161 results about "Risk quantification" patented technology

The application provides a network security vulnerability automatic management method based on network security intelligence, belongs to the field of network security vulnerability management, and is used for solving the problems of low automation, large risk assessment deviation and poor collaboration in the related art. The method comprises network security intelligence collection, screening, storage and analysis, attack path prediction, key asset identification, hazard scene enhancement and vulnerability management. Through technologies such as dynamic weight, high-order probability graph model and multi-agent game, accurate intelligence processing, dynamic risk quantification, attack forward-looking prediction and cross-enterprise collaborative disposal are realized, the vulnerability management efficiency and accuracy are improved, and the network security of the ECUs is ensured.

The present application belongs to the technical field of ultrasonic detection, and discloses a mine roadway sealing layer integrity ultrasonic detection method; the method comprises the following steps: dividing a roadway area, and dividing a regional risk level of each roadway area; generating a sealing layer stress thermal diagram, performing stress partition, and generating a measuring point according to the regional risk level of the roadway area and the sealing layer stress partition; obtaining individual difference characteristics of the measuring point, and dividing the measuring point into different types according to the individual difference characteristics of the measuring point; executing a differentiated detection scheme according to the measuring point type; and adjusting the measuring point type and the regional risk level according to the detection result; the present application is characterized by multi-dimensional risk quantization division and individual differentiation focusing, which improves the detection accuracy, reliability and resource utilization efficiency.

This application discloses a key distribution generation method and system for multi-park integrated authentication, relating to the field of industrial internet key technology. The method includes: collecting cardholder information and sending it to a cloud center; generating a first authentication risk coefficient based on the current operating status and a second authentication risk coefficient based on historical verification records, and evaluating the authentication risk index; if the authentication risk index does not exceed a preset risk threshold, using a basic key distribution generation strategy for identity verification; if the authentication risk index exceeds the preset risk threshold, calculating the risk index overflow value, optimizing the key distribution factor composition and the selection method of the derived algorithm, and generating an adapted key distribution generation strategy for identity verification. This solves the technical problems of existing key distribution generation strategies being fixed and rigid, unable to adaptively adjust to the dynamic risk characteristics of multi-park authentication scenarios, and lacking a risk quantification assessment mechanism, leading to a lag between security strategies and risk situations.

The present application relates to the technical field of dynamic assessment of traffic safety risk, in particular to a method for dynamic assessment and intelligent management and control of expressway traffic safety risk. The present application utilizes the parallel computing characteristics of LightGBM, combines edge computing nodes, and realizes millisecond-level risk inference. The present application takes the three-level risk label as the basis for label generation, simultaneously utilizes SHAP to discover the blind area not covered by the rules, and reversely optimizes the rule library. Thus, the present application realizes real-time risk quantification, hierarchical early warning and dynamic intervention on the vehicle behavior in the complex traffic flow of the expressway.

This invention discloses a risk quantification method for AI large-scale models in engineering safety production, relating to the field of engineering safety technology. The method includes: S1, acquiring a domain causal template library constructed based on preset engineering safety standards, the domain causal template library including multiple standardized causal chain structure templates; S2, parsing the inference text generated by the main model in real time, and constructing an instance graph reflecting the current safety inference logic; S3, performing structural matching between the instance graph and the templates in the domain causal template library, and determining whether there is a structural deviation in the inference chain of the main model based on the matching result; if so, triggering a backup model to take over; S4, extracting key safety commitments from the inference context or instance graph of the main model, converting the key safety commitments into formal logical constraints, and injecting the formal logical constraints into the backup model; S5, verifying the response generated by the backup model and determining whether it satisfies the formal logical constraints.

This invention provides a termite control method based on remote intelligent control using the Internet of Things (IoT), comprising: collecting multi-dimensional environmental data related to termite activity in real time through an IoT sensor node network deployed in the monitoring area; uploading the multi-dimensional environmental data to a remote intelligent control platform, analyzing and making decisions in real time based on a preset algorithm model, and generating optimal control instructions; and remotely controlling one or more control execution devices in the monitoring area according to the optimal control instructions to start or adjust their working modes and complete targeted termite control operations. This invention aims to solve the problems of inaccurate risk quantification, lack of scientific decision-making, low control precision, and lack of dynamic optimization mechanisms in existing termite control technologies. By using IoT multi-dimensional sensing, dynamic pressure field modeling, and multi-agent collaborative game decision-making, it achieves accurate quantification of termite activity risk, optimal decision-making for control schemes, and remote intelligent control, thereby achieving efficient, economical, and environmentally friendly termite control goals.

