1138 results about "Least squares" patented technology

The invention relates to the technical field of numerical control programming, in particular to a five-axis machining path planning method and system based on data driving, and the method comprises the following steps: obtaining real-time coordinates of each axis of a machine tool, calculating linear velocity and angular velocity components to construct a Jacobian matrix, executing singular value decomposition, and calculating a conditional number ratio by using maximum and minimum singular values; and inputting a nonlinear mapping function to calculate a dynamic penalty factor, generating a rotating shaft weighted item in combination with a rotating shaft identifier, constructing a weighted damping least square objective function, calculating a five-axis motion increment, and accumulating the five-axis motion increment with a real-time coordinate to generate a target absolute position coordinate. According to the method, the pose singularity degree is quantified by monitoring the machine tool pose condition number ratio and converted into the dynamic penalty factor to apply the self-adaptive constraint to the rotating shaft, the severe sudden change of the rotating shaft in the singularity area is inhibited, the tool nose track following error is minimized, and meanwhile smooth distribution of the motion increment is achieved; and the dynamic stability and the surface quality of five-axis linkage machining are improved.

The invention belongs to the technical field of geophysics. According to the truncation model space three-dimensional magnetotelluric inversion method and system, a three-dimensional inversion objective function is constructed according to an inversion initial model, the inversion objective function is optimized by adopting a Gaussian-Newton method, and a Gaussian-Newton increment equation is obtained, a sensitivity matrix in the Gaussian-Newton increment equation is a sparse matrix which is obtained after truncation is carried out by adopting a distance truncation threshold value and a sensitivity amplitude truncation threshold value, the Gaussian-Newton increment equation is converted into an unconstrained least square form, and an inversion updating direction is determined according to the least square form; and performing iterative optimization according to the initial inversion model, the inversion updating direction and the model updating step length to obtain an updated inversion model. According to the invention, units with small contribution to inversion updating are effectively cut, and the model space corresponding to each piece of data is reduced.

The invention provides a front and rear axle load estimation method and system and a steer-by-wire vehicle, and the method comprises the steps: (1) when the vehicle is in a longitudinal stable and straight driving state, updating the mass of the whole vehicle; (2) establishing a least square recursion equation with a forgetting factor based on the dynamic motion equation of the vehicle on the ramp, and calculating the gradient of the current ramp; and (3) calculating a front axle load and a rear axle load based on the whole vehicle mass and the current gradient. The whole vehicle mass estimation is started under the working conditions of longitudinal stability and straight driving, so that the reliability and the stability of the whole vehicle mass calculation process are ensured; besides, vehicle dynamics and kinematics models are fused, recursive calculation is performed by applying a least square method with a forgetting factor, the road gradient can be estimated in real time at high frequency and high precision under various driving working conditions of the vehicle, and the limitation of a traditional fixed parameter model is broken through.

The invention discloses a fractal mask bilinear interpolation-based checkerboard angular point positioning method, which comprises the following steps of S1, acquiring a checkerboard image by using image acquisition equipment, and extracting candidate angular points in the checkerboard image; s2, determining a direction angle of the edge of the local area by using an edge detection algorithm and a polar coordinate transformation and clustering algorithm; s3, based on the direction angle of the edge of the local area, using a gray integral method and a least square method to preliminarily position the coordinates of candidate angular points; s4, in the neighborhood of the preliminarily positioned candidate angular point coordinates, obtaining a sub-pixel-level gray value by using fractal mask bilinear interpolation, and calculating local gradient distribution; s5, dynamically generating a weight mask based on a fractal theory, constructing a central point minimum error function according to a corner local gradient consistency principle, iteratively optimizing candidate corner coordinates, and outputting a sub-pixel level position; according to the method, the sub-pixel-level accurate positioning of the checkerboard angular points is realized by fusing the edge detection, the gray integration and the iterative optimization of the fractal theory.

