394 results about "Data assimilation" patented technology

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention relates to the technical field of oil and gas field development, and discloses an ultra-large type true triaxial hydraulic fracturing fracture evolution path prediction method and system.The prediction method comprises the steps that a coupling geomechanical model integrating microscopic, macroscopic and wellbore flow scales is constructed; initializing the model and performing crack initial expansion simulation; collecting construction data in real time, and dynamically optimizing model parameters through a data assimilation algorithm; performing fracture evolution advanced prediction by using the updated model; and generating an optimization decision based on the prediction result and feeding back to the construction site. According to the method, fusion of a multi-scale physical mechanism and real-time dynamic prediction is realized, and accurate prediction and active control can be performed on crack expansion under the ultra-large true triaxial condition.

The invention discloses a coastal sound tomography data assimilation method based on stream function fitting. The method comprises the following steps: arranging a coastal sound tomography system, obtaining underwater sound data containing observation domain hydrological information, carrying out correlation peak extraction, and calculating to obtain a sound ray mutual return transmission time difference; scanning topographic information of an observation domain and temperature and profile data between stations, inputting the topographic information and the temperature and profile data into a sound velocity model for sound ray simulation, determining an actual propagation path of sound rays, and inverting path average flow; constructing a three-dimensional unstructured grid of the observation domain, and determining a projection relation between an actual propagation path of sound rays and the grid; establishing a state transition matrix by taking a sound ray path average flow as an observation vector of extended Kalman filtering and taking a coefficient matrix of a flow function least square fitting ocean numerical pattern calculation result as a state vector; and assimilating and updating by using an extended Kalman filtering algorithm, and obtaining an optimized flow field. According to the invention, the observation precision, the resolution and the spatial range of the coastal flow field can be obviously improved.

The invention discloses a near-shore sea surface temperature fusion method based on deep learning driven variational analysis, and belongs to the technical field of data processing, and the method specifically comprises the steps: inputting historical sea surface temperature data after variational analysis into a deep learning sea surface temperature prediction model, outputting a predicted sea surface temperature in a future specified time period as a predicted background field of current variation analysis; based on a deep learning sea surface temperature prediction model, establishing a relationship between a multi-step sea surface temperature prediction difference value and a prediction error, generating a background error variance during fusion, and fusing a spatial distance function and a short-term time correlation function to construct a background error covariance model during variational analysis; assimilating sea surface temperature observation data collected in real time to the prediction background field, solving an optimal analysis field and adjusting the resolution by combining the background error covariance model and the observation error weight, and outputting a near-shore area sea surface temperature analysis field; according to the invention, a sea surface temperature analysis field with high precision and fine scale characteristics is provided for a near-shore area.

The invention discloses a forest-town junction region fire dynamic early warning method based on multi-source data assimilation, and the method comprises the following steps: inputting collected multi-source observation data into a fire risk prediction model for training, and outputting a fire risk grid map covering the whole forest-town junction region for monitoring a high-risk building group; constructing a fire spreading numerical model covering multi-material, multi-scale and multi-physical mechanism coupling; a fire propagation characteristic quantitative model is constructed, and the building heat influence is evaluated; and constructing a data assimilation and fire dynamic prediction model, and dynamically predicting and outputting fire spreading parameters and a spreading trend chart. According to the method, a forest-building-oriented complex scene is constructed, and propagation behaviors of flames under multiple mechanisms of convection, radiation and conduction are effectively described; through the modes of risk identification, dynamic simulation, data assimilation, visual early warning and the like, the precision, response speed and engineering applicability of forest-town boundary region fire prediction are remarkably improved.

