74 results about "Optimal estimation" patented technology

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention provides a carbon emission calculation method based on adaptive Kalman filtering. According to the carbon emission calculation method based on adaptive Kalman filtering, a data matrix is generated by acquiring multi-source data, and a unified data basis is provided for subsequent calculation; a dynamic system model and an error measurement matrix are initialized, a state transition model indicates a carbon emission evolution law, an observation model establishes a mapping relation between multi-source data and carbon emission, and the error measurement matrix dynamically indicates a model effect and adaptively adjusts calculation parameters to ensure the adaptability of the model to the dynamic change of the system; a carbon emission prediction parameter and an error measurement matrix are calculated in real time through adaptive Kalman filtering, a prediction value is corrected through an observation parameter, an optimal estimation parameter and an observation residual error are obtained, meanwhile, the error measurement matrix is updated in real time, and dynamic fusion and noise suppression of multi-source data are achieved through the closed-loop process. And the precision and the real-time performance of carbon emission calculation are improved.

The invention discloses an atmosphere and ocean integrated atmosphere temperature and humidity profile inversion method based on multiple sources and multiple frequency bands. The method comprises the following steps: S1, acquiring multi-source collaborative observation data, and preprocessing the acquired data; s2, constructing an integrated state vector and coupling forward model, and obtaining an integrated state vector X; s3, performing optimal estimation based on the preprocessed data and the integrated state vector X, and performing joint inversion to realize integrated inversion solution; and S4, outputting an inversion result and theoretical uncertainty output. According to the method, integrated cooperative inversion is carried out through a physical model and an optimal estimation theory, and the inversion precision of the atmosphere temperature and humidity profile in the clear sky and in the presence of clouds, especially in the air above the ocean, is remarkably improved.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

The invention relates to the technical field of communication, and discloses a measurement and control communication parameter transmission state monitoring system, which comprises a data acquisition and adaptation module which is responsible for acquiring an original parameter data stream by measurement and control equipment, performing unified formatting processing on the original parameter data stream and then sending the original parameter data stream to a state optimal estimation module. A technical scheme of constructing a state space model at an edge end, performing optimal state estimation on original measurement and control parameters in combination with a Kalman filter, and performing statistical distribution deviation quantification on a filtered residual sequence by using KL divergence is adopted, a system high-fidelity operation state is extracted from strong noise background data, and progressive weak abnormity is early warned in advance and accurately. Compared with a scheme of carrying out fixed or simple statistical threshold alarm on an original measurement value, the problems that state misjudgment is caused by measurement noise interference, progressive performance degradation cannot be effectively sensed, fault discovery is lagged and the early warning capability is insufficient are solved.

The invention discloses an engineering measurement error dynamic correction method based on multi-sensor data fusion, and relates to the technical field of engineering measurement. Three error sources of sensor physical characteristics, environmental interference and manual operation are defined as estimable state variables, and differentiation processing is performed according to different characteristics of errors: a dynamic model is established to embed the sensor and environmental errors into a state space, so that real-time compensation based on the model is realized; according to the scheme, a gross error recognition mechanism based on data-driven statistical characteristics is established, real-time diagnosis and processing are carried out on personal errors, joint optimal estimation is carried out on unified state vectors by adopting a recursive estimation algorithm, and high-precision corrected physical quantities and estimated values of all error states are synchronously output. The measurement precision and reliability are obviously improved; by means of robust processing of gross errors, stability of the system in a non-ideal environment and online self-diagnosis and health monitoring of the measurement system are enhanced.

