729 results about "Sensitive analysis" patented technology

The invention relates to the technical field of smart power grids, and discloses a smart power grid power dispatching optimization method, which comprises the following steps of: firstly, acquiring power grid operation data, and processing data missing and noise problems by utilizing federal learning; and constructing a load prediction model through a dynamic time warping algorithm and a specific network. A multi-energy coupling scheduling model and a demand response game model are constructed, and a multi-time scale rolling optimization framework is established. And carrying out sensitivity analysis on scheduling parameters, designing a hierarchical collaborative optimization mechanism, and constructing a robust optimization model to cope with the power flow uncertainty. And integrating a scheduling instruction verification module, and deploying an online incremental learning mechanism. The method can effectively process data, accurately predict load, optimize multi-energy scheduling, guide demand response, deal with uncertainty, verify scheduling instructions and update the model in real time, improves the safety, reliability and economy of smart grid power scheduling, and realizes optimal configuration of power resources.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The invention relates to the technical field of coupling simulation, in particular to a river and underground water coupling simulation parameter generation method and system. The method comprises the following steps: collecting remote sensing and ground sensing monitoring data, and generating a multi-source space-time initial data set through time mark calibration, space resampling and signal-to-noise ratio weighted fusion; extracting features between the water level of the river and the water level of the underground water based on the data set, constructing a river-underground water space-time topological structure and identifying an interaction mode, and forming a space-time coupling feature map through significance screening and periodic enhancement; performing parameter inversion by adopting a hydrodynamic equation, and generating a physical inversion parameter set in combination with sensitivity analysis and local optimization; a parallelization hydrological simulation framework is constructed, error-driven optimization is executed, and dynamic optimization parameters are obtained; river and underground water exchange simulation is executed, and finally, coupling simulation parameter set updating is achieved through sliding window deviation evaluation and incremental parameter correction. Therefore, the precision and operability of a coupling simulation result are improved.

The invention provides an asset library model security risk situation assessment system and method, and the system comprises a data collection and processing subsystem, a multilayer asset association graph construction module which constructs the function dependence, data flow dependence and physical connection relation of assets in a resource library based on a graph database; the risk propagation probability calculation module is used for simulating a conduction path of an attack from an edge node to a core resource by adopting a Monte Carlo method; and the time sensitivity analysis module is used for dynamically adjusting threat weights in different time periods through a sliding window mechanism. According to the method, real-time comprehensive data is provided for evaluation through a multi-source streaming data fusion engine and distributed edge computing nodes; accurately analyzing asset association and risk propagation by applying a multi-layer asset association map and a Monte Carlo method; by means of a reinforcement learning decision framework and incremental model updating, self-adaptive decision optimization is achieved, and novel attacks are coped with in time; based on a visual interface and a closed-loop feedback control loop, the visual effect and the evaluation accuracy are improved.

The invention relates to the technical field of layered surrounding rock inversion analysis, and discloses a layered surrounding rock three-dimensional crustal stress field inversion intelligent analysis system and method, and the system comprises a dimensional geological value module, an intelligent inversion analysis module and an inversion result verification module. The method comprises the steps of collecting geological data in target layered surrounding rock; carrying out normalization processing on the geological data, and carrying out sensitivity analysis; constructing a three-dimensional geologic model, and performing numerical simulation; a crustal stress field inversion intelligent analysis model is generated based on the three-dimensional geologic model; training the crustal stress field inversion intelligent analysis model by using the training sample; inputting the real-time geological data into the crustal stress field inversion intelligent analysis model to obtain a crustal stress inversion analysis result; carrying out visual output on the ground stress analysis result; and adjusting the crustal stress field inversion intelligent analysis model according to the actually measured data. Scientific ground stress field analysis basis can be provided for complex projects such as tunnel engineering, underground engineering and hydropower stations.

