938 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 relates to the technical field of carbon emission management, in particular to an expressway construction project carbon emission accounting evaluation method and system, and the method comprises the steps: dividing a system boundary into stages of an expressway construction full life cycle; carbon emission sources are divided into direct emission and indirect emission, and a source list of carbon emission in each stage and corresponding activity data are obtained; selecting carbon emission factors of various emission sources, constructing a carbon emission accounting model, and calculating the carbon emission of each stage; performing uncertainty analysis and sensitivity analysis on various emission source carbon emission factors and activity data, and optimizing a carbon emission accounting model; and performing comprehensive evaluation on each stage of highway construction to obtain an evaluation result of each stage. According to the invention, the problems of lack of full life cycle coverage and insufficient accounting precision of the existing highway construction carbon emission accounting method are effectively solved, the data accuracy and the model reliability are improved, and the carbon emission management and low-carbon construction targets are comprehensively supported.

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 provides a data synchronization method and system between trusted DCS terminals, and relates to the technical field of data processing, and the method comprises the steps: a server receives a data synchronization request, comprehensively analyzes historical behavior data and equipment fingerprint features, generates a dual anomaly detection result, judges the rationality of timestamp data, and if the data is reasonable, sends the data synchronization request to a server; if yes, the server obtains a public key from a block chain network for encryption to obtain first verification information and multiple pieces of second verification information; performing sensitivity analysis, generating a sensitive data subset and a non-sensitive data subset, performing disturbance processing on the sensitive data subset, generating a disordered sensitive data subset, performing encryption, generating a data synchronization message, and sending the data synchronization message to a target terminal; performing decryption to obtain the first verification information and the second verification information, performing verification, de-duplication detection and shared decryption to obtain to-be-synchronized data, performing synchronization, generating a synchronization completion voucher, uploading the synchronization completion voucher to the block chain network, analyzing a data synchronization condition, generating a synchronization analysis report and sending the synchronization analysis report to the first terminal.

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 territorial space planning, in particular to a construction land scale prediction method based on territorial space planning. The method comprises the following steps: acquiring historical construction land data, regional economic index data and control line space data of different regions; performing potential development turning point identification according to historical construction land data and regional economic index data, and performing stage trend analysis to obtain dynamic stage trend data; multi-scene prediction is carried out according to the dynamic stage trend data, so that multi-scene prediction data are obtained, and multiple scenes comprise a reference scene, a transformation scene and an innovation scene; and constructing an industry-land demand sensitivity analysis model according to the regional economic index data and the historical construction land data. According to the method, through development turning point identification and stage trend analysis, the dynamic characteristics of construction land change can be better grasped.

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 belongs to the field of intelligent construction, and discloses a deep foundation pit displacement prediction method combining finite element and data driving, which comprises the following steps: calculating a theoretical elastic modulus according to an investigation report, and constructing a sample space by adopting a Latin hypercube sampling method and an orthogonal experiment design method; a proper constitutive model is selected, and corresponding lateral displacement is calculated through finite elements; dimensionality reduction processing is carried out on the data set based on global sensitivity analysis of variance so as to accelerate convergence during subsequent inversion; training an SKRR-LSBOOST model through an ensemble learning method to replace a traditional model; combining an improved Hemma optimization algorithm with a substitution model, and applying the combined model to parameter inversion; and performing forward analysis based on the finite element model defined by the inversion parameters to predict the deformation of the deep foundation pit in the next excavation stage. According to the method, the advantages of numerical simulation and data driving are fully exerted, the calculation cost is reduced according to the rock and soil parameters, higher prediction precision is achieved, and the accuracy of foundation pit deformation prediction is improved.

The invention relates to a surfactant effect evaluation and process optimization method based on data analysis. The method comprises the following steps: carrying out data acquisition and preprocessing on production parameters of the surfactant to obtain a preprocessed data set; constructing four unstructured models based on the preprocessed data set, and calculating the target cleaning efficiency of the surfactant; constructing a physical information composite network based on the target cleaning efficiency and a training set in the preprocessed data set, and training to obtain a performance evaluation model; based on the performance evaluation model, performing multi-granularity information fusion and performance evaluation on a test set in the preprocessed data set to obtain a target performance evaluation result; and according to the target performance evaluation result, performing parameter sensitivity analysis and optimization solution on a surfactant formula and process parameters to obtain a target process optimization scheme. According to the method, the performance evaluation accuracy and the process optimization efficiency of the surfactant are improved.

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 discloses a soil erosion combined forecasting method based on Bayesian weight. The invention relates to the technical field of soil erosion forecasting, and the method comprises the steps: building a watershed multi-source heterogeneous database, and building a soil erosion joint simulation frame based on a MUSLE model, a WEPP model, a LISEM model, an EUROSEM model and a SIMWE model; respectively carrying out parameter sensitivity analysis on each soil erosion model by adopting an FAST algorithm; respectively carrying out parameter calibration and verification on each soil erosion model by adopting a PSO (Particle Swarm Optimization) algorithm; carrying out Bayesian sampling on each soil erosion model by adopting a DREAM algorithm, and carrying out parameter posterior distribution estimation; and based on a Bayesian sampling result, evaluating the uncertainty of the soil erosion model, calculating Bayesian weights of different soil erosion models by adopting a BMA algorithm, and carrying out soil erosion combined forecasting.