This invention provides a berthing adaptation calculation method and system based on a CAD / CAE composite model, belonging to the field of ship berthing auxiliary decision-making technology. It includes: constructing a loading stability calculation model and a three-dimensional profile model database based on the ship loading manual and an initial model containing geometric information; integrating hull geometry, stability, and hydrodynamic performance data into a unified composite model, and defining data dependencies and update triggering rules; solving heave and surge response characteristics based on a wave model, and calculating limit values ​​in conjunction with wave parameters; calculating the safe range of the air gap height at the platform outlet based on the gangway overlap geometry, combined with limit values, tides, and load changes, and dynamically quantifying the risk; and using real-world data to drive online calibration of the composite model and dynamic simulation of the berthing overlap process. This invention achieves accurate dynamic calculation and risk assessment of the air gap under multi-source data fusion, adapting to the berthing needs of multiple ships, and improving berthing safety and operational continuity.

The present disclosure provides a method for optimizing a cross-system permission approval path and related devices, the method comprising: obtaining a historical approval log, and constructing an approval process model based on a process mining algorithm; calculating a comprehensive risk value for a permission application based on a risk assessment algorithm; and generating an approval path based on the approval process model and the comprehensive risk value corresponding to the permission application. The present disclosure constructs a dynamic approval path through process mining technology, and combines a multi-dimensional risk quantification model to achieve dynamic generation of the approval process, thereby significantly improving the efficiency of permission approval.

The application discloses a kind of coal mine underground sealed roadway gas content real-time determination system and determination method, belong to coal mine gas content determination field;The method comprises: before roadway sealing, distribute distributed sensing optical fiber network and determine initial volume;After sealing, continuously monitor surrounding rock strain and internal gas pressure, temperature and gas concentration;Periodically inject known amount of inert gas into sealed space, use the change of gas concentration, pressure and temperature before and after injection, based on gas state equation to invert current absolute volume;Based on optical fiber strain data, calculate volume estimate through geometric and physical model, fuse the above two volume data, obtain continuous, high-precision optimal dynamic volume;Finally, fuse dynamic volume, concentration, pressure and temperature data, calculate gas total content and its change rate in standard state in real time, realize hierarchical early warning and gas resource, risk quantification management.

The application discloses a smart community police management system and method based on Internet of Things security, and belongs to the technical field of smart communities, and solves the problem that existing system data processing cannot utilize edge computing nodes for low-delay real-time analysis, and cannot meet timely early warning requirements, the method comprising preprocessing of multi-source heterogeneous community monitoring information, analysis and processing of the community monitoring information based on edge computing, and generation of a risk propagation path in combination with front-end analysis results and heterogeneity characteristics; in the application, analysis and processing of the community monitoring information based on edge computing avoids the delay of centralized processing, can quickly trigger early warning in the early stage of a risk event, thereby gaining golden disposal time for risk event disposal, and a risk analysis model realizes a leap from single-node anomaly detection to composite risk quantification through multi-dimensional feature fusion and dynamic space-time reasoning, and realizes multi-dimensional feature fusion and dynamic reasoning capability of the risk event.

This invention discloses a novel pollutant source apportionment and dynamic risk quantification assessment method coupling APCS-MLR and PMF, relating to the field of environmental management technology. The method includes: collecting pollutant concentration data from environmental samples, preprocessing the data to construct a data matrix; running various models to perform source apportionment based on the data matrix, extracting source apportionment feature information, and matching the pollution sources identified by each model to obtain high-confidence source matching pairs; fusing the source contribution concentrations of the two models based on the high-confidence source matching pairs to obtain a fused contribution concentration; calculating the ecological and human health risk values ​​of each pollution source based on the fused contribution concentration to obtain source-specific risk values, calculating the risk contribution rate of each pollution source, and outputting a list of high-risk sources. This invention solves the problems of insufficient accuracy and reliability of source apportionment using a single model, and addresses the issues of the disconnect between source apportionment results and risk assessment, making it difficult to identify high-risk pollution sources.

