traffic safety education, and comprises the following steps: generating a multi-dimensional behavior DNA image based on operation data of juveniles in a virtual training scene; matching user resident geographic location information according to high-risk scene features in the multi-dimensional behavior DNA image and automatically labeling spatial anchor points in a real road map; and completing a micro intervention task by a user through a gesture action, instantly comparing the micro intervention task with historical data of the multi-dimensional behavior DNA image, and recording a safety behavior digital certificate of the micro intervention task; and adding the safety behavior digital certificate to a regional safety behavior integral pool and setting a family reward mechanism. The application constructs a multi-dimensional behavior DNA image by using micro behavior data such as a dangerous identification time difference and a decision accuracy rate collected in a virtual training scene, and realizes accurate characterization of individual risk features.

The invention discloses an occupational health detection method and system for mine workers, and particularly relates to the technical field of occupational health detection. According to the method, environmental hazard factor continuous monitoring data and physiological index continuous monitoring data of corresponding operation time periods of mine workers are collected, an environmental hazard factor sudden increase interval and a physiological index abnormal fluctuation interval are divided, a time sequence overlapping relation is extracted, and exposure response correlation fragments are constructed; calculating an environmental hazard factor change slope and a physiological index recovery time difference, constructing an individual load coupling unit, distinguishing an instant load state and a cumulative load state, and generating an individual dynamic risk table set; identifying a risk upgrading condition based on a preset occupational health judgment rule and generating an individual risk early warning result; backtracking the individual exposure response sequence, updating the key monitoring section of the subsequent detection period, and forming an occupational health detection result; according to the invention, individual and dynamic occupational health risk assessment and early warning of mine workers are realized.

This application discloses a method and system for dynamic management and control of construction collision risks based on trajectory prediction, relating to the field of dynamic management and control technology for construction collision risks. This application achieves real-time extraction and aggregation of multi-dimensional state information of various moving targets within the construction site, constructing a unified global dynamic information set. Based on this, according to target type and operational data, its future trajectory and short-term intentions are predicted differentially, and an individual risk field model with adjustable dynamic parameters is constructed to accurately quantify spatial risks. By superimposing the risk fields of each target, a three-dimensional global dynamic risk density cloud map that changes over time is generated, intuitively presenting the risk situation. Finally, complex high-risk areas are identified, multi-target collaborative optimization calculations are performed, a collaborative collision avoidance instruction set coordinated with operational intentions is generated, and dynamic adjustments are achieved through closed-loop feedback. This significantly improves the safety and efficiency of collaborative operations in high-risk construction environments.

The application discloses a multi-branch simulation and risk aggregation evaluation method based on AI decision uncertainty identification. The method automatically analyzes branch decision points in the execution plan before simulation execution, creates a sub-branch sandbox based on the write-once-copy mechanism when the simulation reaches the branch point, concurrently simulates multiple operation paths, and finally aggregates and analyzes individual risk evaluation results of each branch to generate a comprehensive evaluation report containing risk escalation, worst path presentation and path difference comparison. The application breaks through the structural limitations of single-path simulation and realizes comprehensive and safe exploration of potential behavior branches of AI agents.

The invention relates to a swimming pool complex interaction scene risk individual identification method, system and device and a medium. The method comprises the following steps: acquiring a multi-view video stream of a swimming pool scene, and extracting swimming pool environment characteristics by adopting a dynamic environment perception method based on the multi-view video stream; de-noising the multi-view video stream by using a preset swimming pool environment interference suppression algorithm for the swimming pool environment characteristics to obtain an enhanced video stream; determining a moving individual video stream based on the enhanced video stream by adopting a target detection algorithm; for each moving individual video stream, a three-dimensional attitude estimation algorithm is adopted to calculate individual attitude features; determining an abnormal action behavior mode based on the individual posture features according to a preset posture feature-abnormal behavior mode determination rule; an individual risk alert is generated based on the anomalous action behavior pattern. By adopting the method, the risk individuals in the complex interaction scene of the swimming pool can be efficiently identified, and the accuracy and the real-time performance of safety monitoring are improved.

The present application relates to the technical field of Internet of Things, in particular to a personnel intelligent scheduling management method based on Internet of Things, comprising: acquiring personnel position, physiology, behavior and other multi-source data, and fusing surrounding personnel and historical relationship to calculate individual risk score; generating aggregated risk topology map through preset kernel function; analyzing risk peak in the map, matching optimal performer through multi-objective optimization algorithm and generating scheduling instruction. The present application converts discrete individual risk into intuitive global risk view, and realizes automation and optimization of scheduling decision based on dynamic analysis of risk situation, thereby improving risk early warning accuracy and emergency response efficiency.

This invention relates to power grid lines, and more particularly to a discretized risk assessment method for power grid lines considering outage time. The method first uses maximum likelihood estimation to fit a Weibull probability density function based on historical fault data to reflect line aging and fault tolerance. Then, the outage time interval is discretized into equal parts, and the time-varying outage probability for each time period is extracted. For each discrete interval, the real-time severity quantification result, expected power loss, and time-varying outage probability are calculated simultaneously, and three-dimensional vector superposition is performed to obtain the individual risk contribution value. Finally, by performing a time-domain full integration of all individual risk contribution values, the final risk quantification assessment result of the line is output. This invention effectively reduces continuous modeling bias, achieves accurate quantification of probability characteristics and multi-dimensional consequences for different durations, and provides data support for power grid operation and maintenance decisions.

