23results about How to "Improve estimation efficiency" patented technology

According to the road adhesion coefficient determination method, the electronic equipment and the vehicle provided by the invention, when the road adhesion coefficient is calculated, the curve correlation characteristics between the slip rate and the adhesion rate are fully utilized, and the corresponding linear road adhesion coefficient is determined through estimation in advance in a small slip rate range; and in a large slip rate range, a nonlinear road adhesion coefficient with higher precision is determined through real-time calculation values in different operation states, so that the efficiency of safety control is improved, and the driving safety is improved.

The invention discloses a Bayesian estimation method for longitudinal multistage and multilevel item reaction model parameters, and belongs to the technical field of model parameter estimation. The method comprises the following steps: S1, establishing longitudinal multi-level and multi-level project reaction models, including a level 1-level reaction model, a level 2 longitudinal individual growth model and a level 3 multi-level model; s2, based on an improved Bayesian parameter estimation method, estimating all parameters of the project reaction model proposed in the step S1 by introducing auxiliary variables; and S3, verifying the effect of the improved Bayesian parameter estimation method, and comparing the improved Bayesian parameter estimation method with the traditional Bayesian parameter estimation method. According to the Bayesian estimation method for the longitudinal multi-level and multi-level item reaction model parameters, on the basis that estimation precision is guaranteed, the operation time of the QMCMC is better than that of a traditional MCMC, and the operation speed and the estimation efficiency of a longitudinal multi-level and multi-level scoring item reaction theoretical model are improved.

The invention provides a method and device for quickly estimating the residual capacity of a decommissioned battery, electronic equipment and a medium, and belongs to the field of battery capacity tests.The method comprises the steps that a sample impedance characteristic spectrum of a sample decommissioned battery is obtained, and parameter identification circuit fitting is carried out to obtain circuit element parameters; performing feature screening on the circuit element parameters to obtain a first feature subset, performing feature screening on the sample impedance feature spectrum to obtain a second feature subset, and combining the first feature subset and the second feature subset to obtain a comprehensive feature parameter set; and performing iterative optimization according to the comprehensive characteristic parameter set to obtain a battery residual capacity prediction model, and performing rapid capacity estimation on the to-be-detected retired battery according to the battery residual capacity prediction model to obtain a residual capacity evaluation result. According to the method, the battery residual capacity is estimated through the impedance characteristic spectrum of the decommissioned battery, the measurement speed is high, the battery capacity estimation efficiency is effectively improved, meanwhile, only a certain degree of charging is needed, and resource waste in the discharging process is avoided.

The invention discloses a distributed scatterer InSAR phase enhancement method based on a Laplacian operator, and the method comprises the steps: constructing an SAR amplitude information set, and recognizing a homogeneous pixel set based on the SAR amplitude information set; identifying an approximate deformation range based on a Laplacian operator, and adaptively adjusting the window size of a deformation area SHP based on the identified approximate deformation range; performing non-local similarity fusion on the SHP in the adaptive window, and constructing a sample complex coherence matrix model; and in combination with the sample complex coherence matrix model, performing stack compression phase enhancement calibration processing on the time sequence SAR image set to obtain a calibrated enhanced phase. According to the method, the problem that noise suppression and fringe protection are difficult to take into account in a deformation interferogram after DSInSAR phase enhancement in a complex environment can be effectively improved, and the phase enhancement quality is improved.

The invention relates to a battery SOH estimation method and device, electronic equipment and a storage medium, and belongs to the technical field of battery management.The method comprises the steps that an SOH estimated value and an SOC estimated value of a target battery at the last moment are obtained, and the current and the temperature of the target battery at the current moment are obtained; based on the SOC estimated value of the target battery at the previous moment and the current and temperature of the target battery at the current moment, determining the cycle aging rate of the target battery at the current moment; determining the calendar aging rate of the target battery at the current moment based on the SOH estimated value and the SOC estimated value of the target battery at the previous moment and the temperature of the target battery at the current moment; and determining the SOH estimated value of the target battery at the current moment based on the SOH estimated value of the target battery at the previous moment and the circulating aging rate and the calendar aging rate of the target battery at the current moment. According to the method, the accuracy of an estimation result is improved while the SOH estimation efficiency of the battery is ensured.