The invention discloses a regional refined sea wave coupling numerical forecasting method based on a non-static mode, and aims to improve forecasting accuracy. The method comprises the following steps: firstly, constructing a regional refined sea wave coupling numerical forecasting system consisting of a data collection subsystem, a preprocessing subsystem, a data assimilation subsystem, a coupling mode calculation subsystem and a post-processing subsystem; the data collection subsystem obtains related data of atmosphere, ocean and sea waves; the preprocessing subsystem generates atmosphere, ocean and sea wave mode grid files and interpolates atmosphere, ocean and sea wave forecast fields to obtain preprocessed data, namely an atmosphere initial field, an atmosphere boundary field, an atmosphere forced field, an ocean initial field, an ocean boundary field and a sea wave boundary field. The data assimilation subsystem assimilates the preprocessed data to obtain assimilated data; and the coupling mode calculation subsystem performs integration on assimilated data to obtain forecast fields of atmosphere, ocean and sea waves. And the post-processing subsystem generates a forecast product according to each forecast field. The method is small in forecasting error and high in forecasting accuracy.

The invention relates to the field of environmental monitoring, and discloses a plateau region carbon emission monitoring management method and system, electronic equipment and a medium, and the method comprises the following steps: constructing an atmospheric boundary layer dynamic model for a plateau low-pressure and strong-turbulence environment, and achieving environmental adaptability modeling by correcting a turbulence diffusion coefficient and localized combustion efficiency; based on unmanned aerial vehicle group dynamic path planning and a ground station collaborative sensing network, flight parameters are adjusted in real time in combination with the concentration gradient, and time-space continuous multi-source fusion monitoring data are generated; a high-resolution carbon emission field is output through embedding a combustion efficiency correction factor and a space correlation graph structure by adopting a data assimilation algorithm with mixed ensemble Kalman filtering and a graph convolutional network; and constructing frozen soil degradation index parameters, and driving a transfer learning early warning model to realize methane flux dynamic prediction and multi-stage early warning triggering. According to the invention, the problems of insufficient carbon emission monitoring precision and lack of frozen soil degradation correlation evaluation in a plateau complex environment are solved.

The invention relates to the technical field of structural health monitoring and geotechnical engineering, and discloses a breakwater foundation settlement direction real-time monitoring and early warning system and method, and the system comprises a data collection module which is used for obtaining the measurement data of at least one sensor disposed on a breakwater and a foundation of the breakwater in real time; the storage module is used for storing a preset physics-based reduced-order model, and the reduced-order model represents the overall deformation rule of the breakwater and the foundation system through a group of core deformation modes; and the processing module is connected with the data acquisition module and the storage module, and the processing module comprises a data assimilation module which is used for fusing the measurement data and the order reduction model in real time. By arranging the deformation field reconstruction module, the problem that only discrete point information can be obtained through traditional monitoring is effectively solved, real-time and high-precision expansion from sparse measurement to a continuous overall deformation field is achieved, and a more visual and reliable basis is provided for comprehensively evaluating the safety state of a structure.

The invention belongs to the crossing field of environmental engineering and hydrology and hydrodynamics, and particularly relates to a traceability method based on coupling hydrodynamics and a pollutant degradation equation, which comprises the following steps: firstly, acquiring and preprocessing multi-source heterogeneous monitoring data, then selecting a one-dimensional Saint-Venant equation or a two-dimensional shallow water equation according to a water body form to solve a hydrodynamic field, and finally, determining the hydrodynamic field. A multi-component convection-diffusion-reaction equation is coupled to simulate pollutant migration and transformation; an LSTM module is introduced to identify suspected pollution events, pollution source parameters are inverted through a two-channel framework of PDE constraint optimization and Bayesian inference, uncertainty is quantified, model parameters are updated online in combination with data assimilation, and finally the uncertainty is quantified and verified. The method considers traceability precision, efficiency and compliance, supports multiple water bodies and multiple data sources, and is suitable for complex water body pollution traceability.