The invention relates to the technical field of hydrogen production, in particular to a bipolar plate runner structure-mass transfer efficiency parameter simulation optimization method. Comprising the steps that a flow channel structure is designed, specifically, a bipolar plate flow channel is constructed through a fractal tree-shaped network topological structure, the flow channel is composed of multiple stages of branch channels, the section size of each stage of branch channel is decreased progressively according to a self-similarity proportion, and a three-dimensional flow channel network with fractal dimensions is formed; multi-physics field coupling modeling: establishing a multi-physics field coupling model including fluid flow, electrochemical reaction and heat and mass transfer based on computational fluid mechanics and a finite element method, wherein the model considers a turbulence effect in a flow channel, interface impedance of a porous medium diffusion layer and reaction kinetic parameters at the same time; parameter sensitivity analysis: screening key parameters influencing mass transfer efficiency through orthogonal test design, including flow channel fractal dimension, branch angle, porosity and surface wettability parameters, and constructing a parameter response curved surface; the flow channel structure of the bipolar plate can be accurately optimized, and the mass transfer efficiency is effectively improved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention relates to the technical field of multivariable analysis, and discloses a method and a device for improving the strength of flow-state solidified soil based on multivariable analysis, and the method comprises the following steps: collecting a material proportion parameter matrix, a stirring parameter matrix and an environment monitoring data matrix in flow-state solidified soil equipment, and executing feature extraction and feature integration; obtaining a target feature vector; performing intensity prediction of the time sequence on the target feature vector to obtain an intensity prediction value of the flow-state solidified soil; sensitivity analysis is carried out based on the intensity predicted value, and an intensity improvement potential index, a parameter adjustment stability index and an energy consumption influence index are obtained; according to the strength improvement potential index, the parameter adjustment stability index and the energy consumption influence index, adaptive parameter optimization is carried out through discount inverse reinforcement learning, and a target control parameter set is obtained. And the strength and the quality uniformity of the flow-state solidified soil are effectively improved while the operation stability is guaranteed.

The invention belongs to the technical field of industrial waste heat recovery, and discloses a flue gas waste heat recovery system optimization design method considering external parameter changes. The method comprises the steps of system monitoring data acquisition and feature extraction, thermodynamic modeling and parameter sensitivity analysis, external environment parameter and system performance coupling mechanism modeling, equipment failure prediction and reliability constraint determination, multi-objective optimization and self-adaptive control strategy generation and the like. Optimized operation of the flue gas waste heat recovery system under the condition of dynamic change of external environment parameters is achieved. According to the method, a dynamic influence matrix of external environment parameters and system performance is initiatively established, the reliability of the system is evaluated in combination with a stress-life analysis technology, efficiency and cost targets are balanced through a multi-target optimization algorithm, and a self-adaptive control strategy library for different working conditions is generated. The adaptability and stability of the system under complex working conditions are improved, the service life of equipment is prolonged, and the operation cost is reduced.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention relates to the technical field of optimization control, in particular to an operation parameter optimization control method and system for an air condenser, and the method comprises the steps: firstly obtaining environment data, processing the environment data to obtain a self-adaptive environment monitoring value, and constructing a non-uniform frequency conversion strategy according to the self-adaptive environment monitoring value to control the optimization frequency of the operation parameters of the air condenser; then obtaining structural features and operation features of the air condenser, obtaining a first to-be-optimized parameter vector according to a principal component analysis method, obtaining the first to-be-optimized parameter vector, and carrying out sensitivity analysis to obtain a second to-be-optimized parameter vector; and then the optimization parameter prediction model predicts a parameter value vector of the second to-be-optimized parameter vector, the distributed parameter optimization control model sets a local agent of the to-be-optimized parameter, the parameter value vector of the second to-be-optimized parameter vector is acquired, and parameter optimization control is performed through a reinforcement learning algorithm. According to the method, through optimal control over the operation parameters, the parameter optimization speed and stability are improved, and the energy consumption of the air condenser is reduced.

The invention provides an artificial intelligence auxiliary antenna design method based on a Gaussian regression process, and the method comprises the steps: employing a script interface to interact with a simulation platform, automatically completing the parameterized modeling process of an antenna model, and setting an excitation condition and a radiation boundary; a multi-dimensional space stratified sampling method is adopted to generate a uniformly distributed sample point set in the multi-dimensional parameter space, and electromagnetic simulation is carried out; preprocessing the data output by simulation to form a data structure suitable for machine learning model processing; integrating the trained MLNN model into a grey wolf optimization algorithm, and optimizing antenna design parameters; and performing electromagnetic simulation on the optimal parameter obtained by optimization, comparing a prediction result with actual simulation data, and verifying the performance of the optimization engine in the target frequency band. According to the method, the problems of insufficient sample generation efficiency, insufficient parameter sensitivity analysis, limited model generalization ability, low convergence speed of an optimization algorithm and the like in the prior art can be solved.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