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 invention discloses a resource estimation method, an electronic device, a storage medium and a program product, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining initial resource estimation information, current resource consumption information, resource demand information of a target project, and end date information of the target project; inputting the initial resource estimation information, the current resource consumption information, the resource demand information of the target project and the end date information of the target project into a pre-trained resource estimation model to obtain a resource estimation strategy output by the resource estimation model; and displaying resource estimation information in each preset planning period indicated by the resource estimation strategy in a preset display page. According to the resource pre-estimation method and device, the resources needed by the target project are planned through the pre-trained resource pre-estimation model, the situation that the requirement for the working experience of workers is high when the resources are planned through the workers is avoided, and the efficiency, practicability and reliability of resource pre-estimation can be improved by performing resource pre-estimation through the model.

The invention discloses a method for estimating the packaging size of a system-in-package circuit, and the method comprises the following steps: calculating the layout size of a bare chip: determining the layout size of the total bare chip based on the edge distance control requirements according to the size and the welding type of each bare chip; adjusting an effective packaging area: obtaining the effective packaging area according to the expected packaging appearance and the packaging distribution forbidding area, judging the reasonability of the effective packaging area, and adjusting the size of the expected packaging appearance to obtain an available effective packaging area; carrying out device layout: placing bare chips and resistance-capacitance devices according to a sequence from large to small and a residual maximum rectangular area method; and packaging boundary dimension determination: according to the ratio of the residual area to the available effective packaging area, evaluating and adjusting an expected packaging boundary dimension, and obtaining a packaging boundary dimension estimated value of the system-level packaging circuit. The method for estimating the packaging size of the system-in-package circuit is high in accuracy.

The invention discloses an IRS-assisted active node deployment and channel estimation method for a communication-inductance integrated system, and belongs to the technical field of wireless communication. The method comprises the following steps: establishing a channel model based on the antenna array response and channel characteristics of the IRS-assisted communication and inductance integrated system; an active node is arranged on the surface of the IRS in the IRS-assisted communication-inductance integrated system to collect and observe a channel value of a channel model, and an IRS receiving signal model after a base station sends a pilot frequency to the IRS is established based on the channel value; establishing a channel graph topological structure matrix; the channel graph topological structure matrix is subjected to graph segmentation based on spectral clustering clustering, the most smooth node of each cluster is selected for sampling, and deployment of active nodes is carried out; and introducing graph smoothing constraint according to the local smoothing characteristic of the channel graph topological structure matrix, obtaining a linear objective function based on atomic norm minimization, solving to obtain a channel estimation result, and completing channel estimation. According to the method, the accuracy and the stability of channel estimation in different scenes can be ensured.

The application relates to a terahertz radar-based focusing imaging method, device, equipment and medium. The method comprises the following steps: obtaining echo signals of a uniformly rotating large-size target based on a terahertz radar, performing distance-direction pulse compression and azimuth-direction FFT on the echo signals to obtain a two-dimensional RD imaging result, estimating the rotation speed and longitudinal distance center offset of the target according to the spatial distribution law of the PSF inclination of each defocused point in the two-dimensional RD imaging result, constructing a final phase compensation function according to the finally estimated rotation speed and longitudinal distance center offset, performing second-order spatially-varying phase compensation and azimuth-direction FFT on a one-dimensional distance image after Keystone transformation according to the final phase compensation function, and obtaining a finally focused RD image. The method can reduce the calculation search amount of terahertz radar RD focusing imaging and improve the focusing accuracy.

