52 results about "Individual risk" patented technology

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention discloses an intelligent induction and early warning system for poisonous gas in a limited space, and relates to the technical field of gas induction and early warning. A three-dimensional structure model of the limited space is constructed, and non-uniform grid division is carried out based on barrier characteristics; multiple types of toxic gas sensing units are arranged in the grids, and gas concentration data are collected in real time; constructing a toxic gas three-dimensional cloud picture, and generating a dynamic weight field based on the concentration gradient; fusing operator positioning and trajectory prediction information, and establishing an individual risk exposure model; calculating a space-time intersection of the toxic gas cloud picture and the high-risk track area to form a fusion risk matrix; realizing linkage early warning control of a region level and an individual level through multi-level dynamic threshold judgment and thermal block clustering identification; the system can realize accurate perception and active response of the toxic gas gathering trend and personnel exposure risk in the complex limited space, has the technical advantages of high sensitivity, high intelligence and low false alarm, and is suitable for various limited space scenes such as chemical engineering, tunnels and mines.

The invention provides a coal mine personnel safety situation dynamic sensing method, device and equipment and a medium, and the method comprises the steps: carrying out the time-space reference unification of the obtained multi-source heterogeneous data of underground operation personnel, and obtaining the time-space synchronization feature data, which comprises positioning data, environment data and inertial motion data; inputting the positioning data and the inertial motion data into a pre-trained meta-learning behavior recognition model to obtain a behavior label of the operator; constructing a dynamic coupling risk map in combination with the behavior label and the environment data; and performing space-time convolution operation on the dynamic coupling risk map to obtain a prediction risk distribution map and generate an individual risk avoiding path. By adopting the method, the underground safety production level of coal mine personnel can be improved.

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 discloses an environmental component detection data processing method and system in occupational health, and belongs to the technical field of environmental data processing, and the method comprises the steps: obtaining the original detection data of a multi-source detection device, obtaining the real-time position information of a worker, carrying out the matching, generating a structured monitoring matrix, and calculating a dynamic risk value; generating a real-time risk vector, identifying a combined pollution scene, calculating an enhancement effect, generating a superposition risk index, comparing the superposition risk index with an occupational contact limit value standard, generating a risk grading signal, carrying out short-term exposure trend analysis, and predicting future risk grade change. And generating an early warning decision instruction containing the current risk level and the predicted risk level to execute a double-path intervention strategy, and generating an equipment control signal for driving the physical equipment to act. According to the invention, multi-source monitoring data is dynamically associated with personnel positions, and multi-dimensional risk calculation, trend prediction and closed-loop control are carried out, so that accurate dynamic assessment and prospective early warning intervention of individual risks can be realized.

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.

The invention discloses an individual risk dynamic assessment method and system based on a space-time causal graph network, and relates to the technical field of data processing. The method comprises the following steps: acquiring risk-related data of an individual to be evaluated; converting the data into structured data to obtain risk-related structured data of the individual to be evaluated, wherein the risk-related structured data is divided according to static features, dynamic features and causal features; according to the risk-related structured data, calculating a static feature risk score and a dynamic feature risk score of the to-be-evaluated individual, and then determining a final risk score; and obtaining division information of risks, and predicting dynamic risk trend information with the highest confidence degree according to the divided multiple dynamic risk trends in combination with the dynamic space-time fusion prediction result and the causal feature matrix. According to the method, the reliability, accuracy and timeliness of individual risk dynamic assessment are effectively improved, and the individual risk condition can be predicted and assessed more efficiently and dynamically.

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 discloses a data element transaction risk assessment method based on variational Bayesian reasoning. The method comprises the following steps: S1, collecting and constructing a multi-channel multi-source data set from a data element transaction platform; s2, generating structured data; s3, forming a multi-granularity risk feature set; s4, constructing a multi-granularity risk representation model; s5, iteratively optimizing the hierarchical variational Bayesian inference model by using a variational inference method, continuously updating model parameters of each risk layer, and enabling posterior distribution of risk factors of each layer to approach real distribution; and S6, generating a dynamic change trend of the individual risk, the platform risk and the system risk, performing comprehensive calculation to generate a unified risk score, and performing multi-granularity risk quantification on risk factors for real-time risk early warning, triggering an early warning signal and outputting a risk assessment result to a risk management system. The method has higher layered response sensitivity and risk conduction visibility, and the accuracy of platform-level fraud detection and system-level mobility risk identification is obviously improved.

