9results about How to "Improve estimation efficiency" patented technology

The application discloses a channel estimation method and device, electronic equipment, storage medium and program, and applies to the field of wireless communication, and the method comprises the steps of adding a window function on the basis of a pilot signal and a received signal of an obtained target channel, constructing a windowed channel estimation model, determining a posterior distribution probability density function of a channel estimation parameter in the windowed channel estimation model, obtaining an auxiliary manifold and a target manifold which are approximate to a nonlinear term in the posterior distribution probability density function, determining an auxiliary manifold parameter, generating a target manifold parameter based on the auxiliary manifold parameter, and generating a channel estimation parameter of the target channel based on the target manifold parameter. The embodiment of the application can reduce the calculation complexity of the channel estimation process, improve the channel estimation efficiency, reduce the signal transmission delay, and enhance the user experience.

The application is suitable for the technical field of image processing, and provides a traffic scene monocular depth estimation method, device, equipment and storage medium. The method performs standardization processing on a vehicle-mounted original traffic image, avoids repeated feature correction calculation of a model due to input image quality defects. Enhanced multi-scale depth features are statically divided, and local space texture modeling and global long-distance dependence modeling are correspondingly performed, two types of feature decoupling parallel learning are realized, traffic scene details and global scene modeling can be completed in a targeted manner, invalid power consumption caused by feature interference is avoided, and feature calculation utilization is greatly improved. Through a learnable gate vector model, an optimal channel division ratio is adaptively generated according to the feature requirement of each encoder level, the high calculation complexity caused by the fixed global attention is greatly reduced, and the traffic scene depth estimation efficiency can be improved under the condition that the platform computing resource is limited.

The present application relates to a kind of FDA-MIMO radar incremental distance-angle joint estimation method, comprising the following steps: using the echo data of FDA-MIMO radar to establish the signal model of incremental distance and angle;Incremental distance information and angle information in signal model are separated, and the decoupling model of FDA-MIMO radar incremental distance and angle is established;Based on decoupling model, define the two-dimensional atom norm of FDA-MIMO radar target incremental distance and angle, the minimum problem of two-dimensional atom norm is converted into the first semi-positive programming problem, and the first semi-positive programming problem is used as parameter estimation function;Based on parameter estimation function, the incremental distance estimation value and angle estimation value of target are calculated.The method can directly estimate the incremental distance estimation value and angle of target in continuous domain, the method does not need to carry out grid division to space domain, can solve grid mismatch problem, effectively improve the precision of parameter estimation result, improve the efficiency of parameter estimation.

The application discloses a global sensitivity calculation method of a power system risk event based on subset simulation, establishes a representation method of a risk event in power system uncertainty quantification considering uncertainty of a random source model, applies subset simulation to decompose a rare event to reduce sampling times, and efficiently calculates global sensitivity of uncertainty of probability distribution parameters of a random source in a power system to a probability of a rare event. The scheme greatly reduces a sample size while guaranteeing accuracy of rare event probability evaluation, effectively solves the problem of low efficiency of a traditional Monte Carlo sampling algorithm in simulating a rare event, and can quickly evaluate the influence degree of uncertainty of probability distribution parameters of a random source in a power system to a probability of a rare event of power grid operation.

This invention belongs to the field of state estimation technology and relates to a lithium battery state of charge estimation method based on adaptive dual-fusion Kalman filtering. The method includes: constructing an equivalent circuit model; obtaining battery parameters based on the equivalent circuit model; obtaining battery measurement values ​​and measurement estimates based on the battery parameters; using a corresponding Kalman filter to estimate the battery state of charge based on the absolute value of the error between the measurement values ​​and the measurement estimates, and obtaining the battery state of charge estimate; and obtaining the battery state of charge estimation result based on the battery state of charge estimate. This invention combines the advantages and disadvantages of both EKF and XKF filters, employing different estimation schemes at different times, which can improve estimation efficiency while ensuring accuracy.

The application provides a kind of rock mass structure plane fast automatic identification method and device based on three-dimensional point cloud, it is related to image recognition, rock mass structure plane identification technical field, comprising: S1, obtains the three-dimensional point cloud model of the engineering rock mass of survey area;S2, obtains the super voxel model by the super voxel segmentation algorithm and the super voxel normal vector estimation RANSAC algorithm to three-dimensional subsampling;S3, obtains the structure plane model by the structure plane identification of super voxel model to the double criterion constraint region growing algorithm;S4, respectively calculates the occurrence of structure plane model structure plane.This application effectively reduces the seed point number that region growing algorithm needs iteration, improves processing efficiency, can accurately identify structure plane, according to the structure plane occurrence analysis generated by this application, can obtain more accurate and more real rock mass condition, solves the problem of traditional measurement means in efficiency, accuracy and safety.

The invention relates to the technical field of AI-based pipe network project cost estimation, and discloses an AI-based pipe network project cost estimation method and system, and the method comprises the steps: firstly analyzing basic project parameters of a to-be-estimated pipe network project, and extracting project constraint conditions based on a preset cost estimation rule file; determining a matched rule application path according to the constraint condition and the rule application sequence; then constructing a corresponding prompt word chain, wherein the prompt word chain comprises a plurality of prompt word units which are split in sequence; sequentially executing the engineering calculation subtasks corresponding to the prompt word units by using a large language model to generate a cost estimation intermediate result; and performing consistency verification including monotonicity verification on the intermediate result, and outputting an estimation result when the verification meets a preset condition. According to the method, the AI technology and the regularization process are combined, the efficiency and precision of pipe network project cost estimation are improved, the problems that a traditional method is low in efficiency and insufficient in precision are solved, and the method is suitable for early-stage cost estimation of various pipe network projects.

This application relates to a method and device for monitoring vascular parameters. The method includes: acquiring finger pressure information and finger arterial physiological information during a monitoring period; obtaining the actual value of the oscillation envelope using the finger arterial physiological information, and obtaining the actual value of the pulse transit time using the finger arterial physiological information; obtaining a first model; obtaining a second model; and simultaneously optimizing the objective function using the first and second models to obtain estimated values ​​of vascular parameters during the monitoring period. The objective function is generated based on a first error and a second error, where the first error is obtained from the actual value of the oscillation envelope and the estimated value of the oscillation envelope generated based on the first model, and the second error is obtained from the actual value of the pulse transit time and the estimated value of the pulse transit time generated based on the second model. This method can improve the accuracy and robustness of vascular parameter estimation.

The application discloses a short-time fractional Fourier transform-based super-large array near-field space non-stationary channel estimation method and system, belongs to the technical field of signal and information transmission, and solves the problem that existing technologies cannot accurately estimate channel parameters and have large pilot overhead when estimating a near-field space non-stationary channel. The application comprises pilot signal receiving and processing; according to a preset near-field coverage distance and an angle range, a range of a frequency modulation slope of an angular frequency is calculated, the range is uniformly sampled to obtain a frequency modulation slope set, and a fractional order rotation angle set is obtained through mapping; a short-time fractional inverse Fourier transform sub-codebook is constructed and integrated and arranged to obtain a total codebook; a sparse set and sparse coefficients of the near-field space non-stationary channel in a short-time fractional Fourier transform domain are iteratively estimated; and a near-field space non-stationary channel vector is reconstructed in combination with the total codebook. The application is suitable for a super-large array near-field space non-stationary channel estimation scene.