114 results about "Risk zoning" patented technology

The invention provides an underground water pollution source tracing and evaluation method for a riverside water source in a karst area, and the method comprises the following steps: collecting and preprocessing multi-source information, and obtaining a multi-source data set; a water quality sample is collected and analyzed, the surface water-underground water interaction process and pollution characteristics are comprehensively analyzed, and a pollution source type is obtained; performing space-time analysis on the multi-source data set to obtain a pollution activity time period and a potential pollution source area; constructing a karst area hydrological model, simulating the hydrological characteristics of the karst area, constructing a pollutant migration model, and simulating the migration process of pollutants; and constructing a traceability algorithm, tracking pollutants, confirming a pollution source, carrying out risk partitioning and risk assessment, and carrying out visual display of a result. According to the invention, interaction between surface water and underground water can be effectively assessed, accurate traceability of pollutants and multi-angle accurate identification of pollution sources can be realized, and management efficiency can be optimized through reliable risk assessment prediction.

The invention discloses a forest fire risk assessment method based on a composite chain type disaster evolution mechanism, and relates to the field of forest fire risk assessment, and the method comprises the steps: carrying out the chain type feature field construction of multi-source heterogeneous data through the collection of the multi-source heterogeneous data, including remote sensing data, meteorological data, geographic information data and disaster loss data, obtaining a disaster chain core driving factor tensor, and carrying out the feature field construction of the multi-source heterogeneous data; performing composite disaster chain coupling analysis based on the disaster chain core driving factor tensor to obtain a disaster chain space-time cascade trajectory, performing dynamic risk assessment and grading based on the disaster chain space-time cascade trajectory to obtain a five-level risk zoning map, and performing intelligent early warning analysis based on the five-level risk zoning map to obtain a forest fire dynamic early warning map. According to the method, the chain type driving relation among drought, high temperature, vegetation dryness and fire danger is synthesized, the dynamic threshold value is adopted for risk grading, the dynamic influence of climatic variation and vegetation phenology on the fire danger can be captured, and the accuracy of forest fire risk assessment is improved.

The invention belongs to the technical field of risk prediction, and discloses a landslide risk prediction method and system based on data intelligent analysis, and the method comprises the steps: obtaining corresponding multi-source heterogeneous information, carrying out the feature extraction and data fusion of the information, and obtaining a mountain monitoring basic data set; constructing a mountain twinborn feature model based on the mountain monitoring basic data set, and performing landslide scene simulation based on the mountain twinborn feature model to obtain corresponding risk feature information; constructing a trigger factor association network based on the risk feature information, and performing zoning evaluation and grading on the target landslide risk in combination with the mountain monitoring basic data set; constructing a corresponding grading risk zoning map; obtaining a landslide risk dynamic prediction result based on the grading risk zoning map; a corresponding grading early warning rule base is constructed; and the target mountain area is monitored in real time based on the grading early warning rule base, and the monitoring result is fed back. According to the invention, a comprehensive and efficient solution is provided for prevention and treatment of landslide disasters.

The invention provides a drainage basin mountain torrent debris flow physical process simulation method for risk early warning, which comprises specific research and development contents and corresponding problems and achieves the following purposes: constructing a drainage basin unit database, clarifying drainage basin and channel characteristics, and providing data support based on physical process refined simulation; and carrying out basin hydrological process simulation research on the basis of the SWAT distributed model for improving glacier melt water simulation. A mountain torrent and debris flow disaster whole-process coupling model is constructed, a dynamic data chain is used for connecting watershed hydrology and a channel water and sediment dynamic process, full-link simulation from rainfall to disaster modeling is achieved, a disaster forming mechanism is accurately revealed, a visualization system is integrated through the model, channel disaster dynamic evolution evaluation and risk zoning functions are provided, and the reliability of channel disaster dynamic evolution evaluation is improved. A forecasting system designed in an object-oriented development mode can be seamlessly accessed to a provincial early warning platform, and a systematic solution is provided for early warning of watershed or regional disasters.

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 embodiment of the invention provides a risk zoning method and device for helicopter flight, equipment and a storage medium. The method is applied to the technical field of data processing, and comprises the following steps: carrying out classification and threshold division on meteorological elements according to a fusion data set and different flight scenes to obtain a meteorological element threshold breakpoint set; based on the meteorological element threshold breakpoint set, performing frequency statistics and data synthesis on the fused data set to obtain a multi-layer meteorological element frequency data set; obtaining an initial clustering result of each meteorological element through clustering analysis according to the meteorological element frequency data set; according to the initial clustering result, carrying out risk grade division on each meteorological element to obtain a standardized single-element risk zoning result; and based on a single-element risk zoning result, adopting a multi-element fusion method to obtain a comprehensive risk zoning map. In this way, the technical problem that in the prior art, due to the fact that multiple elements jointly participate in clustering, the risk level is difficult to judge can be solved.