The present invention discloses a method for predicting the static behavior of a bridge based on dynamic loading, comprising the following steps: establishing a full-bridge structural analysis model according to the design data of the existing bridge; conducting dynamic loading tests on the existing bridge to obtain the bridge dynamic response values; performing sensitivity analysis on each design parameter in the initial structural analysis model to obtain and determine the key design parameters to be corrected that affect the bridge structure; based on the uniform design sampling method, using machine learning intelligent algorithms, constructing training samples of the key design parameters to be corrected and establishing a surrogate prediction model; correcting the initial structural analysis model using the prediction results of machine learning and intelligent algorithms; predicting the static behavior of the bridge based on the corrected structural analysis model; introducing an error analysis method to evaluate the prediction results of the bridge static force. The beneficial effects of the present invention are: reducing the cost of the bridge static load test, reducing the damage to the bridge's own structure, and having high prediction result accuracy.

The invention discloses a farmland soil humidity intelligent monitoring system based on the Internet of Things, and relates to the field of resource optimization decision crossing, a soil humidity monitoring module executes a soil humidity adjustment plan, monitors farmland soil humidity time sequence data in real time, and obtains a farmland soil humidity monitoring result through time sequence analysis and K-means clustering; obtaining an association rule between the soil humidity time sequence data and the crop yield; and the humidity monitoring report generation module is used for optimizing the soil humidity adjustment plan by tracking an optimal soil humidity threshold value in real time by utilizing a dynamic programming hybrid optimization algorithm according to the optimal soil humidity range, and generating a farmland soil humidity monitoring report. Through an optimization model coupled by a crop moisture production function and a multi-objective genetic algorithm, and in combination with Monte Carlo global sensitivity analysis, an optimal soil humidity range is identified, so that accurate quantitative modeling of a humidity-yield nonlinear relationship is realized, the defect of a static soil humidity threshold is avoided, and the sensitivity of moisture stress early warning is improved.

The invention provides an optimal configuration method of an off-grid wind power hydrogen production system considering full life cycle income, which comprises the following steps: receiving original data, carrying out abnormal value detection and missing value repair, obtaining multi-scale component data through empirical mode decomposition, and generating a time sequence characteristic matrix; extracting equipment performance characteristic parameters and establishing a performance prediction model; constructing a system dynamic model based on a Bond Graph method, and performing stability analysis; calculating all costs and system income of the whole life cycle, and generating a comprehensive evaluation matrix in combination with risk analysis; a multi-objective optimization function is constructed, a grey wolf algorithm is adopted for solving, and an optimal configuration scheme is generated; and performing simulation verification and sensitivity analysis on the optimal configuration scheme to generate a final configuration scheme. According to the method, the equipment performance attenuation law and the system dynamic characteristics are fully considered, and the accuracy and reliability of optimal configuration are improved.

The invention provides an anti-seismic toughness analysis method and system for a segment assembly type railway high pier node, and relates to the technical field of computers.The method comprises the steps that basic parameters of a target segment assembly type railway high pier node are obtained, and the basic parameters comprise material parameters, pier body geometric parameters, seismic oscillation parameters and node response parameters; performing multi-physical field coupling tensor decomposition according to the basic parameters to obtain a multi-field coupling feature tensor; performing topological modeling according to the multi-field coupling feature tensor to obtain a time-varying graph structure model; performing damage evolution simulation according to the time-varying graph structure model to obtain an anti-seismic toughness quantitative index; according to the anti-seismic toughness quantitative index, a failure mode analysis result is obtained through modal sensitivity analysis and failure path extraction; and performing evaluation according to the failure mode analysis result to obtain a node anti-seismic toughness analysis result. According to the invention, by introducing the multi-physics field coupling tensor decomposition and time-varying graph structure model, the response of the node in a complex earthquake environment can be simulated more accurately.

The invention discloses a salinization farmland crop yield prediction method, which is applied to the technical field of crop yield monitoring and prediction, and comprises the following steps: based on a vegetation index and a salinity index, inverting salinity by using a partial least square regression method; inverting the soil moisture based on an empirical relation model of the conditional vegetation temperature index and the actually measured soil moisture; inverting the leaf area index based on an empirical relation model of the normalized vegetation index and the actually measured leaf area index; the method comprises the following steps: constructing an HYDRUS-1D-EPIC coupling model considering salinity, carrying out sensitivity analysis by using an extended Fourier amplitude sensitivity test method, carrying out parameter calibration and posterior distribution inference on the coupling model by using an MCMC method, and sampling posterior distribution by using a DREAM method; and assimilating the inverted salinity, soil moisture and leaf area index to the coupling model by using an ensemble Kalman filtering method, and predicting the crop yield of the salinized farmland. According to the method, the yield prediction precision of the salinized crops is effectively improved.