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 belongs to the technical field of reliability-based design optimization, and particularly relates to a reliability design optimization method combining transfer learning and a physical information neural network. Comprising the following steps: constructing a physical information neural network model by adopting training data of a source domain, and evaluating the reliability of the source domain. A physical information neural network model constructed by adopting training data of a source domain is updated by utilizing a layer freezing and fine tuning technology in transfer learning, so that the reliability of a target domain with different statistical attributes is evaluated, the calculation overhead is saved, and the calculation efficiency is improved. A first-order scoring function is utilized to carry out random sensitivity analysis on the failure probability so as to ensure normal operation of design optimization. And finally, providing estimation reliability, an objective function value and corresponding sensitivity information for an optimizer to obtain a corresponding updated design point. And repeating the iterative design process until the optimal design is realized. The training speed of the physical information neural network model is increased, and the precision of the physical information neural network model is improved.

The invention provides an estuary coast water quality forecasting method based on a hydrodynamic water quality coupling model. The method comprises the following steps: S1, collecting basic data required for constructing an estuary coast hydrodynamic water quality model; s2, establishing and verifying an estuary coast hydrodynamic water quality coupling model based on an FVCOM-ERSEM model; s3, utilizing the verified model to simulate wave and water flow coupling motion and pollutant space-time distribution in the estuary coast area within a set time; s4, key factors influencing estuary coast hydrodynamic force and pollutant transportation are concluded through monitoring, literature and investigation; s5, sensitivity analysis is carried out, and the spatial-temporal change rule of pollutants in the estuary coast area under different factors is studied; and S6, analyzing the influence of the change of various factors on the pollutant concentration by taking the water quality parameters as indexes. According to the method, influences of engineering construction, pollutant discharge and other factors on water quality parameters are identified through quantitative analysis, and the model fitting degree is improved by adopting a high-precision grid technology.

The invention provides an aeolian vibration analysis method for a power transmission line steel tube tower, and the method comprises the steps: S1, obtaining the operation monitoring data of the power transmission line steel tube tower, and historical wind speed meteorological data; s2, sensitivity analysis is carried out on a plurality of different parameters possibly influencing the amplitude of the tower, and main parameters causing vortex-induced vibration response of the steel tube tower component are determined; s3, simulating a typical steel tube power transmission tower component model by using the refined three-dimensional finite element model, and analyzing the natural vibration frequency, the vibration mode curve, the critical wind speed and the maximum amplitude of the component; and S4, establishing vortex-induced vibration models of a single steel pipe and adjacent steel pipes, realizing a dynamic grid effect in the aeolian vibration process based on a nested grid technology, developing a fluid-solid coupling calculation UDF program in the vortex-induced vibration process, and embedding the fluid-solid coupling calculation UDF program into FLUENT software to reproduce a vortex-induced vibration response process of a complex working condition. According to the method, theoretical analysis and numerical simulation are combined to effectively solve the problem of aeolian vibration response evaluation of the high-voltage transmission line steel tube tower, and the calculation efficiency and precision are high.

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 relates to a molecular simulation method for inhibiting gas and coal dust composite explosion microreaction by a powder explosion suppressant. The molecular simulation method comprises the following steps: constructing an initial model of a gas / solid mixed reaction system; training to obtain a machine learning force field; a machine learning force field optimization initial model is utilized to obtain a balanced and uniform reaction model, and de novo calculation molecular dynamics simulation is carried out under the conditions of an NVT or NPT ensemble, specific temperature and pressure; performing data post-processing and calculation on a simulation result, analyzing a pyrolysis evolution process, product types and structure information through a ChemTraYzer method, constructing a reaction trajectory network, extracting key primitive reactions and free radicals, performing sensitivity analysis through a Chemkin post-processor, and calculating the sensitivity of the product. Weak interaction and electronic structure and potential distribution in the molecular dynamics evolution process are calculated based on DFT and TST theories, and the inhibition mechanism of the selected powder explosion suppressant and the evolution rule of the powder explosion suppressant for inhibiting gas / coal dust burning explosion are disclosed.

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 relates to the technical field of air pollution control, in particular to a risk-oriented industrial park VOCs comprehensive tracing method and system, and the method comprises the steps: setting monitoring distribution points and monitoring elements, and carrying out the monitoring of the monitoring elements through a preset monitoring method; setting risk factors, and evaluating the risk factors according to the monitored monitoring factors; performing risk source quantitative traceability to generate a risk source quantitative traceability result; wherein the risk source quantitative traceability comprises PMF source analysis, receptor and pollution source component spectrum matching, risk source comprehensive contribution evaluation and risk source space matching; performing risk assessment uncertainty and sensitivity analysis on a risk source quantitative traceability result; according to a risk assessment uncertainty and sensitivity analysis result and a risk source quantitative tracing result, adopting IMTs to formulate a comprehensive emission reduction method for VOCs in the industrial park; according to the scheme, VOCs in the industrial park can be precisely traced, the uncertainty is reduced, and the fine control capability of pollutants in the industrial park is 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.