85 results about "Risk zoning" patented technology

The invention discloses a risk zoning method for active fault engineering influence, which relates to the technical field of engineering construction evaluation, and comprises the following steps: I, fusing multi-source data of a detection fault, constructing a three-dimensional geometric model of the fault, and analyzing and correcting a fault distribution rule by utilizing a GIS (Geographic Information System) space; iI, combining fault sliding rate, paleoearthquake recurrence period and deep engineering crustal stress inversion, and evaluating activity characteristics of the hidden fracture in the engineering service period. Through multi-source data fusion, three-dimensional model construction and GIS correction, multi-factor overlay analysis and a dynamic correction mechanism, multivariate characteristics of the active fault and complex geological conditions of an engineering site are comprehensively considered, the problem of limitation of a zoning result is solved, the accuracy of risk assessment is improved, limitation of a single index is broken through, and the risk assessment efficiency is improved. Closed-loop management of data acquisition-risk assessment-engineering response is realized, and scientific and reliable technical support is provided for safe site selection, optimization design and operation and maintenance of major infrastructures.

The invention provides a chemical industry park safety management system and method based on the Internet of Things. The method comprises the following steps: collecting multi-source monitoring data in a chemical industry park to generate a dynamic monitoring data set; applying space-time causal constraints to multi-hop connection paths in the topological graph of the equipment in the chemical industrial park to generate path confidence distribution; constructing entity feature embedding of each device in the chemical industry park, and determining a time-varying causal incidence matrix through all entity feature embedding and path confidence distribution; dividing risk equipment groups with different risk levels, and constructing a risk propagation tensor of the equipment risk partition topology network in the chemical industry park through each risk equipment group; and generating a dynamic risk vector through the time-varying causal incidence matrix and the risk propagation tensor, and carrying out early warning on the safety risk in the chemical industry park based on the dynamic risk vector. By adopting the scheme of the invention, time-space registration can be carried out on the multi-source monitoring data of the sensing nodes of the Internet of Things, and the time-varying causal relationship between the devices can be mined, so that dynamic safety risk early warning can be realized.

The invention relates to the technical field of sterilization equipment control, in particular to an intelligent control method and device for sterilization equipment and electronic equipment. The method comprises the following steps: deploying a sensing network in a disinfection space and executing environment calibration to generate sensing reference data; synchronously acquiring space environment data through a sensing network; constructing a target recognition model based on the sensing reference data and the space environment data so as to perform dynamic target recognition and contact judgment; determining environment state parameters according to dynamic target recognition and contact judgment results; executing risk partitioning according to the environment state parameters to determine a region type and a risk score; and establishing a killing strategy rule base and executing killing operation according to the area type and the risk score. According to the invention, a differential disinfection strategy is formulated and executed based on dynamic environment perception and partition recognition.

The invention relates to the technical field of machine learning models, in particular to a geological disaster risk dynamic assessment method based on multi-source data fusion, which comprises the following steps: constructing a basic geographic information database; dividing geological disaster risk areas by adopting a machine learning algorithm; calculating the contribution degree of each environment factor to the geological disaster through an information amount model; fusing the dynamic rainfall data, carrying out weighted fusion on the dynamic rainfall data and the static geological disaster factors, and calculating a comprehensive risk value; dividing risk levels according to the risk values, and generating a geological disaster risk zoning map; compared with the prior art which mainly depends on single static geological data for analysis and has the problems of incomplete evaluation dimensions and poor timeliness, the scheme realizes multi-dimensional fusion analysis of geological conditions and real-time meteorological factors by constructing the geographic information database integrating the multi-source environmental factors and the dynamic rainfall data; and the comprehensiveness and the momentality of risk assessment are obviously improved.

