41 results about "Hazard evaluation" patented technology

A method and apparatus for assessing the hazard of rainfall-induced landslide clusters, and a computer device. The method comprises: acquiring historical rainfall-induced landslide cluster data from past intense rainfall events to construct landslide sample data; performing analysis on the basis of landslide attribute parameters and rainfall-induced landslide cluster data distribution information of the landslide sample data to obtain landslide development characteristics; on the basis of landslide geographical features and geometric features, using the landslide sample data and a plurality of candidate landslide-influencing indexes to analyze underlying patterns of landslide occurrence, so as to establish a landslide hazard evaluation index system; and performing correlation analysis on the plurality of candidate landslide-influencing indexes to determine a target landslide-influencing index, and combining the target landslide-influencing index with the landslide hazard evaluation index system to obtain a landslide hazard assessment model constructed on the basis of an automated machine learning framework. The efficiency of rainfall-induced landslide cluster hazard assessment can be improved and the accuracy of rainfall-induced landslide cluster hazard assessment can be effectively enhanced.

To provide a method that comprehensively evaluates risk potential of a chemical substance by combining comprehensive non-target qualitative and quantitative analysis with various hazard evaluation support databases.SOLUTION: A method for comprehensively evaluating risk potential for a chemical substance comprises: a chromatographic separation step of separating a measurement sample into components; a qualitative analysis step of qualitatively analyzing the separated components; a quantitative analysis step of oxidizing and reducing the separated components to make methane, and then quantifying the methane; and a risk evaluation step, for a target substance identified in the qualitative analysis step and the quantitative analysis step, extracting a chemical substance with a similar structure by referring to a database, extracting a chemical substance with physical properties similar to those of the target substance, predicting a toxicity value or arithmetic mean of the toxicity values of the extracted chemical substance as toxicity potential of the target substance, and estimating risk potential.SELECTED DRAWING: Figure 1

The invention discloses a non-impact ground pressure coal seam multi-face adjacent goaf working face impact risk evaluation method. The method comprises the following steps of 1, obtaining mine data; 2, judging that the rock burst attribute of the mined coal seam is a rock burst-free coal seam; 3, judging that the to-be-evaluated region belongs to a multi-surface adjacent goaf; 4, evaluating the impact risk of the multi-face adjacent goaf working face of the non-impact ground pressure coal seam; according to the method for judging the multi-face adjacent gob-side working face of the rock burst-free coal seam, after it is judged that the target area conforms to the multi-face adjacent gob-side working face of the rock burst-free coal seam, the impact danger is evaluated from the macroscopic perspective through a comprehensive index method, a possibility index method and the like, and the risk of the rock burst-free coal seam is judged. And then, a multi-factor stress superposition impact risk evaluation method and numerical simulation are adopted to evaluate the impact risk under special geology and mining conditions, and guidance is provided for impact risk evaluation of the multi-face adjacent goaf working face of the rock burst-free coal seam.

The invention discloses a coping scheme evaluation method, device and equipment and a medium, and relates to the technical field of scheme evaluation, the method constructs a cost index system suitable for evaluating a coping scheme for evacuation of hazardous events, and hazard evaluation is carried out on the harm of pollutants of various events and the social and economic influence caused by the pollutants. After the input data is determined, the advantages and disadvantages of the input emergency scheme can be evaluated from three aspects by calling the hazard evaluation model library every time, and then the evaluation result is displayed to a decision maker in a visual mode for reference by the decision maker, so that the optimal response scheme is determined. Therefore, a user can be helped to quickly judge the degree of harm of various consequences, and a visual basis is provided for making decisions by adopting different response schemes.

The present application relates to a kind of new energy fluctuation and data injection attack superimposed cyber-physical fusion hazard evaluation method and system, wherein the method comprises the following steps: constructing new energy fluctuation and data injection attack superimposed attack model, attack model introduces data injection attack vector and the change vector caused by new energy output uncertainty in line flow;Through the calculation of line load security constraint, determine the cyber-physical fusion hazard under new energy fluctuation and data injection attack superposition based on attack model.Compared with the prior art, the present application can determine the worst case by new energy output uncertainty and malicious data attack superposition caused by power grid ontology hazard level, can help operation and maintenance personnel to find the key risk node in power system.