This invention discloses a pre-prepared food preservation device and method. The device includes a housing, a multi-dimensional sensing module and a control module disposed within the housing, as well as an interaction module, a communication module, and a central control module. The multi-dimensional sensing module and the control module are connected to the central control module via the communication module, used to detect parameters of the pre-prepared food inside the housing and to regulate the temperature. The interaction module communicates with the central control module for parameter setting and information display. The central control module performs logical analysis based on the real-time parameters collected by the multi-dimensional sensing module, determines the preservation status, and generates a preservation strategy. The preservation status is pushed to the interaction module, and the preservation strategy controls the control module to adjust the temperature, humidity, and ventilation rate inside the housing. This invention uses the multi-dimensional sensing module to monitor temperature, humidity, and various characteristic gases in real time, and the central control module performs multi-dimensional spoilage risk quantification calculations, dynamically synthesizing a freshness index, thereby executing targeted preservation strategies.

This application relates to the field of mine operation detection and early warning technology, and discloses a method and system for detecting and classifying early warning of violations in mine operations. The method includes: constructing a violation classification system and establishing a mapping between equipment and violation nodes; generating a semantic raw data stream; extracting and aligning features to generate standardized data packets; fusing features to infer violations and analyzing the coupling to generate correlation; calling a framework to calculate a normalized total risk score based on multi-dimensional parameters; classifying and handling violations, correcting weights based on feedback and performing incremental learning. The system includes: a data acquisition and classification module, an edge preprocessing module, a large model detection module, a risk quantification module, an early warning and handling module, and a closed-loop optimization module. This invention uses a lightweight cloud-based large model to perform cross-modal feature fusion of visual, sensor, and positioning multimodal data, and introduces a historical accident case library to analyze the causal coupling strength between different violations, thereby enabling the identification of complex risks that are difficult to detect with a single sensor.

The application relates to a cloud platform-based water resource management method, device, equipment and medium. The method integrates multi-source water data through a cloud platform, constructs a hierarchical Bayesian model after data cleaning, uniformly quantizes model parameters, input and posterior distribution of structural errors, drives ensemble Kalman filtering dynamic assimilation of real-time observation data based on the same, generates a prediction distribution fusing multi-source uncertainty, further analyzes the contribution source of prediction variance in real time through a Sobol' index, identifies dominant uncertainty factors, constructs a coupled error model according to the same, feeds the coupled error model back to the assimilation cycle as a state variable for online correction, and finally forms an improved prediction model capable of dynamically tracking and quantifying the uncertainty coupling propagation mechanism, thereby significantly improving the accuracy and reliability of hydrological prediction, and providing risk quantification basis and optimization scheme for flood control scheduling, water resource allocation and urban drainage management decision-making.

This invention provides a dual-hole collaborative electromagnetic wave and laser fusion detection system for karst geological structures, belonging to the fields of precision geotechnical engineering detection and intelligent sensing technology. Based on a probe assembly, this invention heterogeneously fuses the geometric representation of laser point clouds with electromagnetic wave medium inversion data using a unified cylindrical coordinate system. The system quantifies hidden disaster-causing factors by calculating the deep karst development index and cavity volume morphology index; it uses a spatial Euclidean distance difference algorithm to calculate the minimum safe partition wall thickness, accurately locating high-risk sectors of rock wall penetration. Furthermore, the system adjusts the scanning step distance in real time based on the wave velocity gradient and generates an adaptive grouting control strategy. This invention effectively eliminates single detection blind spots, achieves three-dimensional visualization and risk quantification assessment of hidden karst, and improves the safety and scientific decision-making of pile foundation construction in complex geological environments.