The invention provides a fishery breeding risk area division method and related equipment, and relates to the technical field of breeding area planning. The method comprises the steps of simulating a disturbance effect of a fan group on a water flow field based on a geometric model of the fan group in combination with marine environment data of a target sea area where the fan group is located, and obtaining a water flow field simulation result; determining a risk area in the target water area based on the water flow field simulation result; inputting the flow velocity data of the risk area at each moment in the first time period into a pre-established water flow prediction model to obtain flow velocity data of the risk area at each moment in a second time period; and respectively mapping the flow velocity data of the risk area at each moment in the second time period into risk levels, and dynamically dividing the risk area in the target water area based on the risk levels. According to the method, the water flow field near the fan group can be simulated, and the preliminary risk area is determined. And through the water flow prediction model, predicting the water flow change trend at each moment in the future, and dynamically adjusting the risk area.

The invention provides a power transmission line fire risk grade zoning prediction method and system, and relates to the technical field of intelligent power grid environment perception and risk early warning, and the method comprises the steps: data collection and multi-source space-time fusion: collecting the regional characteristics of the historical fire occurrence space-time, real-time meteorological parameters, vegetation types and combustible rates of a power transmission line to form a multi-source data set, the multi-source data set realizes alignment coding through a unified geographic grid and a timestamp, and a fire risk sample set is generated. Through a multi-source data deep fusion and grid space-time alignment mechanism, unified coding and coupling modeling of regional features such as historical fire occurrence space-time, real-time meteorological parameters, vegetation types and combustible rates are realized; risk zoning precision and spatial resolution are remarkably improved, zoning granularity can be refined in practical application, the resolution is far better than that of a traditional method, and the overlapping rate of a risk zoning result and actual fire distribution is improved.

The invention relates to a landslide susceptibility evaluation method and device, electronic equipment and a medium, and belongs to the technical field of geological disaster prediction and prevention, and the method comprises the steps: obtaining preprocessed digital elevation model data and multi-source data of a target area; dividing the preprocessed digital elevation model data based on preset geographic software to obtain evaluation unit data, the evaluation unit data including slope unit data and grid unit data; training a preset deep integration model based on the evaluation unit data, and determining the evaluation unit data with high training precision as target evaluation unit data; determining a landslide influence factor based on the target evaluation unit data and the multi-source data; inputting the landslide influence factor into a preset depth integration model to obtain a preliminary evaluation result of the landslide susceptibility of the target area; and obtaining a landslide susceptibility evaluation result of the target area based on the preliminary evaluation result. According to the invention, landslide disaster risk zoning and prevention and control schemes can be accurately recommended.

The invention discloses a design and maintenance system and method for an anti-icing and anti-icing scheme of a power transmission line in a low-temperature icy region, and aims to solve the problems of extensive design and passive operation and maintenance in the prior art. The system comprises a data acquisition module, an icing risk division module, an icing load engineering determination module, a differential design module, an online monitoring module and a maintenance decision module. According to the method, through multi-source data collection and preprocessing, a refined icing grade distribution diagram is generated in combination with GIS spatial analysis and a random forest model, the ice thickness is deduced and designed based on Gunn Bell distribution, the icing load is calculated, differential design schemes are generated for a pole tower and a foundation, a wire and a ground wire, an insulator and a fitting and power distribution equipment, and the method is applied to the field of power distribution. And closed-loop management is formed in cooperation with online monitoring and dynamic maintenance. The method realizes accurate identification of the icing risk, gives consideration to safety and economy, improves the ice-resistant toughness of a power grid, reduces the operation and maintenance cost, and is suitable for design and maintenance of power transmission lines in low-temperature icy areas such as northern and southwest plateau areas.

The invention relates to the technical field of co-seismic landslide risk evaluation, in particular to a near-fault single co-seismic landslide motion range mapping method and system. The method comprises the following steps: acquiring fracture structure data of a research area; positioning a main seismic fracture based on the fracture structure data, and performing fracture mechanism analysis to obtain seismic source parameter data; constructing a fault geometric structure and earth crust medium structure model containing a three-dimensional terrain based on the fracture structure data and the seismic source parameter data to obtain three-dimensional structure model data; and setting fault source parameters and stratum velocity medium parameters based on the source parameter data and the three-dimensional structure model data to obtain seismic simulation input parameters. According to the method, the whole process of near-fault co-seismic landslide from seismic triggering to accumulation is accurately reproduced through an integrated physical driving simulation frame, the three-dimensional motion range and the danger partition are evaluated in a high-precision mode, and a reliable basis is provided for risk evaluation and disaster prevention decision making.