A model-based functional hazard assessment (FHA) is disclosed. A disclosed example apparatus for generating a model-based FHA associated with a product includes an associator to associate a function of the product to a failure condition to define a first association, and associate a hazard assessment with the failure condition to define a second association, and associate a safety requirement with the hazard assessment to define a third association. The apparatus also includes an organizer to generate the FHA based on the first, second, and third associations. Disclosed examples include automated FHA data validation capabilities.

This invention provides an intelligent evaluation method for rockburst hazard at working faces based on principal component analysis-probabilistic neural networks, belonging to the field of rockburst hazard evaluation technology. An evaluation index system is established and data is collected by combining rockburst hazard influencing factors, drill cuttings monitoring, critical stress index monitoring, and actual field conditions. Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a training set and a test set. A probabilistic neural network (PNN) is used to train the evaluation model on the data in the training set, and then the performance of the evaluation model is tested using data from the test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's smoothing factor needs to be modified and retrained until the accuracy of the evaluation result meets the set value.

The invention discloses a building material smoke release characteristic rapid determination device and method, and belongs to the field of building material safety performance testing, and the building material smoke release characteristic rapid determination device comprises a controllable temperature environment box, a multi-channel optical detection system, a smoke collection device, a manifold construction module, a curvature analysis module, a hazard evaluation module, a sample processing system and a smoke toxicity analysis system, according to the method, through innovative application of a differential geometry theory, smoke distribution in a three-dimensional physical space is mapped to a high-dimensional feature space, and a Riemannian manifold representing the smoke distribution is constructed; through curvature calculation and geodesic analysis, the internal mechanism of smoke diffusion is revealed; according to the method, the comprehensive test can be completed by only needing a trace amount of samples, so that the test efficiency is greatly improved; the spatial heterogeneity of smoke distribution is accurately captured through manifold characterization; a multi-dimensional hazard assessment system is adopted, comprehensive safety risk assessment is provided, and a scientific basis is provided for fire hazard safety assessment of building materials.

The invention discloses a digital factory multi-mode disaster protection system based on a dynamic weighted neural network and a self-optimization method. The self-optimization method comprises the following steps: S1, collecting multivariate data; s2, processing real-time data and constructing a feature tensor; s3, obtaining a hazard value through a hazard value calculation model, and matching the hazard value with a hazard level; s4, automatically starting countermeasures according to the hazard level; s5, inputting the accident data into an accident database, and updating the weight in the hazard value calculation model according to a weight self-optimization mechanism; the system comprises a multivariate data acquisition module, a feature fusion module, a dynamic hazard assessment module, an automatic response system module, a weight optimization module and a system integration and communication module, wherein the automatic response system module is used for automatically starting countermeasures according to hazard levels. According to the method, an algorithm-data-execution closed-loop architecture is formed, and the problems of response lag and assessment stiffness of a traditional factory safety system are solved.

The invention provides a falling object recognition device and method based on an intelligent geogrid, and the device employs photocuring 3D printing to prepare a geogrid packaging FBG optical fiber sensor with a groove to form a distributed sensing network, controls the position of a falling object through a three-way synchronous belt sliding table, and builds a coordinate system in combination with an infrared laser dynamic calibration system; according to the method, a multi-mode hybrid algorithm of ResNet feature extraction, double-branch coordinate regression and random forest Bayesian optimization is adopted to accurately identify and classify falling objects. According to the invention, more comprehensive and accurate rockfall monitoring and hazard assessment support is provided for slope engineering; the monitoring precision and efficiency can be improved, interference of environmental factors on the monitoring effect can be effectively reduced, and reliable guarantee is provided for safety early warning and emergency response in practical application.