The invention discloses a water quality prediction method based on machine learning and an SWAT model, and belongs to the technical field of water environment simulation and water quality early warning. The method comprises the steps that S1, hydrology, water quality, meteorology, land utilization types, pollution sources and spatial elevation multi-temporal data are collected from multiple channels in a drainage basin, and noise reduction, normalization and missing value filling preprocessing methods are adopted for original data; s2, performing sensitivity analysis on the nonlinear relationship between the SWAT model parameters and the output by adopting SVR, and screening out key parameters with remarkable influence; s3, on the basis of the water quality monitoring data, constructing a water quality time sequence prediction model by adopting an LSTM model; and S4, constructing a self-learning mechanism based on the LSTM and the SVR model, and retraining the model by adjusting a learning time window and regularly utilizing latest data. The method is excellent in the aspects of model efficiency, prediction precision and application adaptability, and has remarkable engineering application value and popularization potential.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention provides a rock-fill dam concrete panel cracking reliability calculation method and system, and the method comprises the steps: determining test parameters based on obtained physical parameters of a rock-fill material used by a rock-fill dam; duncan E-B model parameters are determined based on the test parameters, factor sensitivity analysis is conducted on the Duncan E-B model parameters based on an orthogonal test method, and sensitivity parameters are determined; carrying out random field characterization on the sensitivity parameters by using a normal random field, and discretizing random field data by using a Cholesky decomposition method to obtain a sample set and a test set of a training agent model; based on a Kriging model constructed by the sample set, performing optimization iteration through MPSO and a learning function to obtain an optimal agent model, and evaluating the optimal agent model; inputting the test set into the optimal agent model to calculate to obtain a predicted value and an evaluation index, establishing a structural performance function, and calculating to obtain the panel cracking reliability by adopting an important sampling subset simulation method; according to the method, the accuracy of the cracking reliability is improved.

The invention discloses a slope anchor cable support optimization design method based on global sensitivity analysis, and belongs to the technical field of engineering geology. The method comprises the following steps: screening parameters by using a local sensitivity analysis method to determine parameter subsets; secondly, in the anchor cable anchoring force loss calculation model, verifying and calibrating the parallel connection mode of the anchor cable body and the West original rock-soil body rheological model, designing a plurality of sensitivity analysis schemes, and constructing a joint probability density function of uncertainty parameters; and finally, establishing an anchor cable layout and parameter optimization model by taking maximum slope stability reliability as an optimization target and considering constraint conditions such as engineering cost and construction difficulty, and formulating corresponding anchor cable anchoring force compensation measures according to changes of key uncertainty parameters. According to the scheme, the stability risks of the side slope under different working conditions are evaluated more accurately, the engineering cost is effectively reduced and the construction difficulty is reduced on the premise of guaranteeing the stability of the side slope, and the stability of the side slope is jointly improved by designing all links from design to construction.

The invention discloses a concrete arch dam global structural mechanical parameter zoning rapid inversion method, and relates to the technical field of dam operation safety monitoring and management, and the method comprises the steps: building a dam body and foundation three-dimensional finite element model through finite element software according to engineering design and monitoring data, and building a foundation three-dimensional finite element model according to damming material mechanical parameter information; partitioning the three-dimensional finite element model, analyzing the sensitivity of mechanical parameters of each region, determining sensitive mechanical parameters influencing the deformation of the concrete arch dam, and carrying out self-adaptive intelligent sampling on the sensitive mechanical parameters according to a sensitivity analysis result, and constructing a deep learning agent model reflecting a nonlinear relationship between the sensitive mechanical parameters of the dam and the deformation of each monitoring point, and carrying out deep learning inversion on the elastic modulus of the dam body and the deformation modulus of the bedrock. According to the invention, the method can efficiently and accurately invert and determine the structural mechanical parameters of the arch dam in the actual operation period, and provides a good basis for the safety analysis of the dam.