The invention relates to the technical field of data processing, in particular to an electric power project data fusion method and system. The method comprises the following steps: collecting and preprocessing multi-dimensional time sequence data of a distributed power supply and an energy storage system; extracting features of the preprocessed data; an adaptive forgetting factor is introduced to carry out adaptive Kalman filtering processing on the characteristic data, the adaptive forgetting factor combines a current error, an error change trend and a historical error weighted sum difference value, and a prediction covariance matrix and a measurement noise covariance matrix are dynamically adjusted, so that the response capability of filtering to system mutation is improved; and fusing the optimal estimation state of each feature by adopting a D-S evidence theory so as to accurately judge the operation state of the electric power project. According to the method, the problem that estimation is lagged when the state is suddenly changed in traditional Kalman filtering is effectively solved, the accuracy and the reliability of judging the operation state of the electric power project are improved, and an effective guarantee is provided for stable operation of a micro-grid system.

The application relates to a SINS large misalignment angle fast alignment method based on a Lie group and a factor graph, and belongs to the technical field of large misalignment angle alignment, and comprises the following steps: S1, defining system states under a Lie group framework; S2, constructing a factor graph model of the SINS / GNSS large misalignment angle fast alignment; S3, constructing an SINS factor cost function under the Lie group framework; S4, constructing a GNSS factor cost function under the Lie group framework; S5, optimizing the system states under the Lie group framework by using the factor graph model, the SINS factor cost function and the GNSS factor cost function, and obtaining optimal estimation of the system states; and S6, obtaining a theoretical value of a Lie group matrix containing elements related to an attitude, a speed and a position according to the optimal estimation of the system states, that is, completing the SINS large misalignment angle fast alignment method. The application has the advantages of strong robustness, good precision, simple operation, fast convergence, low cost, high alignment precision and good practicability.

The application discloses a molten silicon liquid level estimation method based on a hybrid adaptive resampling particle filter, and comprises the following steps: firstly, processing laser spot images collected by a CCD sensor to obtain observation data of the liquid level; then, establishing a system state equation of the molten silicon liquid level according to a kinematic principle to describe the movement of the liquid level; performing state estimation on a laser centroid vertical coordinate by using a particle filter algorithm, and obtaining a new particle set by using a hybrid adaptive resampling method; after re-normalizing all particle weights, outputting an optimal estimation value; and finally, smoothing the filtered data by using a moving weighted average method, and the smoothed result is the estimation value of the molten silicon liquid level. The application solves the problem that in the prior art, the variance of Gaussian variation is too large, particles jump out of a sampling range, and it is difficult to accurately estimate a real liquid level.

The invention provides an active and passive microwave joint inversion method and system combined with Bayesian probability inversion, and the method comprises the steps: carrying out the sampling in a priori numerical range of a to-be-inverted parameter, obtaining an initial guess value, and carrying out the simulation based on the initial guess value, and obtaining active simulation data. And performing active inversion by combining the active simulation data and the active microwave observation data, and determining an effective roughness parameter required by passive inversion based on the roughness parameter obtained by the active inversion and a pre-constructed relation function. And performing passive inversion by combining the effective roughness parameter, the initial guess value of the soil moisture and the passive microwave observation data. The active inversion result and the passive inversion result respectively comprise an optimal estimation value and an uncertainty quantitative index of the to-be-inverted parameter. And obtaining a joint inversion result by combining the active inversion result and the passive inversion result. According to the scheme, active and passive microwave observation is combined to accurately estimate the soil moisture and quantify the uncertainty in the inversion algorithm, and the soil moisture estimation precision and reliability are improved.

The application discloses a kind of gas insulated switchgear gas chamber pressure leakage degree judging method and system, applied to insulating switchgear gas chamber detection technical field, method includes first obtaining the pressure value of the gas chamber to be evaluated in preset time period, constructs pressure value sequence, and then the sequence is weighted average processing, obtains first final pressure value sequence, again to first final pressure value sequence Pressure mutation identification, locates pressure mutation interval and removes it, obtains second final pressure value sequence.Then, construct pressure state space model, input model to predict second final pressure value sequence, obtain optimal estimation pressure value sequence, calculate pressure decay rate according to optimal estimation pressure value sequence, and the pressure decay rate is segmented linear transformation, finally output gas chamber pressure leakage severity value.Through the above method, the accuracy of pressure leakage evaluation is effectively improved.