The invention discloses a big data-based meteorological data assimilation method and system, and the method comprises the steps: obtaining historical meteorological data, carrying out the time-space coupling of the historical meteorological data, constructing a self-adaptive assimilation dynamic weight model, and obtaining the data assimilation weight of to-be-assimilated observation data; the method comprises the following steps: assimilating observation data to be assimilated according to a traditional assimilation strategy to obtain a first analysis field, constructing a data depth assimilation model, carrying out data restoration and data assimilation on the observation data to be assimilated to obtain a second analysis field, carrying out weather prediction according to the second analysis field, and calculating assimilation deviation. And adopting a Bayesian network to optimize a meteorological data assimilation process according to the assimilation deviation, and processing meteorological data to be assimilated according to the optimized meteorological data assimilation process to obtain an assimilation analysis field. The method not only can improve the efficiency and accuracy of meteorological data assimilation, but also has good interpretability, and can be directly applied to a meteorological data assimilation system.

The invention discloses a unit area irrigation water consumption estimation method and system based on multi-source data fusion, and relates to the field of irrigation water consumption estimation. Multi-source heterogeneous data such as remote sensing images, meteorological data, soil humidity and crop growth information are fused, and observation and model errors are reduced through data assimilation; estimating crop evapotranspiration under an energy balance principle; estimating daily-scale irrigation water consumption by adopting a water balance method; a crop actual irrigation area identification model based on deep learning is introduced, actual irrigation areas of different crop types are effectively distinguished, and the influence of temporal and spatial changes on irrigation efficiency is dynamically reflected. According to the method, high-precision and rapid estimation of the comprehensive unit area irrigation water consumption of a single crop and multiple crops on different regional scales can be realized, the timeliness and accuracy of irrigation water quota evaluation are remarkably improved, and the method has strong adaptability to climate change, soil difference and crop diversity and is suitable for popularization and application. Powerful technical support is provided for precise irrigation and intelligent water conservancy management.

The invention discloses a typhoon intensity prediction method based on an AI initial field driven NWP area mode and fused with vortex power initialization, a medium and a computer program, belongs to the technical field of artificial intelligence and area numerical simulation, and aims to improve the intensity prediction precision and the structure reduction capability of a fast enhanced typhoon. The method comprises the following steps: firstly, constructing an AI weather model based on deep learning, generating a continuous initial field and a boundary field covering multiple meteorological elements, and driving an area NWP mode; secondly, a vortex power initialization process is introduced before report starting, and the consistency and strength accuracy of a typhoon axial symmetry structure in a simulation initial field are enhanced; then, integral simulation is carried out based on an atmosphere-ocean bidirectional coupling mode, and dynamic optimization of a local structure is realized in combination with high-frequency multi-source observation data assimilation; and finally, an AI-NWP residual feedback mechanism is constructed, and closed-loop correction and model fine tuning of prediction errors are realized. The method is suitable for high-precision prediction of typhoon intensity and has good engineering practicability.

The invention relates to an urban rainfall flood process forecasting method based on data assimilation and multi-mode coupling, and the method comprises the following steps: (1) collecting historical meteorological data, hydrological data, topographic data, soil texture data, vegetation data and urban infrastructure data in a region; performing quality control and format standardization on all the data, and inputting the data to a GIS technology-based visual analysis and decision-making platform; (2) establishing a parameter transfer rule among the WRF, the SHUD and the two-dimensional hydrodynamic model; (3) operating WRF, obtaining regional high-resolution rainfall forecast data, performing EnKF dynamic updating, and inputting the data to the platform; (4) driving the SHUD to obtain regional surface runoff and groundwater prediction data, performing EnKF dynamic updating and inputting the data to the platform; (5) driving the two-dimensional hydrodynamic model to obtain a flood propagation path and spatio-temporal release data of a submerged area, and inputting the data to the platform; and (6) forming risk level assessment by using an automatic threshold identification technology, and inputting the risk level assessment to the platform. The method is high in practicability and can realize accurate forecasting.