The application discloses an airplane landing gear measurement and control device and a hazard evaluation method, which comprises a motion curve detection system, a smoothness detection system, a landing gear retraction and extension control system, an electric control circuit detection system, an appearance defect detection system, a tire pressure detection system, a communication control system, a data analysis processing system and a remote control terminal; wherein the motion curve detection system, the smoothness detection system, the landing gear retraction and extension control system, the electric control circuit detection system and the appearance defect detection system all realize data interaction with the data processing and analysis system through the communication control system; the remote control terminal is connected with the communication control system in a wireless mode and is used for controlling the execution process of each system and displaying the state. The application solves the problem that there is currently a lack of comprehensive measurement and control device and hazard prediction and evaluation method for airplane landing gears, so that the airplane landing gears will malfunction and be damaged when used in the field.

The invention discloses a Brinell vole harm monitoring method using computer vision, and relates to the technical field of computer vision, and the method comprises the steps: obtaining a Brinell vole harm monitoring region, and carrying out the management region division; management area division and marking are carried out on the monitoring area, the unmanned aerial vehicle and the sensing equipment are used for obtaining data in the monitoring area, the rodent damage and vegetation state data of the monitoring area are analyzed, the damage comprehensive evaluation index is extracted according to the damage monitoring mechanism, and damage evaluation is carried out through the comprehensive evaluation model. According to the method, a remote sensing ecological index star-based library specially used for vegetation real-time state analysis is constructed, correlation coefficient analysis is performed by using a rodent damage assessment degree value, an objective function model between an ecological value and predicted rodent damage assessment is obtained, the mutual relation between vegetation and rodent damage in an ecological system is fully considered, and the rodent damage condition is predicted by using ecological data. The accuracy of rat damage assessment is improved, and a regional rat damage spatial distribution diagram is drawn.

A cloud-edge-end power internet of things vulnerability hazard evaluation method and system. The method comprises the following steps: determining an influence factor score and a exploitability factor score of a target vulnerability based on the type of the target vulnerability; calculating a weight using an entropy weight method to determine a basic characteristic evaluation value; determining a time influence evaluation value according to the average time of the target vulnerability being exploited and the number of days of existence; obtaining the data value of a cloud server, the computing resource value of an edge server and the device usage rate of a terminal device, calculating the cloud server value, the edge server value and the terminal value in combination with the deployment quantity and the business value, determining a network asset risk evaluation value based on a bidirectional penetration attack graph; and evaluating the hazard level of the target vulnerability based on the basic characteristic evaluation value, the time influence evaluation value and the network asset risk evaluation value. The scheme improves the accuracy and reliability of the cloud-edge-end power internet of things vulnerability evaluation.

The invention belongs to the field of geological disaster evaluation and prevention, and particularly relates to a sand liquefaction discrimination method based on a cloud model, and the method mainly comprises the steps: determining main impact factors; collecting and screening sand liquefaction project examples; classifying and grading the sand liquefaction project examples by adopting a l ogi st ic model; cloud model digital features of all factors under different level conditions are calculated; and based on the measured value of each factor of the to-be-evaluated work site, the certainty degree of each factor belonging to each liquefaction risk level is calculated. According to the invention, a cloud model theory is applied to convert a sand liquefaction risk concept with fuzziness and randomness characteristics into quantitative certainty, and a novel sand liquefaction risk discrimination method is established. The method is firm and reliable in theoretical basis and easy to operate, the uncertainty rule in the sand liquefaction risk judgment risk evaluation process is revealed, the evaluation result is more practical, and a basic basis can be provided for prevention and control of sand liquefaction geological disasters.

The invention provides a target space monitoring method and system, electronic equipment and a computer program product, belongs to the technical field of image processing, and aims to solve the problem that an existing monitoring method cannot perform refined evaluation on monitoring objects and behavior influences thereof in different functional areas of a target space. The method comprises the following steps: collecting video data from the target space, and determining a plurality of regions with different functional attributes in the target space; respectively inputting the video data corresponding to the different areas into corresponding identification models to generate entity identification results of the different areas; and executing a corresponding potential safety hazard assessment process according to the entity identification results of the different areas.