The invention relates to the technical field of ship navigation and data safety, solves the technical problem that an existing ship data safety guarantee system has defects in the aspects of data trust evaluation and data correction, and particularly relates to a safety guarantee method for credible interaction of inland ship shore-based driving control data under a zero-trust architecture. Comprising the following steps: standardizing operation data to obtain ship sensing data and shore-based data with the same dimension; based on an evidence theory, taking the ship sensing data and the shore-based data as independent evidence sources to perform fusion processing so as to obtain a trust evaluation result for quantitatively representing data credibility; and performing consistency correction on the ship sensing data and the shore-based data based on a least square method. According to the method, scientific and rigorous trust evaluation is realized, mathematical optimality of data fusion is guaranteed, and long-term reliability is ensured. Meanwhile, the credibility and the weight are directly associated with the equipment precision and historical data, so that an operator can flexibly adjust the system to adapt to different equipment.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

The invention discloses a PMSM speed regulation method (ILADRC) based on improved RLS identification and dynamic inverse control. According to the method, a layered framework is adopted; a linear expansion observer with fixed parameters is used for a bottom layer to ensure observation stability; the middle layer constructs a parameter identification regression model based on a generalized disturbance observation value by using an improved recursive least square algorithm, introduces an online correction mechanism, and performs online decoupling identification on input gain and pure physical disturbance of a speed ring by using the generalized disturbance observation value and a current instruction; and the upper layer reconstructs an adaptive dynamic inverse control law based on an identification result, weakens an algebraic coupling loop of observation and control, realizes no-static-error and no-overshoot rapid tracking of the system on a rotating speed instruction under a parameter mismatch working condition in combination with an acceleration error integral enhancement item, and effectively improves the dynamic performance and robustness of the motor under a parameter perturbation working condition.

The invention provides a gear peeling time-varying meshing stiffness prediction method and system based on a back propagation neural network, and the method comprises the steps: considering a tooth surface peeling fault based on a gear tooth bearing contact analysis method, and constructing a helical gear pair time-varying meshing stiffness calculation model; a real irregular tooth surface peeling area is fitted by adopting a least square ellipse fitting method to obtain an ellipse appearance representation, any peeling position is completely described through six key geometric parameters, the geometric parameters of the ellipse appearance are systematically traversed and fitted, and diversified peeling appearance samples are generated. Introducing a tooth profile deviation matrix corresponding to the peeling morphology sample into a tooth surface bearing contact analysis model, and constructing a training data set; and constructing a back propagation neural network model of multiple hidden layers, and performing end-to-end training by using the training data set, so that the back propagation neural network model learns a nonlinear mapping relationship from geometric parameters to a time-varying meshing stiffness curve, thereby predicting the time-varying meshing stiffness under any peeling morphology.

The invention discloses a complex wave-based permanent magnet synchronous motor all-electrical parameter online identification method and device. The method comprises the following steps: analyzing the influence of a voltage error caused by dead zone time on parameter identification, and performing corresponding dead zone compensation; a bias complex wave with a certain amplitude and frequency is injected into the d-axis current, so that the d-axis current meets a parameter identification continuous excitation condition; constructing three groups of effective current slopes through four groups of different input data stored in the steady state of the motor, and identifying three groups of parameters of resistance, direct-axis inductance and quadrature-axis inductance by referring to a d-axis voltage equation and adopting a least square method with a forgetting factor; and finally, updating the identified three groups of parameters as known quantities in a q-axis voltage equation in real time, storing two groups of different input data to construct a group of effective current slopes, and identifying flux linkage parameters by adopting a least square method with a forgetting factor. According to the method, the under-rank problem of all-electrical parameter identification of the motor is solved, the identification precision is high, and the identification error of each parameter is about 3%.

The invention relates to the technical field of motion control, in particular to a motion trail real-time optimization analysis method and system based on multi-modal perception, and the method comprises the following steps: a multi-sensor array obtains position and speed visual features, carries out the coordinate transformation, extracts a direction amplitude, and builds a multi-modal perception data set; the method comprises the following steps: inputting Kalman filtering prediction trajectory comparison deviation to generate a trajectory deviation vector field, calculating a deviation gradient, screening an over-threshold point marking time sequence, generating a dynamic weight correction parameter through weighted least square distribution normalization, and outputting a real-time optimization trajectory scheme through weighted correction fusion coordinates. The position information and the velocity vector of a moving target are obtained in real time by fusing multi-modal sensing data, a state estimation model is combined to predict a trajectory, a trajectory deviation vector field is generated, and a deviation point is subjected to weighted correction, so that the trajectory optimization precision is effectively improved, the weight is dynamically allocated to optimize the priority, and the trajectory optimization precision and the system stability are improved. And the self-adaption and robustness in a complex environment are enhanced.