The invention relates to the technical field of pollution risk assessment, and provides an AI-based karst region soil-underground water cooperative pollution assessment system. According to the system, multi-source soil and underground water heavy metal time sequence monitoring data are fused, cross-medium collaborative characterization characteristics are constructed, an enhanced time sequence modeling method with a parameter efficient adaptation capability is introduced, and a composite dynamic loss function oriented to non-stationary pollution evolution characteristics is designed; and collaborative prediction and uncertainty description of the heavy metal pollution states of the soil and the underground water are realized. Furthermore, a prediction result is combined with spatial gridding analysis, a collaborative pollution risk index and a risk zoning result are constructed, technical conversion from pollution evolution modeling to spatial risk assessment and early warning output is realized, and reliable technical support is provided for dynamic early warning, graded prevention and control and environment management decision of heavy metal pollution in the karst region.

The invention relates to the technical field of hydrology and water conservancy, and particularly discloses an intelligent real-time watershed flood early warning method and system, and the method comprises the steps: watershed data acquisition, fusion forecasting model construction, multi-stage early warning threshold calculation, early warning generation and online evaluation. According to the scheme, the real-time residual error of the physical hydrological model is constructed, the complex mode that the residual error changes along with the real-time rainfall intensity and the drainage basin state is learned by adopting the residual error prediction model based on machine learning, and an interpretable and steady forecast baseline and high-precision error correction are provided through a forward coupling and feedback coupling mixed architecture; the overall robustness of flood forecasting is effectively improved; dynamic inversion is carried out by using a fusion forecasting model, flood risk differences under different soil conditions are identified, the matching degree of early warning signals and actual risks is greatly improved, a comprehensive risk value is calculated in real time on a unified grid division system, and a risk zoning map is refreshed in real time along with updating of rainfall forecasting, so that the real-time prediction of the flood risk is realized. And the practicability of early warning information and the pertinence of emergency response are effectively improved.

The invention provides an urban inland inundation simulation method and a risk early-warning risk-avoiding system based on coupling of a one-dimensional model and a two-dimensional model. The method comprises the following steps: performing one-dimensional and two-dimensional hydrodynamic simulation on urban inland inundation rainstorm and flood based on SWMM and OpenFOAM; establishing a water exchange interface between the first model and the second model, dynamically calculating exchange flow according to a water level relationship, and establishing and executing a two-dimensional model coupling mechanism; based on a coupling mechanism, running simulation and outputting data and results; building an urban model based on a Cesium platform, and inputting flood routing data obtained based on model coupling to realize visual simulation of urban inland inundation flood routing; a risk early-warning risk-avoiding system is built on the basis of a visual result and in combination with emergency management theoretical knowledge and comprises a waterlogging simulation module, an early-warning module, a risk zoning module and a risk-avoiding transfer module; risk area division of a target area is realized based on double factors of water depth and flow velocity, and a corresponding optimal risk avoiding route is planned based on a Dijkstra algorithm.

The invention relates to the technical field of suspended sediment dynamic monitoring, in particular to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, which comprises the steps of multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional ocean current vector data and seabed microtopography slope data of a target area; simulation of plume dynamic diffusion: inputting the acquired data into a microtopography coupled adaptive plume diffusion model to generate a plume diffusion three-dimensional space-time cloud picture in the future 6 hours; dynamic risk partition generation: performing space-time overlay analysis on the plume diffusion three-dimensional space-time cloud picture and a preset ecological sensitive area dynamic electronic fence to obtain an operation risk thermodynamic diagram; and intelligent partition scheduling execution: dynamically dividing a mining prohibited area, a buffer area and a safe area, and sending an inter-partition rotation scheduling instruction to the mining equipment group. According to the method, the spatial resolution and the aging response capability of sediment diffusion behavior prediction are improved, and a basis is provided for environmental impact assessment and ecological protection.

