57 results about "Hazard evaluation" patented technology

The invention provides a burnt rock aquifer water disaster prevention and control design method which comprises the following steps: exploring mine geological and hydrogeological conditions, determining burnt rock distribution characteristics, an aquifer range and a water-rich abnormal region, and constructing a burnt rock aquifer water diversion characteristic judgment system; on the basis of the residual bursting coefficient of the burnt rock, a calculation formula of the development height of the burnt rock stratum water-flowing fractured zone is corrected, and the development height of the working face mining overlying strata water-flowing fractured zone is accurately judged through the formula; constructing a burnt rock water-bearing layer risk evaluation system according to the relationship between the burnt rock water-bearing layer position and the development height of the water-flowing fractured zone; according to the danger evaluation system, the'intercepting-dredging-building 'burnt rock aquifer layering treatment method is provided. Compared with traditional aquifer water disaster treatment, the method has the advantages that the aquifer damage of the burnt rock can be judged more accurately, layered treatment is more economical and efficient, and the method is of great significance to prevention of underground water inrush accidents and underground water loss and realization of safe, efficient and green mining of coal.

The invention discloses a power transmission line foreign matter identification method and system based on an improved YOLOv10 model. Relates to the technical field of power transmission line detection. According to the method, the method is improved on the basis of the existing target detection technology in the scene of a special power transmission line, the structure of a YOLOv10 model is optimized and improved by introducing an ODConv dynamic convolution module, a CAFM hybrid convolution attention mechanism module and an NWD loss function on the basis of the YOLOv10 model, in the detection stage, foreign matters are recognized through a foreign matter detection model, and the foreign matters are recognized through a foreign matter recognition module. Inputting the image containing the foreign matter into a component detection model to determine the position of the component, and finally performing hazard assessment and making a response according to the relative position of the foreign matter and the component. The detection precision of the YOLOv10 model is effectively improved, the hazard assessment capability is improved, and the detection efficiency is improved, so that the efficiency of foreign matter removal work of the power transmission line is improved, data management can be performed, powerful support is provided for operation and maintenance of the power transmission line, and safe and stable operation of electric power is guaranteed.

The present disclosure relates to systems and methods for chemical toxicity prediction. The methods of the present disclosure comprise: receiving test data from an analysis instrument; selecting a candidate chemical according to the test data; determining a hazard translated level and a hazard evaluation level of the candidate chemical according to a molecular fingerprint of the candidate chemical; and predicting the toxicity of the candidate chemical by using a quantitative structure-activity relationship (QSAR) model based on the hazard translated level and the hazard evaluation level.

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.

The invention discloses a chemical facility accident hazard assessment method, device, equipment and medium, and relates to the technical field of chemical facility accident assessment, the method comprises the following steps: extracting feature dimensions of accident hazards generated by chemical facility accidents from public opinion data, and extracting feature data from the public opinion data by using a large language model; furthermore, the text feature data are numeralized, and resources are saved for subsequent calculation; under the condition that the accident hazard feature data is less, the generative adversarial network commonly used in the picture field is used for generating more accident hazard feature data, so that data enhancement and expansion are realized; and accident hazard grade division is carried out on the extracted feature data by using a clustering algorithm, so that the influence of human subjective judgment is reduced, and the manpower and time cost required by manual division is saved.

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 present invention discloses an unmanned vehicle and a driving route generation method, device, medium, and product, which are applied to the field of autonomous driving technology. The method includes determining a target object contained in a road surface to be detected based on the three-dimensional shape information of the road surface to be detected, and obtaining the three-dimensional shape data of the target object; the target object is a road raised obstacle or a pothole area; according to the three-dimensional shape data of the target object and the vehicle parameters, determining the hazard of the target object to the target vehicle and the hazard of the driving state, respectively, and obtaining the target object hazard assessment result; based on the target object hazard assessment result corresponding to each road raised obstacle and / or pothole area contained in the road surface to be detected, generating the vehicle driving route of the target vehicle. The present invention can solve the problem that the vehicle driving route of the related technology cannot meet the needs of users, and can generate a vehicle driving route that is both comfortable and safe.

The present invention relates to the field of coal and rock mining technology, and in particular, provides a method for evaluating the rock burst hazard of coal mines. The method includes constructing an index system for evaluating the rock burst hazard of the amount of coal and rock ejected, and obtaining a training data set and a test data set through data regularization; optimizing and analyzing the constructed index system using a fuzzy neighborhood decision rough set model and the training data set to obtain a simplified subset; inputting the obtained simplified subset into a multi-classifier model to obtain a classification result, and performing a classification accuracy evaluation on the classification result, completing the weighting of the classifier based on the classification accuracy evaluation, and obtaining the classifier weight and the trained multi-classifier model; inputting the test data set into the trained multi-classifier model for testing to obtain a test result, and weighting and fusing the test results to obtain a hazard evaluation level, and the method realizes the accurate division of the rock burst hazard level of coal mines.

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.