The application provides a rock burst danger evaluation method based on "far-near" field precursor information, and relates to the technical field of mine safety. The method first establishes a rock burst "far-near" field comprehensive monitoring system, determines "far-near" field monitoring data evaluation indexes, and constructs a rock burst comprehensive evaluation index system; then, based on the rock burst comprehensive evaluation index system, a rock burst "far-near" field monitoring data group is constructed; the combination weight of the rock burst danger evaluation index is solved by using the "AHP method + variation coefficient method" based on game theory; finally, the rock burst danger grade is divided based on the weighted rank-sum ratio method. The method obtains the data monitored by the far-near field, realizes the rock burst early warning by using the combination weight and the weighted rank-sum ratio method, and considers the far-near field, so that the early warning is more accurate.

The invention belongs to the technical field of environmental health risk identification and supervision, and discloses an environmental health risk assessment method and system for combined pollution of a metal smelting area, and the method comprises the steps: S1, constructing an environmental health risk assessment system; s2, acquiring and inputting data; s3, heavy metal environmental health hazard identification; s4, evaluating receptors of heavy metal environmental health; s5, heavy metal environmental health hazard assessment; s6, evaluating the exposure of the heavy metal environment health; s7, comprehensive evaluation of heavy metal environment health; s8, judging the supervision priority; and S9, performing closed-loop calibration and dynamic updating. According to the method and the system provided by the invention, environmental health risk assessment of metal smelting area combined pollution is carried out based on the GIS grid and internal and external exposure coupling, and differential supervision aid decision-making with explainable risk contribution, adjustable assessment result and dynamically updated supervision priority area is realized.

The invention discloses a coal rock structure transient induced impact-based working face impact risk evaluation method, which comprises the following steps: acquiring geological data of a target working face, and determining a to-be-evaluated area of the working face; determining a stress state of the to-be-evaluated region before structure transient and a stress state of the to-be-evaluated region after structure transient; according to the stress state before the structure transients and the stress state after the structure transients, calculating the impact stress of the working surface under the transient excitation action of the overlying strata space structure; according to the stress state before the structure transients and the impact stress during the structure transients, establishing an impact risk evaluation index of the to-be-evaluated area of the working face; and according to the impact risk evaluation index, performing impact risk evaluation division on the to-be-evaluated area of the working face. The method can effectively improve the working face impact risk evaluation precision, provides guidance for mine rock burst prevention work, and ensures safe production of the working face.

The invention discloses a new energy power plant power generation acquisition terminal network security defense method. The method comprises the following steps: performing security threat analysis on a power generation acquisition terminal; constructing a power generation acquisition terminal network security protection framework; aiming at attack threats existing in the power generation acquisition terminal, attack association rules are mined based on a parallel algorithm, and association rules in a specific attack scene are generated offline; on the basis of similarity calculation, matching of online abnormal events and association rules is achieved, and specific network attacks are recognized; and analyzing security risk indexes of the power generation acquisition terminal, predicting a possible influence range and severity of a current attack according to hazard assessment and anomaly recognition results, and constructing and optimizing an anti-permeation strategy for the terminal in the influence range. According to the invention, the risks of hijacking, eavesdropping, interference, denial of service attack, forgery of control instructions and tampering of measurement data of wireless communication between the power generation acquisition terminal and the station level are reduced, and the network security defense capability of the power generation acquisition terminal is effectively improved.

This invention discloses a method for detecting defects in concrete dams, belonging to the technical field of dam crack detection. The method includes: acquiring an image of the concrete dam; performing environmental adaptive compensation adjustment on the concrete dam image; preprocessing the concrete dam image; separating the concrete dam image into crack regions and background regions; extracting the contours of the crack regions; geometrically filling the contours of the crack regions to obtain key feature values ​​of the cracks; and evaluating the crack hazard based on the key feature values. This invention not only reduces the impact of environment, lighting, and weather on the captured images but also suppresses transmission noise, effectively improving image capture and transmission quality. Furthermore, the geometric filling of the crack region contours enables rapid crack hazard evaluation. The entire method is computationally simple, fast, has low computational cost, low resource consumption, and is adaptable to processing various types of dam image data, exhibiting strong generalization ability.