The invention discloses a gas well production state estimation method and system based on particle filtering. The method specifically comprises the following steps: S1, collecting production data of different types of gas wells; s2, visual curve drawing is carried out according to the production data of different types of gas wells; and S3, outputting an estimated gas well state through the constructed particle filtering prediction model based on the acquired data of gas well production. Dynamic estimation of gas well parameters is achieved based on particle filtering, the particle filtering serves as a suboptimal estimation framework, and when the number of particles is large enough, the particle filtering can approach the optimal estimation result; meanwhile, compared with Kalman filtering, the particle filtering has the advantages that the particle filtering can also approach optimal estimation for a nonlinear and non-Gaussian process; the gas well state estimation accuracy is improved; meanwhile, the production trend and the occurrence of abnormal conditions are judged through the estimated gas well state, and the management level and safety are improved.

The application discloses a kind of unknown distribution's laser interferometer measurement result's uncertainty evaluation method, is used to the measurement result of non-Gaussian and distribution type unknown for uncertainty evaluation;The steps of this method include: 1 using beta distribution method to express measurement result;2 the parameter of beta distribution is regarded as to be estimated parameter, and state space model is established using Bayesian statistical method;3 determine the prior distribution of beta distribution parameter, and the beta distribution parameter of measurement result is estimated recursively based on particle filtering method;4 according to distribution parameter estimated value, the distribution type of interferometer measurement result and its uncertainty are obtained.The method of the application can represent the multiple distribution types of laser interferometer measurement result by beta distribution, so as to solve the optimal estimation and uncertainty evaluation problem of the measurement result of distribution type unknown.

The invention discloses a multi-rate multi-sensor information fusion method and system and a storage medium, and the method comprises the steps: obtaining an observable subsystem of each local sensor through observability decomposition; designing a local Kalman filter in the observable subsystem, and executing Kalman filtering on the observable subsystem at a sampling moment; obtaining an estimation value of a local sensor and an estimation error covariance matrix to calculate a fusion weight; and obtaining a fusion estimation value according to the fusion weight and the optimal estimation value of the local sensor. For a multi-rate system with a local unobservable sensor, a distributed fusion estimation framework is provided, and the problems of asynchronous data alignment and partial observability are solved. Through observability decomposition, a stable Kalman filter is designed in an observable subspace of each sensor to prevent divergence; realizing heterogeneous sensor estimation synchronization based on a dynamic alignment mechanism of a state transition matrix; covariance cross fusion ensures consistency and robustness.

The application discloses a two-dimensional direction of arrival estimation method based on L-array prior waveform signal source, comprising the following steps: solving the two-dimensional direction of arrival angle of the prior waveform based on a first convex optimization function to solve the underdetermined equation insoluble problem generated when solving the two-dimensional direction of arrival angle according to an estimated angle vector model, and obtaining an initial estimated value of the two-dimensional direction of arrival angle; solving the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function to solve the off-lattice phenomenon generated when executing the above step, and obtaining an optimal estimated value of the two-dimensional direction of arrival angle; determining the complex amplitude estimated value of each group of two-dimensional direction of arrival angle according to the optimal estimated value; and matching the prior waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimated value, and obtaining the optimal estimated value of the two-dimensional direction of arrival angle of each prior waveform. The application has a smaller calculation complexity and can be applied to a scene with a higher real-time requirement.

The invention provides a bidirectional clock synchronization method based on an LQG control strategy, and relates to the technical field of wireless communication. Firstly, a state equation of master and slave clock nodes is constructed, clock skew of the master and slave clock nodes is defined, and a process noise covariance matrix of the master and slave clock nodes is established. And obtaining a corresponding timestamp based on bidirectional communication of the master clock node and the slave clock node. And constructing an observation equation of the master and slave clock nodes based on the timestamps. Based on the observation equation, the process noise covariance matrix and the state equation, state prediction and state updating of the master and slave clock nodes are carried out, and the optimal estimation value of the state of the master and slave clock nodes is obtained. And calculating a control value of an LQR controller based on the optimal estimated values of the states of the master and slave clock nodes, and adjusting the frequency of the slave clock nodes based on the control value to realize synchronization of the master clock nodes and the slave clock nodes. According to the clock synchronization method provided by the invention, the precision and robustness of clock synchronization are improved, and the synchronization requirement of a wireless communication network is met.