The invention discloses a sand and dust vertical flux high-precision inversion method based on a laser radar. The method comprises the following steps: acquiring multi-wavelength back scattering and polarization information by using a 355 nm, 532 nm and 1064 nm three-wavelength polarization laser radar system; constructing a five-dimensional optical feature vector space, and combining a support vector machine classifier to realize automatic identification of dust particles; a variational data assimilation technology is adopted to fuse radar observation and numerical forecasting information to invert a three-dimensional wind field; inverting sand and dust mass concentration vertical distribution based on the corrected particle spectrum distribution model; flux calculation and uncertainty quantization are realized through adaptive weighted fusion and a Monte Carlo method; the method has the characteristics of high temporal-spatial resolution, high precision, strong adaptability and the like, and can be widely applied to the fields of weather forecast, environment monitoring, climate research and the like.

The invention discloses a data assimilation method for infrared hyperspectral data of a clear sky channel. The data assimilation method comprises the following steps: acquiring the infrared hyperspectral data of the clear sky channel; a linear or non-linear relationship to convert variables from a mode space to an observation space using a fast radiation transfer mode; satellite infrared hyperspectral atmospheric vertical detection data channel screening is carried out in combination with a principal component method; a channel which is not influenced by cloud pollution is searched by observing the difference between the brightness temperature observed by the infrared and the brightness temperature simulated by the background field; eliminating the statistical deviation between the radiation value observed by the satellite instrument and the radiation value simulated and calculated according to the background field profile; eliminating observation data with errors exceeding a preset threshold value on the mixed earth surface type; and outputting a data assimilation result of the infrared hyperspectral data of the clear sky channel. Analysis fields of all layers of the global atmosphere are improved, the numerical forecasting level is effectively improved, typhoon forecasting intensity errors are remarkably improved, and path errors before landing are superior to conventional observation data tests.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention provides an atmosphere data assimilation method and device, equipment and a storage medium. Relates to the technical field of computer science and atmospheric science fusion. The method comprises the following steps: adding a cost function, priori estimation and the gradient of the cost function into a gradient-based descent algorithm, adding posteriori estimation, and constructing a four-dimensional variation and ensemble Kalman filtering combined model based on Bayesian priori information; and carrying out data assimilation on atmosphere data based on the four-dimensional variation and ensemble Kalman filtering combined model. According to the method, an artificial intelligence technology is combined, especially deep learning and a Bayesian optimization adaptive algorithm are adopted, a traditional data assimilation algorithm is enhanced and optimized, and the method can be widely applied to prediction of extreme weather events such as high temperature and heat waves. Through assimilation of historical data and real-time observation data, the prediction precision of the future climate change trend is significantly improved, and more powerful support is provided for extreme weather early warning and decision making.

The invention belongs to the technical field of marine environment prediction, and discloses a neural network-based sea wave spectrum intelligent correction method and system. The method comprises the following steps: setting a target sea area and acquiring environmental data; performing numerical calculation on the sea wave spectrum in the target sea area through a sea wave numerical mode; a sea wave spectrum correction model comprising a numerical calculation module, an actual measurement processing module and an intelligent correction module is constructed through a deep learning method; and carrying out precision verification and adaptability evaluation on the constructed sea wave spectrum correction model. According to the model, the problem of errors caused by physical process simplification in the simulation process of a traditional method is solved. Meanwhile, the problem that an existing data assimilation method is limited by observation data is solved. According to the method, the simulation precision of the sea wave mode can be effectively improved, and the method has good sea area adaptability.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

The invention discloses a regional wind power plant group multi-scale meteorological data assimilation and space-time reconstruction method, and aims to solve the problems that existing wind power plant meteorological prediction precision is insufficient, multi-source data fusion is difficult and the like. According to the method, effective assimilation of multi-source heterogeneous meteorological data is realized by constructing a deep convolution variational auto-encoder, space-time reconstruction of a high-resolution three-dimensional wind field is realized in combination with a heterogeneous graph network and a Shenchang differential equation technology, and physical constraint conditions are introduced to ensure physical consistency of output results. Experimental results show that the method can significantly improve the meteorological prediction precision of the wind power plant, and provides reliable meteorological data support for fine operation management of the wind power plant group.