The invention relates to the technical field of environmental science and engineering, and discloses a water body sediment heavy metal pollution diagnosis and risk zoning method, which comprises the following steps of: firstly, performing functional zoning and differentiated point distribution sampling based on hydrodynamic topographic features; secondly, carrying out multi-dimensional index detection on the sediment sample to obtain a multi-dimensional data set containing a chemical occurrence form; secondly, combining a positive definite matrix factorization model with bivariate local space autocorrelation analysis, quantitatively analyzing the contribution of the pollution source and verifying the space fate of the pollution source; then, constructing a two-dimensional risk assessment system based on the chemical occurrence form, and calculating a migration release risk index and a bio-availability risk index; and finally, according to source-sink positioning and risk assessment results, a differentiated partition control strategy is generated. According to the invention, through coupling chemical morphology and spatial analysis, the concealment risk that the total amount does not exceed the standard but the bioavailability is high can be accurately identified, and accurate positioning and migration path verification of the composite pollution source are realized.

This invention relates to a snowmelt flood prediction method and system based on a physical information neural network, specifically in the field of snowmelt flood prediction. By embedding physical constraints such as shallow water equations and energy conservation equations into the neural network model, the accuracy and stability of prediction are improved while maintaining physical consistency. It can effectively compensate for the shortcomings of traditional empirical models in terms of spatiotemporal continuity and generalization under extreme scenarios. At the application level, this invention can realize the fully automated operation of the entire process from meteorological data input, snowmelt and runoff calculation, flood evolution prediction to risk level assessment. The system can output multi-time step water depth, flow velocity and inundation range results based on real-time or forecast meteorological conditions, and combine them with population density, land use and infrastructure distribution data to generate flood risk zoning maps and early warning information, providing a scientific basis for watershed flood control scheduling, engineering design and disaster emergency management.

The invention relates to the technical field of environment monitoring and fishery resource management, in particular to a near-shore fishery environment safety evaluation method based on heavy metal risk, which comprises the following steps: S1, measuring the biological effective state concentration of target heavy metal, calculating a basic safety threshold value, and correcting the basic safety threshold value to generate a dynamic safety threshold value; s2, predicting the heavy metal concentration in the target fishery organism by taking the dynamic safety threshold as a reference, and generating a preliminary risk zoning map; and S3, according to the preliminary risk zoning map, making and implementing a corresponding fishery management strategy, regularly monitoring the heavy metal content in the actual fishery organism, and feeding back the monitoring result as new historical fishery management effect feedback data to S1 for adaptive adjustment of the dynamic safety threshold of the next evaluation period. And carrying out inversion calculation by constructing a migration enrichment model and combining a dynamic threshold value to obtain the predicted concentration of the heavy metal in the target fishery organism.

The invention relates to the technical field of chemical safety engineering and intelligent hazard source monitoring and early warning, and discloses a major hazard source detection and early warning management system for chemical enterprises. Marking coordinates in a methane pipeline according to risk zoning, and sampling and calibrating in a fixed period; determining an initial range according to history or reference plus noise, and building grids and priori; during sampling, a dynamic interval is obtained through observation and a previous boundary, and scale likelihood is constructed; initial priori is mapped at the first moment, the last posteriori is mapped subsequently, the posteriori is obtained through multiplication and normalization with likelihood, and anomaly rollback is carried out. Taking a fixed quantile concentration, and taking the right end when the right tail is insufficient; comparing with an upper limit to trigger a first level or record a difference value; triggering the second level according to the shortest continuous number of times and maintaining counting; and setting the average reconstruction density of the window mapping as the next priori, not fully using effective samples, and if the difference is large, prompting re-checking.