The invention discloses a cross-scale numerical simulation and feedback analysis method for a seepage field. The method comprises the following steps: S1, establishing a numerical calculation model for cross-scale analysis of the seepage field; s2, determining and initializing seepage parameters to be inverted, selecting observation points and constructing a target function; s3, carrying out seepage cross-scale numerical calculation by utilizing the numerical calculation model to obtain a simulation result of a seepage field; s4, according to the simulation result of the seepage field and the observation information of the actual seepage field, calculating the value of an objective function and a Jacobian matrix; step S5, updating the seepage parameters to be inverted by using a Levenberg-Marquardt optimization method; and S6, outputting the optimal estimation of the to-be-inverted seepage parameters and a seepage field calculation result when a convergence condition is satisfied. Based on the method, the accuracy of the cross-scale numerical simulation and feedback analysis result of the seepage field is improved.

The application discloses an estimation method of an unknown failure tumbling target angular velocity based on an electromagnetic induction torque, which is composed of the following steps: obtaining historical data of the unknown failure tumbling target, wherein the historical data comprises angular velocity historical data and electromagnetic induction torque historical data; performing dimension increasing on the historical data through nonlinear transformation to obtain dimension-increased data; performing dynamic mode decomposition on the dimension-increased data to obtain a system matrix, an output matrix and a prediction matrix; constructing a linear predictor model of the unknown failure tumbling target in the electromagnetic despinning process according to the system matrix, the output matrix and the prediction matrix; and solving the linear predictor model as a constraint condition of an optimal estimation problem to obtain a real-time accurate estimation value of the angular velocity of the unknown failure tumbling target. In the electromagnetic despinning process of a close-range space target, the application realizes real-time accurate estimation of the target angular velocity by using electromagnetic despinning torque data, thereby solving the problem that the target angular velocity is difficult to measure in a close-range despinning task.

The invention provides a multi-sensor target fusion method and system based on sequential filtering, and belongs to the technical field of intelligent driving, and the method comprises the steps: obtaining a fusion track of a plurality of target sensors, and calculating a track state at a current moment according to a track state at a previous moment; adopting a greedy algorithm to respectively match the plurality of target sensors with the current time track; and updating the track state by adopting sequential filtering according to the matched measurement value. According to the method, the greedy algorithm is adopted for matching, the time complexity is far smaller than that of a KM algorithm and the like, but an approximate global optimal matching result can be achieved in most cases, and the calculation amount can be reduced to a great extent. According to the method, sequential filtering is adopted to update the flight path state, the method serves as a recursive filtering algorithm, measurement data are comprehensively considered, meanwhile, the method is easy to implement, optimal estimation of the system state can be provided based on the minimum mean square error criterion, and in addition, filtering parameters and measurement weight values can be adjusted according to the actual situation.

The invention relates to the technical field of millimeter wave radar signal processing, in particular to a course solving method and system through optimal estimation of the amount of motion among multiple frames and application of the course solving method and system. S2, assuming a uniform motion model; s3, constructing a cost function; and S4, solving the optimal displacement. According to the invention, jitter errors caused by dependence on single-point amplitude in a traditional method can be avoided; the estimation robustness is improved by using the point cloud information of the whole frame; constructing a point cloud distance sum cost function to directly reflect the inter-frame matching degree; the method is easy to implement, high in calculation efficiency and suitable for a real-time system.