The invention provides a two-line element generation method and orbit prediction method adaptive to an analytic propagation model, and the method comprises the steps: obtaining the historical osculating orbit elements at each epoch moment, and carrying out the orbit element optimization step of a plurality of iteration rounds on each historical osculating orbit element through employing the analytic propagation model, obtaining a target osculating orbital element number at each epoch moment, enabling the historical osculating orbital element number to be the same as the target osculating orbital element number, and performing inversion to obtain an average orbital element number; and extracting each average semi-major axis of each average orbital element, fitting each average semi-major axis by adopting a least square method to obtain a semi-major axis attenuation rate, inverting the semi-major axis attenuation rate to obtain an atmospheric resistance parameter, and equivalently converting the atmospheric resistance parameter into a standard resistance term in two rows of elements to obtain the two rows of elements. According to the method, the problems of inconsistent models, insensitive residual errors, unable physical explanation of parameters and the like in the two-line element generation process can be solved, and the ephemeris extrapolation orbit prediction precision is improved.

Auto-tuning covariances associated with a set of noise models for a variety of sensor modalities and / or perception components such that the covariances are leveled respective to one another may include whitening the covariances and / or error models and determining scalars to apply to the covariances. Determining these scalars may comprise using the residuals that result from generating the set of noise model (e.g., such as may be determined as part of least squares estimation) along with the hat matrix of the process model to determine the scalars. The covariances may iteratively be updated until the scalar adjustments converge or until another end condition is met.

The invention relates to an all-angle hemispherical gyroscope delay and damping asymmetric error joint compensation method, which belongs to the technical field of inertial navigation, and comprises the following steps: S1, obtaining a self-precession excitation signal and an angular velocity measurement value signal output by a gyroscope; s2, preliminarily estimating the time delay of the gyroscope by adopting a maximum likelihood weighted cross-correlation time delay estimation method; s3, estimating the damping asymmetry error of the gyroscope through a recursive least square method by utilizing the estimated time delay; s4, re-estimating the time delay of the gyroscope by using the estimated damping asymmetric error to obtain the updated estimated time delay; judging whether the updated estimated delay is converged or not; if not, returning to the step S3, and re-estimating the damping asymmetric error of the gyroscope through the recursive least square method by utilizing the updated estimated delay; if the time delay is converged, the finally estimated time delay is obtained; and S5, compensating the finally estimated delay and damping asymmetric error into a control system of the gyroscope. According to the invention, the measurement precision and long-term stability of the gyroscope are improved.

The invention relates to the technical field of industrial measurement, and discloses a cylinder inner wall detection method and system based on a standard jig. The method comprises the following steps: acquiring scanning point cloud data of a standard circular tube jig; a radial deviation sequence is calculated based on the known inner diameter, cosine function fitting is carried out, and an eccentric parameter is calculated to carry out XY-axis iterative correction; calculating a laser inclination angle based on the corrected jig point cloud; obtaining the mixed point cloud, and segmenting the mixed point cloud into a jig point cloud set and a workpiece point cloud set based on the radial distance; calculating a mapping coefficient and performing coordinate conversion on the workpiece point cloud; and performing circular ring fitting by using a least square algorithm to output an inner diameter detection result. The problem of an inner wall imaging blind area is solved by obliquely installing the line laser sensor, and relative measurement is achieved through the standard jig to eliminate system errors.