Embodiments of the present application disclose a reservoir over-standard flood inundation risk zoning method, device and equipment, by determining the reservoir siltation influence probability factor and the reservoir dam break influence probability factor under the siltation condition, the reservoir siltation influence probability factor and the reservoir dam break influence probability factor are used to calibrate the reservoir inundation comprehensive risk degree of siltation-dam break double influence probability under the over-standard flood condition, and the K-means clustering algorithm is used to cluster and analyze the reservoir inundation comprehensive risk degree of each grid calculation unit, and finally the flood risk grade is determined. The technical problem of low accuracy of prediction and zoning of flood inundation risk in the prior art for the reservoir area with high sediment concentration and weak dredging capacity is solved, and a scientific basis and certain technical support are provided for reservoir flood inundation risk assessment and disaster prevention and mitigation planning in high sediment concentration basin areas.

The present application relates to the technical field of suspended sediment dynamic monitoring, and particularly relates to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, comprising multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional sea current vector data and seabed microtopography slope data of a target area; plume flow dynamic diffusion simulation: inputting the acquired data into a microtopography coupled adaptive plume flow diffusion model to generate a plume flow diffusion three-dimensional space-time cloud atlas for the next 6 hours; dynamic risk zoning generation: performing space-time superposition analysis on the plume flow diffusion three-dimensional space-time cloud atlas and a preset ecological sensitive area dynamic electronic fence to generate an operation risk heat map; intelligent zoning scheduling execution: dynamically dividing a forbidden mining area, a buffer area and a safe area, and sending zoning interval rotation scheduling instructions to a mining equipment group. The present application improves the spatial resolution and time response capability of sediment diffusion behavior prediction, and provides a basis for environmental impact assessment and ecological protection.

The invention relates to the field of coalbed methane exploration risks, in particular to a coalbed methane exploration risk assessment method and system based on big data analysis. The method comprises the following steps: collecting coalbed methane data and monitoring productivity data of a coalbed methane exploration area, constructing a risk factor characteristic matrix, outputting an initial risk level and an enrichment potential value, and obtaining a coalbed methane risk partition model; analyzing the initial risk level and the monitored productivity data to obtain a well section level deviation value, and constructing a deviation chain graph structure; obtaining a factor deviation mapping table according to the deviation chain graph structure; analyzing the factor deviation mapping table to generate a candidate consensus factor set; performing verification analysis on the candidate consensus factor set to obtain a stable consensus factor group; constructing a staged evaluation model; and after each round of model updating, recording a global deviation change sequence of a predicted value and an actual value of the model, obtaining a convergence rate and an error variance fluctuation rate, and outputting a stable model set. The risk analysis capability can be improved.

This application provides a method and system for assessing earthquake-induced landslide risk in transportation networks in high-intensity seismic zones, relating to the field of landslide risk assessment technology. The method includes: screening key influencing factors of earthquake-induced landslides; optimizing the construction of positive and negative sample libraries; using a spatial weighted machine learning model to evaluate landslide susceptibility and generate a susceptibility zoning map; extracting parameters of potential landslide source areas based on the high-susceptibility zone identification results; using a parallel particle discrete element method to simulate the entire landslide movement process to obtain hazard parameters; inputting the hazard parameters into a transportation engineering structure vulnerability model to quantitatively calculate the failure probability; and fusing the susceptibility probability and structural vulnerability results to generate a risk zoning map based on road network units. The final results of this application are directly applicable to the operation and maintenance management of transportation infrastructure. The provided risk zoning map can be directly used to guide the optimal allocation of risk control resources, the formulation of emergency plans, and the seismic resilience design of engineering projects, demonstrating clear application value.

The invention relates to the field of data processing of disaster risk management and geographic information, in particular to a flood risk zoning method based on multi-source data entropy correction G1 empowerment. The method comprises the steps of obtaining multi-source data and performing metadata management to generate an original index set; performing quality control and aperture alignment to form a quality control index matrix; calculating entropy values based on the quality control data, and constructing an entropy difference set with difference measurement; generating an adjacent ratio mark set through stable item addition, approach threshold rollback, upper and lower limit truncation and monotone constraint; generating a weight vector by adopting hierarchical grouping and G1 inverted recursion; carrying out weighted summation to obtain a comprehensive risk scoring sequence; generating a five-level risk tag through K-means clustering; and finally, outputting a result data packet and a risk zoning map through GIS association visualization. According to the method, objectification, automation and traceability of weight distribution are realized through an entropy correction G1 weighting method, and the accuracy and practicability of flood risk zoning are remarkably improved.