This invention relates to a multi-level simulation testing method and system for intelligent driving. The method includes: hierarchically labeling different test scenarios based on a six-dimensional complexity evaluation model to obtain expected functional safety scenario events and functional safety failure events; generating precise timestamps corresponding to the expected functional safety scenario events and functional safety failure events; synchronously triggering the expected functional safety scenario events and functional safety failure events based on the precise timestamps to simulate corresponding risk scenarios; selecting the verification level of the risk scenario based on the type of functional safety failure event and executing the corresponding simulation instance to obtain test data; and conducting risk assessment based on the test data using a risk assessment model. This application achieves joint injection of scenarios and failures, continuous accumulation of multi-level verification data, and composite risk quantification assessment, improving the comprehensiveness and efficiency of safety verification for intelligent driving systems.

This application discloses a planning method, system, equipment, medium, and product for a new energy hydrogen production system based on multi-timescale risk quantification, relating to the field of new energy hydrogen production technology. The method includes: constructing a new energy hydrogen production system; constructing an operational model; constructing a risk quantification model; acquiring hourly predicted and historical power generation data of the new energy generator units; calculating the prediction error based on the predicted and historical power generation data; constructing constraints based on the operational model and the prediction error; constructing a planning mathematical model for the new energy hydrogen production system considering multi-timescale risks based on the constraints and the risk quantification model; and solving the planning mathematical model for the new energy hydrogen production system considering multi-timescale risks under the constraints, with the objective of minimizing the annual comprehensive risk level, to obtain the optimal planning scheme. This application improves the safety and reliability of the new energy hydrogen production system.

The present application relates to the technical field of land ownership change management, and in particular to a blockchain smart contract storage and risk early warning method for land ownership change, which acquires land ownership change request data packets and generates data fingerprints, calls ownership verification smart contracts to complete ownership consistency verification. After verification, a space-time heterogeneous graph data is constructed by fusing space topology and time sequence information, a risk quantification proof is generated by a space-time graph convolution risk quantification model, and a risk warning level is output after zero-knowledge verifiable calculation verification. Low-risk changes automatically complete on-chain storage and network state synchronization, and high-risk changes automatically generate warning instructions and push them to the supervision node and freeze the request. The present application realizes tamper-proof storage and pre-risk early warning of the whole process of land ownership change, improves the processing efficiency and supervision ability of ownership change, and effectively prevents ownership fraud and illegal trading behavior.

This invention discloses an intelligent detection method for power monitoring data security risks that integrates knowledge graphs. The method includes: a data acquisition layer that integrates and collects raw power data streams and sends them to a preprocessing layer; a preprocessing layer that standardizes the data, extracts features from the data, groups and associates the data according to the features, and performs a preliminary evaluation; a knowledge construction layer that receives standardized event streams, power equipment features, and abnormal data tags, and updates the knowledge graph in real time based on the above data; an intelligent detection layer that queries the graph display results to ensure reliable graph display, detects threat events and issues warnings, and simultaneously performs risk quantification scoring on threat events, analyzes the risk composition, and determines whether the detection is genuine; and a decision response layer that receives threat reports, integrates and sorts the reports, and predicts subsequent threats. This method solves the technical problems of fragmented power monitoring detection, delayed response, and isolated protection in existing technologies.

The application discloses a new energy and energy storage demonstration platform design method and system meeting multi-scene requirements, and belongs to the technical field of power system planning and intelligent optimization. The method comprises the following steps: obtaining typical scene characteristics to construct a multi-scene classification model; establishing an upper multi-objective optimization model to generate a configuration scheme candidate set; establishing a lower deviation calculation and constraint verification model to perform constraint verification, deviation quantification and risk mapping on the candidate scheme, and feeding back the results to the upper layer; using NSGAII and Gurobi solver double-layer iteration to solve the optimal configuration capacity; and through a scene scaling mechanism, the optimization result is reduced to the resource constraint range of the demonstration platform, and equipment configuration details and verification reports are output. The application realizes multi-scene adaptation, deviation risk quantification and double-layer collaborative optimization, can flexibly adapt to the demonstration requirements of scenes such as Shaguo desert, offshore wind power, source-grid-load-storage integration and the like, guarantees the rationality of equipment configuration, and reduces the engineering landing risk.