The application discloses a roller coaster track measurement error control method fusing zero speed and revisit constraints, obtains angular velocity and acceleration data output by an inertial measurement unit, speed data output by an odometer and wheel pair relative position data output by a plurality of wheel pair displacement sensors, identifies a pseudo zero speed state identifier based on the angular velocity and acceleration data, determines a true zero speed state and triggers zero speed correction to generate a first correction constraint when the speed data is lower than a preset threshold and the pseudo zero speed state identifier is no, inputs each data into a track-vehicle coupled dynamics model, constructs a joint state vector, applies a sparse Gaussian process prior to a track state vector therein, further constructs a hierarchical Bayesian model based on redundant observation, alternately updates a posterior distribution by using variational Bayesian inference, and generates optimized track geometric parameters and vehicle motion states. The application effectively suppresses inertial measurement errors by joint modeling and optimal estimation, and realizes high-precision measurement of track geometry.

The invention relates to an SF6 gas density relay verification signal anti-jitter processing method and system, and the method comprises the steps: firstly collecting a switching value signal of a relay contact in verification in real time, and obtaining an original sequence containing jitter noise; then, a Kalman filtering algorithm is initialized, and meanwhile, a snow ablation optimizer (SAO) is configured; thirdly, constructing a fitness function containing smoothness, real-time performance and accuracy indexes, and taking minimization of the fitness function as a target to drive a snow ablation optimizer to optimize to obtain an optimal covariance matrix; the optimal parameters are injected into Kalman filtering, the state is reset, original signals are input point by point, and anti-jitter signals are output through recursive optimal estimation; and finally, judging and identifying a contact action moment through a threshold value, and recording a corresponding pressure / density value to finish verification. Compared with the prior art, the method has the advantages of being high in anti-jitter adaptability and universality, accurate in verification result, intelligent in verification process and the like.

The invention discloses a multi-target tracking method and system based on tandem joint probability data association and variational Bayesian filtering, electronic equipment and a medium, and the method comprises the following steps: 1, predicting each target, and calculating the interconnection probability between each measurement and the target; step 2, generating equivalent fusion measurement and equivalent covariance for each target; step 3, performing dynamic correction on the fusion measurement noise covariance of each target; 4, updating and iterating each target by using Kalman filtering, and returning to the step 3 if the number of iterations is less than the set number of iterations; otherwise, storing the optimal estimation value and the posterior error covariance of each target. According to the method, the calculation complexity and the resource consumption are remarkably reduced, and meanwhile, the multi-target state estimation precision and the system robustness are synchronously improved.

The invention discloses a target state determination method and device, and relates to the technical field of automatic driving. A specific embodiment of the method comprises the steps of obtaining a detection result of a detector on a first type of motion features of a target at the current moment, wherein the detection result comprises a detection state value at the current moment and a detection score representing detection reliability; adjusting an initial value of the measurement noise data according to the detection score to obtain a current value of the measurement noise data; obtaining a state optimal estimation value and a state covariance optimal estimation value of the second type of motion features of the target at the previous moment, and determining a Kalman coefficient at the current moment according to the state covariance optimal estimation value and the current value of the measurement noise data; and determining the optimal state estimation value of the second type of motion features of the target at the current moment by using the optimal state estimation value, the Kalman coefficient at the current moment and the detection state value at the current moment. According to the embodiment, the calculation accuracy of the Kalman filtering algorithm can be improved.

This invention provides a method and system for measuring atmospheric data using integrated optical sensing, comprising: real-time acquisition of surface pressure distribution and surface recovery temperature using a FADS subsystem, and inversion of Mach number, static pressure, and attack angle parameters; periodic laser emission using a MOADS subsystem, and analysis of atmospheric parameters such as air velocity, attack angle, and density through backscattering spectroscopy; independent Kalman filters running on the FADS and MOADS subsystems, performing local optimal estimations using aerodynamic equations and observation equations; employing a federated Kalman filtering algorithm, dynamically adjusting the weights based on the residual covariance matrix of the two subsystems, and jointly solving the pressure, temperature data, and optical signals based on the adjusted weights, outputting Mach number, attack angle, total / static pressure, total / static temperature, and atmospheric density. The technical solution of this invention addresses the technical problems of high cost and limited environmental adaptability associated with using FADS or MOADS systems alone in existing technologies.