The invention discloses a network space information security control method and system, and relates to the technical field of network information security, the penetration test intensity is constrained according to hazard scores in a preset test period, if the network security degree is lower than expectation, vulnerability detection and repair are performed, otherwise, key data are backed up or transferred; an abnormal event is monitored and recognized in real time, an alarm instruction is sent out if abnormity exists, if the alarm is not responded and an abnormal node exceeds the expectation, the intervention degree is generated according to corresponding abnormal alarm data, and a second-level alarm mechanism is started for the abnormal event when the intervention degree exceeds the expectation; classifying the abnormal events and constructing a response strategy library, if alarm instructions are continuously received, switching alarm channels to ensure instruction receiving, evaluating the abnormal events by using a hazard evaluation model, and matching and executing corresponding response strategies; when it is determined that response is needed, a targeted processing strategy is rapidly output, and rapid targeted response is achieved when a network system is controlled.

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.

The invention relates to a new energy fluctuation and data injection attack superposed cyber-physical fusion hazard assessment method and system, and the method comprises the following steps: constructing a new energy fluctuation and data injection attack superposed attack model, the attack model introduces a data injection attack vector and a change vector caused by uncertainty of new energy output into the line power flow; and through calculation of the line load security constraint, determining the information physical fusion hazard under the superposition of the new energy fluctuation and the data injection attack based on an attack model. Compared with the prior art, the method has the advantages that the level of power grid body harm caused by superposition of new energy output uncertainty and malicious data attack under the worst case can be determined, and operation and maintenance personnel can be helped to find key risk nodes in a power system.

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 present invention provides a numerical calculation and hazard assessment method for the leakage and combustion explosion of hydrogen-doped natural gas. The method includes: establishing a three-dimensional geometric model of the gate well space by using FLACS software; based on the hydrogen-doped natural gas leakage and diffusion model, calculating the change curve of the monitoring data of each monitoring point at different positions in the gate well space with time and the concentration distribution cloud map of hydrogen-doped natural gas by using FLACS software; performing numerical calculations of combustion and explosion based on the distribution cloud map to obtain the data of each monitoring point and the distribution cloud maps of jet flame, shock wave overpressure, and thermal radiation intensity; based on the numerical calculation results of combustion and explosion, calculating the probability of the thermal radiation and explosion shock wave generated by the explosion causing harm to the human body, so as to realize the assessment of combustion and explosion hazards. By adopting the probability function method to calculate the probability of different levels of damage to the human body when subjected to different shock wave overpressures and thermal radiations, the present invention can effectively evaluate the combustion and explosion hazards.

The present invention belongs to the technical field of wave monitoring for dredging ship operations, and discloses an engineering-scale wave monitoring system and method based on image information recognition. It includes monitoring area setting, wave image acquisition, wave parameter analysis, breaking situation monitoring, surge situation monitoring and dangerous situation identification. The present invention analyzes the wave hazard assessment, wave breaking hazard assessment and wave surge hazard assessment of each monitoring area respectively, comprehensively judges whether there are dangerous wave conditions in each monitoring area, and then further judges whether the target dredging engineering ship is dangerous, thereby achieving all-round risk control of dredging ship operations, effectively reducing the risks of ship collision, capsizing and facility damage, assisting in accurate decision-making, providing a reliable basis for optimizing the arrangement of dredging operation routes, etc., reducing the probability of ships entering dangerous waters, ensuring the smooth implementation of navigation plans, and completing engineering tasks on time.

The utility model provides a sampling and detection device for evaluating the hazard of industrial hazardous solid waste, comprising: a feeding pipe, a material guide plate is fixedly installed at one end of the feeding pipe, a feeding rod is rotatably installed inside the feeding pipe through a bearing, and the feeding rod is adapted to the feeding pipe. The sampling and detection device for evaluating the hazard of industrial hazardous solid waste provided by the utility model, when sampling is required, the material guide plate is inserted into the solid waste, the motor drives the feeding rod to rotate, and the rotation of the feeding rod effectively transports the solid waste inside the material guide plate to the inside of the feeding pipe, and part of the solid waste falls into the inside of the detection component through the first discharge pipe, thereby effectively detecting the detection component, and the detection component is used for solid waste detection, which is an existing mature technical means, and will not be repeated here, and the sampling and processing of solid waste in this way effectively avoids the user from contacting with fixed waste, thereby improving safety.

The present invention relates to the technical field of thermal runaway assessment of electric vehicles, specifically to a method, system and electronic device for assessing thermal runaway and disaster-causing hazards of electric vehicles. The method comprises placing the vehicle under test and the target object in parallel and maintaining a distance below a gas collection hood; the vehicle triggers thermal runaway and collects key indicators on site; performs hazard dimension analysis, including heat flow assessment analysis, smoke hazard assessment analysis, explosion hazard assessment analysis and open fire hazard assessment analysis; performs weighted assessment according to the assessment results of each dimension, and obtains the results of thermal runaway disaster assessment of electric vehicles. The technical solution can comprehensively and accurately assess the hazard of thermal runaway of electric vehicles.

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.