This invention discloses a method for precise operation path correction of unmanned tractors, relating to the field of unmanned tractor technology. The method includes the following steps: collecting land operation data of the unmanned tractor; constructing a baseline operation path model for the unmanned tractor based on the land operation data; collecting real-time operation data of the unmanned tractor and calculating a risk assessment value based on the baseline operation path model; determining the operation status of the unmanned tractor based on the risk assessment value to obtain a risk quantification result; performing path planning for the unmanned tractor using an improved ARA* algorithm based on the risk quantification result to obtain a safe passage path; and predicting and controlling the path deviation of the unmanned tractor using a fuzzy adaptive model predictive control algorithm based on the safe passage path to obtain stable control commands, thereby achieving precise operation path correction for the unmanned tractor.

This invention provides a method for monitoring the condition of transmission lines. The method decouples and quantifies multi-segment conduction and multi-physical quantity coupling by constructing independent inter-segment topological conduction parameter systems and intra-segment multi-physical quantity coupling parameter systems. After two-level normalization of operational monitoring data, a comprehensive degradation index and evolution trend are generated. The dynamic residual degradation barrier is obtained by weighted integration of the influence of neighboring area conduction and the local degradation rate. A collaborative amplification factor is determined based on the degree of degradation. The gradually changing base and the amplified transient impact are superimposed to form a comprehensive risk value. The ratio of this value to the residual degradation barrier is used as the risk ratio. The state type and risk quantification results are output in conjunction with neighboring area constraints. This invention solves the problem in existing technologies where it is difficult to comprehensively handle the coupling effects of multiple factors and their dynamic evolution relationships during transmission line operation, leading to insufficient accuracy and reliability in condition assessment.

The application discloses a kind of production well gas channeling risk discrimination method and system, by fusing production well three-phase output dynamic data and injection-production connected section static geological parameters, build gas channeling coefficient, channel advantage index and form comprehensive sorting matrix, composite gas channeling risk sequence value is obtained by calculation, then according to well group production well quantity and the quantity of representation quantity statistical derivation theory central value and composite standard deviation, build objective theory risk definition interval, can realize dynamic combination gas channeling risk quantification discrimination, discarded artificial experience subjective setting threshold, improve the stability and accuracy of gas channeling risk discrimination, can provide reliable basis for well group gas channeling risk classification identification.

The application discloses a campus intelligent guiding learning data security monitoring system based on data security, which comprises a data import module, a detection and analysis module and a risk research module.The data import module collects multi-source heterogeneous data streams of a campus system, performs scene recognition operation and anonymization preprocessing on the multi-source heterogeneous data streams, and obtains to-be-analyzed data streams.The detection and analysis module performs multi-dimensional context correlation operation on the to-be-analyzed data streams, generates guiding learning data correlation graph, constructs an abnormal guiding learning detection model based on an isolated forest based on the guiding learning data correlation graph, performs unsupervised anomaly detection based on the abnormal guiding learning detection model, and generates fine-grained anomaly detection results.The risk research module constructs a threat intelligence database, performs root cause tracing operation on the fine-grained anomaly detection results to obtain abnormal tracing detection data, performs real-time risk quantification evaluation on the abnormal tracing detection data, and generates comprehensive risk event data including risk level and attack path research.

This invention proposes a geospatial data security risk monitoring method based on user entity behavior analysis. The method includes: S1, multi-source heterogeneous data acquisition and semantic preprocessing; S2, multi-dimensional behavioral feature extraction and vectorized representation; S3, adaptive behavioral baseline modeling and dynamic updating; S4, multi-level anomaly detection and risk quantification; and S5, adaptive response and dynamic handling: executing response measures according to risk scores. This invention's risk-score-based hierarchical response mechanism effectively reduces the cost of handling security incidents while ensuring business continuity.

This invention discloses a method and system for early warning of reservoir dam risks based on multi-source data fusion, belonging to the field of dam monitoring technology. The method includes: acquiring multi-source data of the target dam; performing differentiated feature extraction and hidden hazard enhancement on the multi-source data to generate a multi-source feature set; performing feature filtering on the multi-source feature set to generate a core feature set; performing fusion on the core feature set to obtain fused features; inputting the fused features into a trained early warning model to obtain a risk quantification value; acquiring heterogeneous data of the target dam and creating heterogeneous features; performing scenario adaptation quantification on the heterogeneous features to generate multi-dimensional heterogeneous features; performing enhancement on the multi-dimensional heterogeneous features to obtain enhanced multi-dimensional heterogeneous features; constructing a threshold dynamic optimization model; and performing risk assessment based on the target dam's early warning dynamic threshold and risk quantification value to generate an early warning classification for the target dam. This invention improves the accuracy and reliability of dam risk early warning.

