4 results about "Error coefficient" patented technology

This invention discloses a method for improving the accuracy of inertial guidance based on polynomial recursive least squares, comprising: constructing a guidance tool error model based on the difference between inertial guidance telemetry observations and a flight environment function, wherein the guidance tool error model satisfies a linear relationship; constructing a recursive formula based on polynomial recursive least squares based on the guidance tool error model; determining the estimated value of the guidance tool error coefficient at the current recursive time based on the difference between guidance telemetry observations at the current recursive time and the recursive formula of least squares, and using the estimated value of the guidance tool error coefficient to compensate for the guidance telemetry observations at the current recursive time. This invention can significantly improve estimation accuracy and efficiency, and has a wider range of applications and higher engineering value.

This invention discloses a method for processing error data of smart energy meters based on edge computing, belonging to the technical field of error data processing methods. The invention includes: S1, calculating the mean and standard deviation of the total energy increment sequence within a sliding window, defining a first-order autoregressive hysteresis correction term, setting an adaptive threshold, defining the state machine system state, and finding the computer increment sequence during inactive periods by constructing an inactive period criterion; S2, storing the filtered effective data increments, calculating the energy increment of each sub-meter, defining a continuous zero increment detection function, collecting effective rearranged data points by setting the meter failure detection window and parameters, and constructing an observation matrix X and an observation vector Y; S3, using an improved genetic optimization algorithm framework, calculating eigenvalues ​​and condition numbers, and iteratively obtaining optimal and distinct individuals; S4, determining the optimal regularization parameters using the L-curve method, and calculating the error coefficients of each energy meter using improved Tikhonov regularization.

A self-calibration method for frame non-orthogonality error and accelerometer error in a dual-axis frame inertial system is proposed. Under static base conditions, the platform is locked at 10 frame angular positions, accelerometer measurements are collected, and the specific force measurement of the inertial system in the platform coordinate system at each locked position is calculated. The attitude matrix of the inertial system from the platform coordinate system to the local horizontal coordinate system at each locked position is also calculated, yielding the acceleration of the inertial system in the local horizontal coordinate system at each locked position, which is used as the error observation. The residuals of the error coefficients are calculated using a total least squares algorithm, and the estimated values ​​of the measured parameters are corrected. Iterative calculations are performed until the iterative convergence criterion is met, obtaining the self-calibration results for each error coefficient. Through the self-calibration and compensation of the error coefficients, the accuracy of attitude measurement of the dual-axis frame inertial system carrier or base affected by the non-orthogonality error of the axis system can be improved.

This invention relates to the field of electric drive system design and development for new energy vehicles, specifically to a method for testing and analyzing the dynamic stiffness of electric drive assembly mounts and the accuracy of simulation. The method involves standardized frequency response testing of the electric drive assembly mounts, identification of minimum dynamic stiffness, and the construction of a dynamic simulation model consistent with the test boundaries to extract the minimum dynamic stiffness on the simulation side. Then, it sequentially performs frequency and amplitude error analysis of the minimum dynamic stiffness points in the tests and simulations, as well as average dynamic stiffness error analysis. Finally, it comprehensively calculates accuracy performance coefficients based on multi-dimensional error coefficients and establishes a scoring system to achieve a quantitative evaluation of the test and simulation accuracy. This method solves the technical problems in existing technologies where evaluating the dynamic stiffness of electric drive assembly mounts requires multiple physical impact tests, which are resource-intensive, time-consuming, and lack a systematic and quantitative analysis method for testing and quantifying the accuracy of mount dynamic stiffness, thus failing to utilize the preliminary analysis results to guide subsequent mount design and development.