The invention discloses a full-flow intelligent regulation and control method and equipment for sewage treatment and a medium, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring basic data of a sewage treatment plant to be regulated and controlled, and constructing a corresponding sewage treatment model based on the basic data; based on the sewage treatment model, determining input parameters and output indexes in the sewage treatment process; respectively disturbing each input parameter by utilizing a preset global sensitivity analysis method, determining key input parameters of which the sensitivity indexes are greater than a preset threshold value, and constructing a lag disturbance model of each key input parameter; monitoring data in the sewage treatment process are collected in real time, and real-time lag effluent quality prediction is carried out according to the monitoring data and the lag disturbance model; and judging whether the real-time lagging water quality prediction data exceeds an effluent water quality limit value or not, and performing control parameter dynamic optimization and regulation on the sewage treatment model based on a preset multi-target optimization algorithm under the condition that the real-time lagging water quality prediction data exceeds the effluent water quality limit value.

The invention provides a water-wind-light integrated system optimization scheduling method considering source load uncertainty. Based on historical output data of wind power, photovoltaic and hydropower, constructing a three-dimensional joint probability density function, and generating multiple groups of typical scheduling scenes; establishing a water-wind-light integrated dispatching optimization model; solving the scheduling optimization model by adopting an improved NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; and carrying out source load sensitivity analysis based on the optimal solution set, identifying the influence weight of the load fluctuation rate and the wind-solar prediction error on the system operation, and dynamically adjusting the hydropower output strategy according to the influence weight so as to improve the stability and adaptability of the system under the extreme load change. The method can effectively cope with the dual uncertainty of wind power, photovoltaic output and load demand, and improves the scheduling feasibility and renewable energy utilization rate of the water-wind-light integrated system.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention relates to the technical field of fluid-solid coupling, in particular to a rock-soil body fluid-solid coupling simulation method and system. Firstly, a fluid-solid coupling model is constructed according to a rock-soil body balance equation, a seepage continuity equation and a permeability function considering deformation influence, and the fluid-solid coupling model can realize bidirectional coupling of a stress field and a seepage field, so that the fluid-solid coupling process of the rock-soil body is more accurately simulated through the fluid-solid coupling model; in addition, the fluid-structure interaction model uses many parameters, including mechanical parameters and permeability parameters, so that the inversion efficiency is low; according to the method, an orthogonal test method is adopted to carry out sensitivity analysis on the fluid-solid coupling parameters of the rock-soil body, parameters sensitive to monitoring data are obtained, only target parameters are inverted, the number of the inverted parameters is reduced, and therefore the parameter inversion efficiency is remarkably improved; the invention further provides a model parameter inversion method based on the long and short term memory neural network.

The invention relates to the technical field of mining subsidence prediction, and discloses a complex working face mining subsidence prediction method and system.The complex working face mining subsidence prediction method comprises the steps that a geological feature coding network is constructed, and complex geological information is converted into digital feature vectors; constructing a subsidence parameter inversion model based on deep learning; the problem of insufficient new mining area data is solved by applying a transfer learning framework; analyzing and optimizing model performance through parameter sensitivity; efficient subsidence calculation is realized based on a non-uniform block model; according to the method, the limitation that a traditional subsidence prediction method depends on empirical parameters is broken through, the prediction precision under the complex geological condition is improved, the dependence on monitoring data is reduced, the calculation efficiency of a large-scale block model is improved, the multi-working-face superposition subsidence effect can be accurately simulated, and the method is suitable for being popularized and applied. Powerful support is provided for safe mining of mines and environmental protection.

The invention discloses a risk assessment method and system based on a Bayesian network and an evidence theory, and relates to the technical field of risk intelligent assessment, and the method comprises the steps: determining an assessment dimension and an assessment index of a high-investment low-income risk of an educational institution in MOOC learning according to a human-cargo-field model and an industry report; constructing a MOOC learning risk assessment index system according to the assessment dimensions and the assessment indexes; constructing a Bayesian network structure according to the MOOC learning risk assessment index system; obtaining questionnaire data and expert opinions according to the MOOC learning risk assessment index system, and determining Bayesian network parameters based on the questionnaire data and the expert opinions; and inputting the Bayesian network parameters into the Bayesian network structure to obtain an assessment result of the high-investment low-income risk, the assessment result including a risk prediction result, a key risk factor and a sensitivity analysis result. According to the method, the high-investment and low-income risk in MOOC learning can be accurately evaluated.