The invention relates to the technical field of wind and snow flow monitoring, in particular to a wind and snow flow intelligent monitoring system based on artificial intelligence, which comprises a server, a wind and snow supervision analysis unit, an aging analysis unit, an early warning management unit, a risk assessment unit and an efficiency analysis unit, according to the invention, environmental data of a road are collected and wind and snow hazards are evaluated and analyzed, on one hand, a supervision effect on the wind and snow flow is realized, and on the other hand, the hazard condition of the wind and snow flow is conveniently judged, so that early warning is timely carried out, the hazard influence degree of the wind and snow is reduced, and the supervision and early warning effect on the wind and snow flow is improved; meanwhile, the transmission data of the monitoring points are collected, aging influence assessment analysis is carried out, and whether aging abnormity exists in data transmission of the monitoring points or not is judged, so that the stability and the timeliness of data transmission are improved, that is, deep analysis is carried out on the collection equipment and the collection object, and the accuracy and the effectiveness of an assessment analysis result are improved.

The invention discloses a non-coal stratum tunnel harmful gas risk evaluation method and system and a medium, and relates to the technical field of tunnel construction, and the method comprises the steps: building a Poisson's ratio sequence of an unexcavated section rock mass along the longitudinal direction of a tunnel; a plurality of advance drill holes are constructed in the initial tunnel face and measured while drilling, and a harmful gas concentration sequence of all the advance drill holes is established; building a harmful gas emission quantity sequence of the rock mass section of the unexcavated section along the longitudinal direction of the tunnel; fusing the Poisson's ratio sequence, the harmful gas concentration sequence and the harmful gas emission quantity sequence to generate a harmful gas data matrix; construction excavation is conducted, multiple harmful gas emission amount data are obtained and substituted into the harmful gas data matrix, harmful gas emission amount data of the rock mass sections of the rest non-excavated sections are obtained through calculation, and the danger level of harmful gas is judged. According to the method, the limitation of a traditional single detection means is overcome, the overall distribution rule of the harmful gas of the unexcavated section can be reflected more accurately, and the accuracy of risk evaluation of the harmful gas is improved.

The invention discloses a safety risk intelligent assessment system for an unmanned storage yard of a dry bulk cargo wharf, and particularly relates to the technical field of safety risk assessment. Comprising a dry bulk cargo wharf area division module, a dry bulk cargo wharf information acquisition module, a dry bulk cargo wharf information processing module, a dry bulk cargo wharf night risk early warning module, a dry bulk cargo wharf information analysis module and a dry bulk cargo wharf information evaluation module. According to the method, the cargo safety data of each monitoring sub-area of the unmanned storage yard area of the target wharf is acquired, the cargo spontaneous combustion hidden danger assessment index, the cargo stacking safety index and the personnel safety management index are calculated, and the cargo night safety risk early warning index and the dry bulk wharf safety risk assessment coefficient are further analyzed. Management personnel are reminded to take countermeasures in time, the safety risk is reduced, night safety management of the unmanned storage yard of the dry bulk cargo wharf is enhanced, and the overall level of wharf safety management is enhanced.

The invention relates to the technical field of vehicle function safety requirements, in particular to an expected function safety requirement mining method and a related device. The method comprises the following steps: constructing a normal behavior model for each component of the automatic emergency braking system; constructing an abnormal mode database for each component; constructing an environment element database; performing association analysis on each environmental element and the abnormal mode of each component to obtain a plurality of pieces of abnormal injection information; determining hazard assessment information respectively corresponding to each piece of abnormal injection information; and for each piece of abnormal injection information, determining an expected function safety demand associated with the abnormal injection information according to the hazard assessment information and the abnormal injection information corresponding to the hazard assessment information. According to the invention, the expected function security demand mining method with comprehensive coverage can be systematically provided.