The present application relates to a kind of gas hidden disaster-causing factor evaluation method, belong to coal mine safety technical field.It includes: obtaining the gas geological basic data and engineering disturbance data of the region to be evaluated;Gas geological basic data includes overlying coal seam goaf gas occurrence state parameter, engineering disturbance data includes mine mining connection plan;Based on the data obtained, construct comprehensive evaluation index system;The region to be evaluated is divided into several evaluation units, based on comprehensive evaluation index system and mine mining connection plan, apply the preset evaluation model, calculate the dynamic disaster-causing index of each evaluation unit in evaluation region;According to dynamic disaster-causing index, the risk level of evaluation region is divided, and the gas hidden disaster-causing factor risk zoning map is generated.The present application can realize the dynamization and visualization of evaluation result, improve the evaluation precision and prediction ability of hidden, coupled gas risk, provide scientific basis for targeted prevention and control.

The invention discloses a mechanical rapid supporting method for a high-crustal-stress rockburst tunnel, and relates to the technical field of tunnel excavation and supporting. The mechanical rapid supporting method comprises the steps that S1, early-stage monitoring and risk zoning are conducted; s2, a first-level flexible layer is constructed; s3, mounting and adjusting a secondary rigid layer; s4, performing real-time monitoring and dynamic regulation and control; and S5, checking the operation quality and circularly summarizing. According to the invention, based on a micro-seismic monitoring system and a fiber grating sensor, initial surrounding rock micro-seismic signals are collected, a rockburst risk area is identified in real time through full fusion of multi-dimensional data, a supporting mechanical arm is linked through a PLC control center, and dynamic adjustment of supporting is carried out and the supporting speed is increased through a multi-stage system supporting system and intelligent monitoring and feedback control; and the trolley integrates laser radar and background positioning, an operation path can be automatically planned according to a high-risk area defined by micro-seismic monitoring in the construction process, automatic obstacle avoidance is achieved, and therefore the damage rate of rockburst is reduced.

The invention relates to a comprehensive flood risk evaluation method for a plain area, which comprises the following steps of: constructing a three-order progressive flood risk evaluation index system comprising four criteria and fifteen indexes through information arrangement of a target area, establishing a fuzzy consistent judgment matrix by utilizing a fuzzy analytic hierarchy process, and scientifically quantifying index weights, so as to evaluate the flood risk of the target area. And carrying out overlay analysis and comprehensive risk grading on each risk factor index according to each calculated index weight by utilizing a GIS tool, and finally generating a flood risk zoning map of the target region. According to the method, risk assessment can be converted from qualitative description to quantitative and visual partition, so that the high-risk area of the target area is accurately identified, a methodological template is provided for risk assessment of similar areas, and the application range is wide.

The invention discloses a coastal to deep sea wind energy resource refined evaluation method and system, and relates to the technical field of renewable energy sources in the climate resource development and utilization field, and the method comprises the steps: constructing a Fujian province national meteorological observation station, an ocean buoy station, reanalysis data, an offshore anemometer tower and other multi-source gradient anemometer databases; the hourly 1 * 1km high temporal-spatial resolution 0-300m gradient wind energy resource fine evaluation method based on WRF numerical mode simulation of an optimization design scheme is developed; and researching and developing WTGS exceeding probabilities and thresholds of different levels of wind speeds during the influence of the tropical cyclones, determining available wind speed, early warning wind speed and defense wind speed regions for wind resource development, and drawing a risk zoning map for development and utilization of wind farms from coastal to deep sea. Meanwhile, an offshore wind energy resource refined evaluation system is researched and developed, wind energy resource refined evaluation and developable potential estimation at different heights from the coastal sea to the deep sea are achieved, and wind energy resource development and utilization risk level zoning at any grid point in different sea areas is achieved.