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 invention relates to a motion state estimation method for a spatial non-cooperative target, and relates to the technical field of aerospace, and the method comprises the steps: inputting a current state estimation value into a disturbing force prediction model, and obtaining a current disturbing force prediction value of the spatial non-cooperative target at a current moment; determining an average observation value of the spatial non-cooperative target at a next moment corresponding to the current moment according to the current state estimation value and the current covariance estimation value, and determining a prediction state estimation value and a prediction covariance estimation value of the spatial non-cooperative target at the next moment according to the current interference force prediction value; and determining a target state estimation value of the spatial non-cooperative target at the next moment based on the average observation value, the prediction state estimation value and the prediction covariance estimation value, and estimating the motion state of the spatial non-cooperative target at the next moment based on the target state estimation value. According to the invention, the accuracy of the estimated motion state is improved.

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.

The invention provides a channel estimation method and device, electronic equipment and a storage medium, and the method comprises the steps: determining target terminal equipment which needs to be subjected to channel estimation processing at present; splitting the channel estimation task according to the number of the target terminal devices, the number of receiving antennas of the network device, a first threshold value and a second threshold value to obtain a plurality of channel estimation sub-tasks; performing parallel processing on the plurality of channel estimation sub-tasks to obtain a first channel estimation value of the to-be-filtered time slot of the target terminal equipment; wherein the first threshold value refers to the maximum number of the terminal devices processed by each channel estimation subtask, and the second threshold value refers to the maximum number of the receiving antennas processed by each channel estimation subtask. Therefore, the channel estimation task is split into the plurality of channel estimation sub-tasks according to the granularity of the terminal equipment and the granularity of the antenna, and the plurality of channel estimation sub-tasks are processed in parallel, so that the processing time delay of the channel estimation task can be reduced, and the channel estimation efficiency can be improved.

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 invention relates to the technical field of rock mass structure prediction methods, in particular to a rock mass fracture parameter estimation method and system. The method includes: acquiring a rock fracture image; preprocessing the rock mass fracture image to obtain a preprocessed image; the preprocessed image is analyzed and calculated to obtain a dominant fracture density range and a fracture density range in any direction, and Monte Carlo simulation is adopted to analyze the dominant fracture density range and the fracture density range in any direction so as to estimate the density of rock mass fractures; normal distribution and logarithmic normal distribution fitting are carried out on the preprocessed image, and the inclination angle of the rock mass fracture is estimated based on the projection angle; and counting the fracture length of each group of fractures in the preprocessed image, and estimating the length of the rock mass fractures in the group based on the fractures with the fracture length greater than a set threshold value. The rock mass fracture image is analyzed based on the geometrical characteristics and the distribution rule of the fractures, and the density, the inclination angle and the length of the rock mass fractures can be accurately estimated.

Embodiments of the present application provide a motion estimation method and apparatus in an encoding process, a device, a storage medium, and a program product. The method comprises: acquiring a video frame to be encoded, and determining a target encoding block to be encoded in said video frame; acquiring reference motion information of an auxiliary encoding block of the target encoding block; determining a target reference image search range from M reference image lists for the target encoding block according to image search range information indicated by the reference motion information, the reference motion information of the auxiliary encoding block comprising: motion information of N adjacent encoding blocks adj acent to the target encoding block and motion information comprised in a reference encoding block list, and M being an integer greater than 1; and performing motion estimation on the target encoding block according to the target reference image search range.

The present invention discloses a method for rapidly estimating frequency deviation in a large range, the method comprising: a transmitting end sends a signal x(λ) at time T0, a receiving end obtains a received signal y(λ) at time T1, and obtains: #imgabs0# through a processor; obtains: #imgabs1# through a processor; establishes a new function: #imgabs2#; outputs #imgabs3#; uses a real-valued signal to obtain #imgabs4#; substitutes #imgabs5# and #imgabs6# into a cross-correlation function through a processor to obtain a frequency deviation #imgabs7#. The present invention is based on an FFT model, screens out constants, splits Y(n), establishes a model of a frequency-free signal and a frequency-deviation signal, so that the model meets the solution requirements of the cross-correlation function, and realizes rapid measurement of the frequency deviation. The present invention can be widely used in existing wireless uplink and downlink communication systems to solve the problem of time / frequency asynchrony when communicating between terminals.

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.