The invention discloses an urban flood prediction method based on a dual-drive urban flood model, and the method comprises the steps: carrying out the early-stage preparation of model construction, constructing an urban flood hydrological and hydrodynamic coupling model frame, constructing a deep learning model frame based on a GBDT algorithm, and carrying out the prediction of the urban flood. Assimilation of predicted values and measured data of a hydrological hydrodynamic model and a deep learning model is realized through a real-time data assimilation technology, then an output result of an urban flood hydrological hydrodynamic coupling model is used as an input feature of the deep learning model, and the input feature and parameters are dynamically adjusted according to the matching degree of a confusion matrix. The TP in the confusion matrix is maximum, the TN in the confusion matrix is minimum, finally, construction of the dual-drive urban flood model is completed, and prediction is conducted through the model. According to the method, the characteristic of high calculation efficiency of the deep learning model is exerted while calculation accuracy is considered, a layered coupling architecture is provided, and the problems that the calculation efficiency of a hydrological hydrodynamic model is low and a traditional deep learning model has a black box effect are solved.

The invention discloses a multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method, and belongs to the field of atmospheric pollution monitoring. According to the method, the accuracy and physical interpretability of emission estimation are improved through adaptive fusion and high-precision complementation of multi-source observation data in combination with collaborative assimilation of a physical model and a deep intelligent model; and by adopting multi-scale feature dynamic fusion and a hierarchical graph neural network, high-resolution and refined emission spatial-temporal distribution modeling is realized. And meanwhile, Bayesian optimization and transfer learning are adopted to realize continuous adaptive optimization and knowledge generalization of model parameters. The method effectively solves the problems of heterogeneity, data missing, model migration, insufficient generalization ability and the like of multi-source data, and can be widely applied to the fields of atmospheric environment management, carbon emission monitoring and the like.

The invention discloses a machine learning correction forecasting method for wind and light elements of a new energy power station in a complex terrain, which relates to the technical field of data processing and comprises the following steps: acquiring a multi-source observation data set of a target area, and performing numerical forecasting assimilation on the multi-source observation data set of the target area; obtaining an output data set of the water-wind-light power station in the target area, and determining a forecasting factor through a statistical method based on the multi-source observation data set and the output data set assimilated by numerical forecasting; establishing and training a joint prediction model according to the prediction factors; and obtaining weather forecast data of the target area in the to-be-predicted time period, and predicting the hydroelectric power generation power, the wind power generation power and the photovoltaic power generation power of the water-wind-light power station in the target area in the to-be-predicted time period through the joint prediction model based on the weather forecast data of the target area in the to-be-predicted time period. According to the method, the initial field is improved through data assimilation, so that the numerical forecasting effect is improved.

The invention discloses a meteorological data assimilation method fusing topographic features, and relates to the technical field of meteorological data assimilation, and the method comprises the steps: combining a meteorological variable-topographic factor correlation map with an atmospheric state numerical field, and obtaining a multivariable initial meteorological field model fusing topographic factors; identifying space-time dynamic deviation distribution characteristics of the multivariable initial meteorological field model by using real-time multi-source environment observation data, and generating a terrain disturbance response matrix; carrying out dynamic correction and space-time error reconstruction based on a terrain disturbance sensitive weight on the terrain disturbance response matrix and the multivariable initial meteorological field model through an assimilation optimization algorithm to obtain a meteorological assimilation result; according to the method, dynamic correction and space-time error reconstruction are carried out on the initial meteorological field through the terrain disturbance sensitive weight, directional perception and intelligent correction of observation deviation are realized, and the response capability and the error control level of the model in a dynamic change scene are effectively enhanced.

The invention provides a dynamic landslide early warning method and device based on a landslide physical model and data assimilation, belongs to the technical field of geological disaster monitoring and early warning, and can dynamically, more accurately and timely carry out landslide early warning on a monitoring area. According to the technical scheme, the data assimilation technology is adopted to correct the landslide physical model used for predicting the landslide safety coefficient and the machine learning model used for predicting the landslide displacement, so that the landslide safety coefficient threshold value and the landslide displacement rate ratio threshold value are dynamically adjusted; and carrying out multi-model fusion on the predicted landslide safety coefficient and landslide displacement of the landslide mass to generate a landslide early warning index of the monitoring area, and carrying out landslide early warning according to the landslide early warning index.