Various embodiments of the present disclosure provide machine learning model-based risk prediction and treatment pathway prioritization for entities associated with a respective disparity group. Example embodiments are configured to generate, using a risk prediction model, an individual risk score for an entity of a disparity group associated with an entity cohort. Example embodiments are also configured to generate, using a disparity risk adjustment model, a disparity adjusted risk score for the entity based on the individual risk score. Example embodiments are also configured to initiate various prediction-based actions for the entity based on a comparison between the disparity adjusted risk score and a risk score threshold. Example embodiments are also configured to generate a phenotypic profile for the entity based on an evaluation data object and an image-based evaluation data object for the entity and generate a prediction-based action sequence for the entity based on the phenotypic profile.

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 discloses a software developer information security judgment method, system and device and a medium, and relates to the technical field of software development security management, and the method comprises the steps: obtaining the historical core development behavior data of a developer, and constructing a multi-dimensional behavior feature vector and a development team cooperation relation graph; establishing a personalized behavior baseline model in combination with the role behavior template; calculating an individual risk score based on the behavior data collected in real time; establishing a team risk propagation model to generate a team cooperation risk index; determining a risk level according to the individual risk score and the team cooperation risk index, and executing a response strategy; and collecting evaluation data to optimize the behavior baseline model and the team risk propagation model. According to the method, a complete technical link from historical data analysis to real-time risk monitoring is constructed, the risk identification and early warning capability is improved, dynamic regulation of risk prevention and control is realized through differentiated response of scene perception, and a reliable information security protection scheme is provided for a software development team.

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 invention relates to the technical field of the Internet of Things, in particular to an intelligent personnel scheduling management method based on the Internet of Things, which comprises the following steps of: acquiring multi-source data such as positions, physiology and behaviors of personnel, and calculating individual risk scores by fusing surrounding personnel and historical relationships; generating an aggregation risk topological graph from the risk scores through a preset kernel function; and analyzing a risk peak value in the graph, matching an optimal executor through a multi-objective optimization algorithm, and generating a scheduling instruction. According to the method, discrete individual risks are converted into a visual global risk view, automation and optimization of scheduling decisions are realized based on dynamic analysis of the risk situation, and the risk early warning accuracy and the emergency response efficiency are improved.

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 invention discloses a high-net-value crowd risk intelligent assessment system based on multi-dimensional dynamic interaction, and belongs to the technical field of medical health information. Comprising a data acquisition module for acquiring and quantifying multi-dimensional risk factors; the weight distribution module is used for carrying out dynamic weight distribution based on the multi-dimensional risk factors; the data processing module is used for constructing and training a hybrid reinforcement learning model, taking the result of the dynamic weight distribution as the input of the hybrid reinforcement learning model, and outputting a risk scoring result; and the feedback module matches an intervention strategy based on the risk scoring result. According to the method, the problems of static property, low dimension and intervention lag of a traditional method are effectively solved.

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 a log generation method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, test data corresponding to a test component is obtained; then performing individual risk assessment and overall risk assessment on test parameters in the test data to obtain parameter risk values corresponding to the test parameters; determining a component risk value corresponding to the test component according to the parameter risk value; according to the component risk values corresponding to the test components, determining abnormal components from the test components; and finally, generating an abnormal part positioning log corresponding to the abnormal part. In this way, risk assessment can be carried out on the whole test parts, the abnormal parts can be determined according to the part risk values, the abnormal part positioning logs can be generated, the abnormal part positioning logs can directly reflect problems existing in the test process and can be fed back to testers more visually, and automatic positioning of the abnormal parts is achieved.

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.