This invention provides a novel large-scale electrochemical energy storage risk assessment and emergency response method, relating to the field of electrochemical energy storage technology. The steps of this invention are as follows: S1, collect multi-source data and preprocess it; S2, determine risk factors and construct coupling relationships; S3, perform coupled risk quantification assessment; S4, conduct risk warning and predict its changing trend; S5, take emergency measures according to the risk level. The beneficial effects of this invention are as follows: quantifying risk through risk factors improves the accuracy of risk assessment; introducing coupled risk values ​​into the comprehensive risk value considers the impact of multiple risk factor coupling on the risk level; the weights of risk factors are determined by both subjective and objective weights, avoiding the one-sidedness of risk factor weight values; the adjustment coefficients of subjective and objective weights combine the similarity of sample sites, avoiding the impact of excessively low similarity on the accuracy of weights; the adjustment of risk levels considers the warning needs of emergency scenarios.

This invention relates to the field of intelligent alarms and discloses a store intelligent alarm and voice reminder method based on event classification, comprising the following steps: collecting and preprocessing multi-source data from stores through a full-domain sensing terminal to construct a core data feature library; constructing an event classification scoring model based on the feature library, completing dimension weight initialization and risk quantification classification; dynamically scoring real-time events and determining whether to trigger proactive reminders, generating standardized alarm information; classifying reminder objects according to event levels and collaboratively pushing text and voice reminders; simultaneously implementing a duplicate alarm suppression and timeout escalation mechanism to avoid alarm proliferation and response delays; and finally iteratively optimizing the classification model based on the handling data. This invention achieves accurate event classification, differentiated proactive reminder push, and intelligent linkage between voice and text, effectively solving the problems of frequent false alarms, chaotic reminders, untimely responses, and lack of closed-loop optimization in traditional store alarms, thereby improving alarm effectiveness and handling efficiency.

The application discloses a coal mine building foundation settlement prediction system and method based on a multi-source intelligent sensor, belongs to the technical field of coal mine building monitoring, and is characterized in that multi-source sensors such as settlement displacement, inclination, pore pressure, water potential and microseismicity are arranged in a building foundation area, non-settlement characteristic signals in a fixed time period are collected, and a multi-source original data set is constructed; non-displacement disturbance characteristics are extracted through wavelet transform and phase analysis to form a characteristic sequence matrix; the characteristic sequence matrix is input into a deep time graph neural network combined with time evolution weight and spatial coupling structure, and a foundation settlement prediction response sequence is output; error inversion is performed in combination with measured data, deep response factors are identified, and a cause graph is constructed; finally, a risk function is established based on risk factors and the prediction sequence, settlement risk grade division and regional early warning are realized, and the application has the advantages of response preposition, causal explainability and risk quantification and is suitable for settlement prediction and hierarchical control under complex geological environments.

This application discloses a method and apparatus for quantitatively evaluating the failure risk of oil and gas gathering and transportation pipelines, relating to the field of data processing. The method includes: dividing the pipeline under test into multiple first pipe segments; classifying multiple typical failure types into internal corrosion failure and other failures; for each first pipe segment, determining the basic risk values ​​for internal corrosion failure and other failures based on a preset risk scoring table, obtaining a first basic risk value and a second basic risk value; correcting the first basic risk value based on a risk correction factor, obtaining a corrected risk value; the risk correction factor is dynamically determined based on the risk of slug flow occurring in the pipe segment; determining the comprehensive risk value of the pipe segment based on the corrected risk value and the second basic risk value; and obtaining the failure risk assessment result of the pipeline under test based on the comprehensive risk value of each first pipe segment. The scheme provided by this application can improve the accuracy and reliability of the assessment results and enhance the generalization ability of the assessment system.