The application belongs to the field of safety identification, and discloses a safety identification method and system for an intelligent camera. The method comprises the following steps: generating a two-dimensional grid map containing semantic labels based on a real-time video stream of a construction site and a three-dimensional scene model; detecting and tracking a moving target in the video stream to obtain its continuous trajectory in a world coordinate system; identifying the behavior intention of the moving target by combining the trajectory and the semantic labels of the grid; generating a static risk field based on a static dangerous area, and generating a dynamic risk field of space-time diffusion for a preset risk intention target; obtaining a total risk field by fusing the static and dynamic risk fields, and calculating a real-time personal risk value of a person; and dynamically adjusting a warning threshold in combination with the area type, historical warning information and behavior intention, and generating a warning if the threshold is exceeded. The application accurately identifies intention through multi-dimensional features, filters normal operation to reduce false positives, realizes spatial quantization of risk and individual accurate dynamic measurement, and can balance the sensitivity and accuracy of the warning.

The invention relates to the technical field of intelligent chips, in particular to a medical waste hospital circulation monitoring method and system based on RFID. The method comprises the following steps: configuring a sensing tag integrated with an intelligent chip and a multi-mode sensor for a medical waste container; the intelligent chip performs cross-modal correlation analysis on the sensor data at the label end to generate a composite state event; constructing a full life cycle behavior portrait for each container based on the event, and calculating individual risk and structural integrity scores; and meanwhile, a medical flow association risk matrix is constructed to assess the cross contamination risk, and individual risks are fused to generate a visual contamination field domain graph. When the structural integrity score or the field domain graph risk potential value exceeds a threshold, predictive maintenance or adaptive intervention is triggered. According to the invention, the RFID tag with the static ID is endowed with the dynamic perception capability through the intelligent chip, and quantitative risk assessment and closed-loop management and control of the medical waste circulation process are realized.

The application discloses a belt fire-fighting safety management system suitable for a roller robot, belongs to the technical field of production line fire-fighting safety management, and specifically comprises a patrol path planning module, a multi-source mobile sensing module, a risk assessment positioning module, a pre-warning verification module and a fire-fighting execution module; the system is used for full-line automatic patrol by means of a roller robot, overcomes the disadvantages of blind area and low efficiency of manual patrol, comprehensively collects original belt running state data based on a mobile control mode, obtains multi-source feature data based on feature extraction after preprocessing, analyzes the multi-source feature data in combination with a machine learning model, the system can identify potential individual risks and comprehensive risks in an early stage of accidents such as fire and mechanical failure, dynamically adjusts a patrol strategy according to the risk types, realizes smooth switching from 'global scanning' to 'local focusing', intensively monitors and verifies a key risk area, and improves fire-fighting execution accuracy.

Techniques for assessing security risk at scale for a computing environment are disclosed. In an example method, a computing system accesses a risk model specified for a computing environment including at least a set of individual risk factors, a set of composite risk factors, and a final composite function for computing an overall risk score. The computing system receives a set of one or more inputs. The computing system computes an individual risk score for each individual risk factor using at least one input. The computing system computes a composite risk score for each composite risk factor. The computing system computes the overall risk score using the final composite function using at least two composite risk scores and outputs the overall risk score.

The system according to this embodiment aims to detect the risks of excessive sharing of personal information and phishing scams on social networking services and to provide users with appropriate warnings and advice. [Solution] The system according to the embodiment comprises an analysis unit, a warning unit, a detection unit, and an advice unit. The analysis unit analyzes the content of SNS posts and detects excessive sharing of personal information and risks. The warning unit provides warnings based on the risks detected by the analysis unit. The detection unit analyzes SNS messages and links and detects phishing scams. The warning unit provides warnings based on the phishing scams detected by the detection unit. The advice unit analyzes the user's SNS usage patterns, generates individual risk profiles, and provides advice.

The invention discloses a multi-dimensional psychological state real-time monitoring system and method for professional tiredness early warning, belongs to the technical field of psychological health assessment, and solves the problem of low risk assessment accuracy caused by the fact that data sources of existing methods are limited to traditional static information and acquisition and analysis of individual real-time multi-dimensional psychological state data are lacked. The method comprises the steps of performing local analog-to-digital conversion processing on monitoring original data, preprocessing the monitoring original data after the local analog-to-digital conversion processing, performing edge risk judgment on a multi-dimensional data set, performing identification analysis on an individual risk sequence based on a feature identification model, and calling a tiredness intervention strategy based on a monitoring database. According to the method, the real-time multi-dimensional psychological state data of the individual can be collected and preprocessed, the individual risk sequence is identified and analyzed based on the feature identification model to generate the individual tiredness early warning result, the complex change of the individual psychological state can be comprehensively captured, and the potential occupational tiredness risk can be found in advance.

The invention discloses an occupational health risk multi-dimensional dynamic assessment and intelligent management system based on the industrial internet, and the system comprises a multi-dimensional data collection module which collects environment parameters, industrial equipment operation parameters and the individual data of operators in real time through industrial internet equipment, a positioning system is combined to calculate individual real-time exposure of operators, and a multi-source data association mechanism of the environment, the equipment and the operators is constructed; the dynamic evaluation model module constructs a dynamic evaluation model based on multi-source data, and dynamically optimizes an individual risk baseline through a federated learning framework to solve the hysteresis of traditional static threshold evaluation, in the invention, the data dimension is more comprehensive, through environment, equipment and individual multi-source data association, the limitation of traditional single environment parameters is broken through, and the evaluation efficiency is improved. The risk assessment accuracy is improved; the evaluation model is dynamic, individual risk baseline self-adaption is realized based on federated learning, a risk source is accurately positioned in combination with causal inference, and the problem of static threshold hysteresis is solved.