The invention relates to a magnetizing inrush current hazard assessment method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring system operation parameters when a magnetizing inrush current event occurs in a high-voltage direct-current power transmission system; according to the system operation parameters, utilizing a hazard severity calculation model to evaluate the hazard severity of the magnetizing inrush current event; according to the system operation parameters, utilizing a hazard possibility calculation model to evaluate the hazard possibility of the magnetizing inrush current event; based on the hazard severity, the hazard possibility and the hazard occurrence frequency, obtaining a hazard assessment result for the magnetizing inrush current event; wherein the hazard occurrence frequency is used for representing the frequency of protection maloperation of system key equipment caused by the excitation surge current event. According to the invention, hazard assessment of the excitation surge current event can be realized.

This application proposes an abnormal behavior warning method, device and application, which uses a progressive approach to integrate artificial intelligence analysis methods and social hazard assessment methods to monitor and track the perpetrators. First, a preset image feature model is used to compare and analyze the behavior images, and then a preset behavior factor feature model is used to compare and analyze the behavior factors. In some cases, the four-dimensional social hazard assessment method is finally used to evaluate the behavior of the perpetrators. It can timely and accurately warn of possible abnormal behaviors, reduce social conflicts and play a risk prevention role.

The invention discloses a method and a system for identifying and disposing a multi-point explosion-related device. The method comprises the following steps: establishing a multi-point explosion-related device identification database and an explosive disposal strategy database; identifying the explosives in the explosion-related scene based on the information of the explosives and the explosion-related devices in the multi-point explosion-related device identification database; and according to the identification result, in combination with data information in an explosive disposal strategy database, obtaining a targeted explosive disposal strategy, and outputting the targeted explosive disposal strategy to a user. According to the method, the basic database is established, so that reliable data support is provided for subsequent explosion-related device identification, hazard assessment and on-site disposal; through combination of a database management module, an identification module and a disposal strategy module, a deep machine learning technology is further adopted, and a set of complete daily learning, usual training and wartime disposal solution is provided for a user.

The invention, which belongs to the technical field of distribution network engineering protection, discloses a distribution network grounding current diffusion hazard assessment method based on different accumulated water depths, mainly comprising a power supply module, a disc electrode, a line module, a metal hemispherical shell, an accumulated water layer, a grounding module, an integrated module, a connecting line, an insulating fence and the like. A distribution network grounding current diffusion hazard assessment test platform is built, distribution network grounding current diffusion tests under different accumulated water thicknesses are carried out, and the accumulated water layer depth is recorded, so that a distribution network grounding current diffusion hazard assessment factor analytic expression is obtained. The distribution network grounding current diffusion hazard assessment test platform established by the invention can effectively test distribution network grounding fault current diffusion under different ponding depths, can obtain distribution network grounding current diffusion hazard assessment factors based on different ponding depths, and provides theoretical support for distribution network project current diffusion diversification safety assessment and protection decision under complex working conditions.

The invention relates to the field of land planning management, in particular to a land index management system and method based on differentiated territorial space use control, and the method comprises the steps: obtaining land use control partition data and land attribute data, and evaluating the land use control sensitivity; construction attribute data and adjacent space environment data are obtained, and overflow hazard assessment of the construction land candidate position is executed; constructing a purpose control-overflow hazard coupling weight matrix based on the land purpose control sensitivity and the overflow hazard evaluation result, and evaluating the spatial coupling influence of each construction land candidate position; performing developability judgment on each candidate construction land based on a spatial coupling influence evaluation result, and evaluating the development suitability of each construction land candidate position; and generating a construction land selection suggestion based on the development suitability evaluation result. According to the method, the scientificity and the reliability of the development site selection decision of the construction land are improved by quantifying the coupling influence of the use control sensitivity and the overflow hazard degree.