The application discloses a method for identifying thermophysical parameters based on prior distribution regularization, which comprises the following steps: for a heat calibration test of heatproof material, based on the temperature observation data and the identified thermophysical parameters, a covariance matrix of the identified thermophysical parameters is calculated through an information matrix, then the optimal estimation and the covariance matrix of the identified thermophysical parameters are combined to obtain a thermophysical parameter distribution of the heat calibration test, and the thermophysical parameter distribution is taken as a prior distribution; a new batch of heatproof material is tested to obtain a new group of temperature observation data, the prior distribution is combined with the new test data to form a new objective function; the new test data comprises the temperature observation data; and according to the new objective function, the optimal estimation value of the to-be-identified thermophysical parameters is obtained. The application improves the identification precision and accuracy of the thermophysical parameters.

The application relates to a Beidou / Galileo system fusion-based satellite-based precise timing method and system. Observation data, broadcast ephemeris data and correction numbers broadcast by a Beidou three-satellite system and a Galileo satellite system are respectively received by a receiver, two different systems are fused for timing, system clock differences between the two satellite systems are respectively taken as constants, white noises and random walks for construction of an estimation model, a PPP fusion model of the two satellite systems is constructed, each estimation model is brought into the fusion model for calculation, the estimation model corresponding to the best timing precision is taken as an optimal estimation model for correction of the system clock difference, other satellite data is corrected, receiver clock difference parameters are obtained, the parameters are used for clock taming, and satellite-based precise timing is completed. The method has higher timing precision and a wider application range.

Provided are a direction of arrival (DOA) estimation method and system for a sparse array based on Vandermonde decomposition reconstruction, relating to the technical field of array signal processing. The method includes: constructing a covariance matrix completion optimization model based on sparse array signals and uniform linear array signals, and performing Vandermonde decomposition by using characteristics of a uniform linear array; introducing a nuclear norm to optimize a rank function in the model, and updating the covariance matrix completion optimization model; and introducing an auxiliary variable to transform the model into a solvable optimization problem, and solving the problem by an alternating direction multiplier method to obtain an optimal estimation value. The DOA is estimated using a root multiple signal classification algorithm.

The invention provides an aerosol and gas profile inversion method and system based on MAX-DOAS, the method comprises a profile inversion step and a result monitoring step, the profile inversion step is used for double-layer MCMC profile inversion, the first layer uses an O4 inclined column concentration to invert an aerosol profile, and the second layer uses an O < 4 > inclined column concentration to invert a gas profile. The second layer of fixed aerosol information is subjected to gas concentration profile conditional probability inversion so as to obtain a profile inversion result, and the result monitoring step is used for providing quality control indexes, monitoring the inversion progress in real time and diagnosing abnormity so as to ensure the correctness of the result. According to the method, the aerosol resolution is improved to 100 m from 200 m, the trace gas resolution is improved to 50 m, the vertical resolution and the error separation precision are improved, the Gaussian hypothesis limitation of traditional optimal estimation is broken through, global convergence is ensured through self-adaptive adjustment and step length optimization, and high-precision inversion is achieved.

The application discloses a seepage field cross-scale numerical simulation and feedback analysis method, comprising the following steps: S1, establishing a numerical calculation model for seepage field cross-scale analysis; S2, determining to be inverted seepage parameters and initialization, selecting observation points and constructing an objective function; S3, carrying out seepage cross-scale numerical calculation by using the numerical calculation model to obtain a simulation result of the seepage field; S4, calculating the value of the objective function and a Jacobian matrix according to the simulation result of the seepage field and observation information of an actual seepage field; S5, updating the to-be-inverted seepage parameters by using a Levenberg-Marquardt optimization method; and S6, outputting optimal estimation of the to-be-inverted seepage parameters and the seepage field calculation result when a convergence condition is met. Based on the method, the accuracy of the seepage field cross-scale numerical simulation and feedback analysis result is improved.