The invention discloses a hydraulic engineering operation and management cooperative control system based on digital twinning, and the system comprises the following steps: a data assimilation module which collects observation station, remote sensing and meteorological data and assimilates the data to obtain an assimilation estimator; the dictionary and lifting module is used for generating a lifting variable sequence according to hydrodynamic force prior; the structure identification and uncertainty module is used for identifying a lifting linear model and recursively predicting uncertainty under the constraints of conservation, monotonicity, dissipation and spectral radius; the terminal security module constructs a positive invariant terminal security set according to a support function; the opportunity constraint module is used for setting a total out-of-limit probability budget and generating an opportunity constraint substitution set; the Koopman rolling optimization module is used for solving the control sequence and executing the first control quantity; and the online updating and correcting and deformation and verification module is used for executing residual error triggering low-rank correction, projection return and support surface parameter online deformation. According to the invention, risk-controllable and stable and efficient collaborative scheduling is realized.

The invention discloses a method and system for predicting methane emission flux based on a data coupling model, and the method comprises the steps: downloading remote sensing, soil, hydrology, observation and meteorological data of a target wetland region, and carrying out the data preprocessing; the TECO model and the Wetland-DNDC model are coupled, and hourly underground water level data output by the TECO model are transmitted to the DNDC model; preprocessing the wetland soil data, and calculating the water content change of the soil profile; operating the coupling model, and simulating methane generation, oxidation and emission; based on actually measured data, assimilating and optimizing key parameters by adopting Bayesian data, quantifying uncertainty through Monte Carlo simulation, and performing methane emission flux prediction data; and finally, carrying out precision verification on a prediction result and an actual measurement result. According to the method, high-precision prediction of the wetland methane emission flux is realized through the coupling model.

The invention discloses a rock fracture prediction method and system based on a resistivity data assimilation algorithm, and the method comprises the steps: building a numerical model for simulating the asymptotic fracture process of a rock mass under the load effect according to the boundary conditions and initial conditions of the rock mass; simulating the stress damage process of the rock mass by using a numerical calculation method to obtain prediction data of stress field and fracture damage field distribution of the rock mass; monitoring the damage process of the rock mass sample in real time by using a high-density electrical method to obtain observation data; rock mass fracture damage distribution is obtained based on observation data; an assimilation algorithm is adopted, information of observation data and information of prediction data are combined, and initial conditions or parameters of the numerical model are corrected; and then corrected rock fracture prediction data is obtained. Through the data assimilation algorithm, different data and model simulation results are fused in the power frame of rock fracture, the model track is automatically adjusted continuously depending on observation, the analysis result with higher precision and more consistency is obtained, and the prediction precision and predictability of the model are improved.

The invention discloses an intelligent early warning method and system for snow-melting flood. The method comprises the following steps: constructing digital twin integrating snow-melting confluence and hydrological hydrodynamic force; collecting multi-source monitoring information, and forming driving data through quality control and spatio-temporal interpolation; driving data are input into digital twinning, and model parameters and states are updated through data assimilation; simulating snow-melting runoff, water level and flow to obtain snow-melting flood probability prediction; the method comprises the following steps of: solving a river reservoir gate pump scheduling scheme by using a multi-objective evolutionary algorithm by taking reservoir outlet flow, gate opening and pump station starting and stopping as decision variables, calculating a water level, flow and a submerging range in digital twinning, and selecting a target scheme according to an evaluation rule to generate a scheduling instruction and graded early warning information; monitoring data is collected during scheduling execution and compared with probability prediction, and when the deviation exceeds a threshold value, data assimilation and scheduling scheme updating are triggered. According to the invention, the snow-melting flood forecasting precision and flood control scheduling collaboration are improved.