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 provides an evacuation path optimization method based on coupling numerical simulation and hierarchical reinforcement learning, and relates to the field of storm surge, and the method comprises the steps: constructing a coupling numerical simulation framework of high-precision storm surge; performing storm surge numerical simulation through a coupling numerical simulation framework to generate a submerged depth data set; performing high-resolution submerged depth interpolation through the submerged depth data set in combination with an inverse distance weighted interpolation algorithm to generate a high-resolution submerged depth interpolation data set; performing risk grading on the high-resolution submerged depth interpolation data set to generate a risk zoning map; generating an evacuation road network through a path planning neural network model based on a graph neural network and the risk zoning map; an evacuation strategy of an evacuation road network is optimized by adopting a hierarchical deep reinforcement learning framework, a combined optimal dynamic evacuation strategy is generated, and the generalization performance and the calculation efficiency of evacuation path planning are remarkably improved.

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 geotechnical engineering, and provides a marine facies soft soil deformation characteristic evaluation method based on a disturbance state concept, comprising the following steps: step 1, constructing a constitutive model based on the disturbance state concept; 2, acquiring natural physical and mechanical parameters of the marine soft soil based on indoor test data; 3, based on the natural physical and mechanical parameters, the disturbance process of engineering construction on the marine facies soft soil stratum is simulated step by step, and calculation variables of all stratum units in the simulation process are extracted; 4, identifying potential damage units in each stratum by adopting a dual-threshold damage judgment method; and step 5, based on the spatial distribution of all potential damage units, generating a stratum damage distribution cloud picture, and realizing visual identification and partition grading of the engineering risk area. According to the scheme, a coupling criterion among structural evolution, energy dissipation and plastic deformation is constructed, and meanwhile quantitative recognition and risk zoning of potential damage areas and key deformation areas in the soft soil stratum are achieved.

The invention relates to the technical field of natural disaster risk assessment, in particular to a sandstorm disaster risk zoning method, which comprises the following steps of: performing comprehensive risk assessment by combining a disaster-inducing factor risk index, a disaster-pregnant environment sensitivity index, a disaster-bearing body vulnerability index and a disaster prevention and reduction capability index and adopting a weighted synthesis method to obtain a sandstorm disaster risk zoning map; and then a sandstorm hazard source can be determined through a sandstorm disaster risk area, that is, the wind speed reaches a main area in which the surface sand is generated, so that the degree of influence and harm caused by sandstorm diffusion to the environment, animals, plants and human beings can be correctly evaluated, a scientific basis is provided for formulating targeted risk management measures, and the method is suitable for popularization and application. Therefore, the technical problem that the life and property of people cannot be safely guaranteed due to the fact that no assessment method capable of comprehensively considering the risk of disaster-inducing factors, the vulnerability of disaster-bearing bodies and the risk area prone to sand storm exists at present is effectively solved.

The invention discloses a railway line heavy rainfall disaster risk zoning method based on a combined weight analysis method, and relates to the technical field of railway line heavy rainfall disaster risk assessment, and the method comprises the following steps: determining a heavy rainfall disaster risk evaluation index according to an established railway line heavy rainfall disaster risk model; acquiring data of related three-level indexes by utilizing ArcGIS software; performing region segmentation on the target line, firstly determining the positive and negative directivity of the three-level indexes for each small region, and then performing corresponding normalization processing on the three-level index data; performing subjective weight scoring on the importance of each three-level index by using a traditional analytic hierarchy process; carrying out objective weight assignment on the importance of each three-level index by using an entropy weight method according to the data; according to the method, the entropy weight method is introduced to neutralize the subjectivity of the weight obtained by the analytic hierarchy process, and meanwhile, the data acquisition is also based on the generation of GIS software, so that the final risk division has higher objectivity and accuracy.

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.

The invention provides a power grid icing disaster risk assessment method and system, and the method comprises the steps: constructing a multi-source data system through the deployment of a customized meteorological terminal, the combination of FNL reanalysis data, power grid disaster situation and operation and maintenance data, improving the data precision through a Kalman filtering algorithm and a dynamic downscaling method, screening icing key disaster-causing factors through partial correlation analysis, and achieving the risk assessment of the icing disaster. According to the method, disaster levels are divided, risks are quantified, a visual platform is built based on the WebGIS technology, multiple types of data are integrated to achieve multi-layer display and interaction, the problem that the spatial-temporal scale of traditional meteorological data is not matched with the power grid requirement is solved, the method better meets the special requirement of the power grid, differentiated emergency measures are made in an assisting mode, and the disaster prevention efficiency is improved; the method comprises the following steps: step 1, collecting data, and constructing a multi-source data system; 2, processing data, and improving forecasting precision; 3, screening icing key disaster-inducing factors by adopting partial correlation analysis; 4, dividing disaster grades, quantifying risks, and drawing a risk zoning map of the disasters; and 5, carrying out visual display and early warning.

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.