The application discloses a forestry disease and pest control method based on big data mining, which comprises the following steps: collecting and preprocessing multi-source heterogeneous data of a target forest area; extracting multi-source features and performing fusion processing according to the correlation of ecological factors; performing spatio-temporal correlation analysis and constraint fusion processing based on the multi-source fusion feature set; performing spatio-temporal prediction modeling and result analysis on the comprehensive representation sequence of diseases and pests based on an improved AGCRN model, and generating disease and pest prediction results; performing risk zoning and control decision determination processing on different monitoring areas in the target forest area; constructing a control decision mapping relationship and generating a control strategy set; constructing a strategy evaluation index and performing comprehensive evaluation and sorting optimization to determine the target control strategy. The application adopts a multi-source data fusion and spatio-temporal correlation modeling method, realizes accurate prediction of diseases and pests and optimization of control strategies, and has the advantages of high prediction accuracy, strong decision scientificity and high resource utilization efficiency.

The invention belongs to the technical field of environmental risk assessment, and relates to a method for zoning assessment of heavy metal pollution environmental risks in a metal mine area. The method provided by the invention is established by referring to pollution characteristic analysis, combining risk evaluation, referring to national technical guidelines and regulations, existing related literatures and books and expert consultation methods. The pollution characteristic analysis part is completed through Meta analysis and hot area analysis, and the index weight calculation part is completed through combination of an analytic hierarchy process and expert consultation. The method is based on an environmental risk evaluation theory, follows a'risk source risk-diffusion migration capability-sensitive receptor vulnerability 'model, and fully considers natural environment characteristics and surrounding environment conditions of different regions throughout the country and pollution risk source danger, diffusion migration capability and sensitive receptor vulnerability characteristics of different mining areas. Therefore, the method can be suitable for various metal mine areas across the country. By dividing the heavy metal environmental pollution risk of the target metal mine area into four areas (a low risk area, a medium risk area, a high risk area and an extremely high risk area), the remediation of different priorities can be carried out for different areas according to different zoning results, the determination of a remediation sequence and the selection of a remediation method are facilitated, and reasonable management and control measures are proposed.

This invention belongs to the field of power system safety technology. It discloses a safety assessment method for high-voltage transmission lines based on intelligent algorithms. The method constructs a database through multi-source data acquisition and fusion processing, utilizes deep learning models to analyze the correlation between meteorological conditions and icing formation mechanisms and explore their evolutionary patterns, and combines the physical characteristics of transmission lines and topographic information to conduct multi-scenario simulations and sensitivity analyses, establishing a risk zoning assessment system for transmission line icing. This method calculates the critical values ​​of line mechanical strength and icing thickness based on a mechanical model, formulates graded early warning threshold standards, constructs a time-series prediction algorithm and a risk propagation model to achieve intelligent early warning, and forms a prevention and control strategy through optimized allocation of anti-icing resources and adaptive generation of de-icing schemes. This invention effectively improves the ability of high-voltage transmission lines to resist icing disasters and enhances the safe and stable operation level of the power grid.

The invention relates to the technical field of digital twinning intelligent mining, in particular to a digital twinning-based gold mine working face self-adaptive mining system. And the geological modeling module constructs a composite geological twinborn body fusing a rock mass damage quantification field, an ore body topological structure and a weak plane permeability tensor. And the mining optimization module performs closed-loop calculation based on the twinborn body, and outputs a mining parameter dynamic scheme and a geological risk zoning map. And the aging correction module performs long-term mechanical feedback correction on the mining scheme according to the matching relationship between the rock mass rheological property and the stress field evolution trajectory. And the collaborative intervention module generates a regulation and control instruction according to the risk map. According to the system, self-adaptive optimization of the mining process on the dynamic change of the geological environment and advanced accurate intervention of engineering risks are realized.