The invention discloses a track noise control method and system based on active acoustic control, and the method comprises the steps: collecting a track noise signal through a sensor array, and processing the signal through Fourier transform to obtain a spectrum distribution characteristic; judging the type of a noise source according to the spectral distribution characteristics, if the spectral distribution shows high-frequency dominance, determining the noise as wheel-rail contact noise, and if the spectral distribution shows low-frequency dominance, determining the noise as rail vibration noise, and obtaining a noise source contribution proportion; according to the noise source contribution proportion, a dynamically adjusted control signal is obtained; acquiring noise source coupling information which changes in real time from the dynamically adjusted control signal, and determining a fused noise source model; according to the fused noise source model, a least square method is adopted to optimize a control strategy, and an optimized control instruction sequence is obtained; and according to the optimized control instruction sequence, acquiring matching data of the current operation density and speed, and if the effect does not reach the standard, iteratively updating the model to obtain final control output.

The invention provides a lithium battery parameter online identification method based on dynamic weight particle swarm optimization, and relates to the field of power lithium battery model parameter identification. According to the method, a lithium battery equivalent circuit model based on a second-order RC network is established, and a state-space equation is derived according to the Kirchhoff's current law and the voltage law; a discrete output model is obtained through Laplace transformation and discretization processing; constructing a recursive least square parameter updater with a forgetting factor, and jointly optimizing an initial parameter vector of the forgetting factor recursive least square and the forgetting factor by adopting a dynamic weight particle swarm optimization algorithm; and based on the optimized initial parameter vector and the forgetting factor, performing recursive least square updating with the forgetting factor, performing online identification on model parameters, and reversely deducing an equivalent parameter value in an original circuit. According to the method, the parameter identification precision can be remarkably improved, and the problem that data saturation parameter convergence is slow in a traditional recursive least square method is solved.

The invention provides a distributed radar robust target positioning method based on outlier sparsity perception, and the method comprises the steps: carrying out the sparse modeling of outlier noise, enabling the robust target positioning to be expressed as a nonlinear least square problem of potential constraint, precisely representing the potential constraint through employing the convex function difference, and achieving the robust target positioning. A potential constraint nonlinear least square problem is equivalently remodeled into a function difference penalty nonlinear least square problem easy to process, finally, a PBCD-based efficient solving algorithm is proposed to solve the problem, and in the solving process, two sub-problems are solved through two algorithms based on main optimization-minimization and convex function difference respectively. Therefore, the accurate target position and the outlier noise are obtained. According to the method, through sparse modeling of the outlier noise and the PBCD algorithm, the outlier influence is effectively suppressed, the positioning precision is kept stable along with the increase of the number of the outliers or the increase of the upper bound of the amplitude of the outliers, the calculation complexity is low, and the positioning accuracy is high.

The invention relates to the technical field of tunnel safety monitoring, in particular to a strong noise tunnel point cloud monitoring method based on improved RANSAC and dynamic residual, which comprises the following steps: firstly, collecting two-dimensional point cloud data of a cross section of a tunnel, and performing attitude correction through ground baseline fitting to unify a coordinate system; afterwards, de-noising is carried out by adopting a vault-dominated improved RANSAC algorithm, and the algorithm effectively filters out strong noise and retains key lining points through constraint sampling, model geometric constraint and dynamically adjusted residual error threshold values; and performing nonlinear least square complete circle fitting on the denoised point cloud, and accurately extracting circle center and radius parameters. And finally, based on the absolute deviation, the relative deviation, the abrupt change increment and other multi-dimensional indexes of the radius, safety evaluation and early warning are carried out in combination with a standard threshold system. According to the invention, the robustness and precision of tunnel contour monitoring under complex working conditions of strong noise, equipment installation deviation and the like are obviously improved.

The invention provides an unmanned aerial vehicle group obstacle avoidance method and system based on least square and distributed arbitration, and belongs to the field of multi-unmanned aerial vehicle path planning, and the method comprises the steps: maintaining the historical observation data of a dynamic obstacle; a trajectory prediction model is constructed based on historical observation data, and a future trajectory prediction set is generated through least square fitting; selecting an optimal prediction model from the trajectory prediction set through a backtracking verification strategy; on the basis of a preset distributed arbitration rule, whether the local machine obtains the release right of the obstacle prediction information or not is judged; packaging the trajectory information corresponding to the optimal prediction model into a standard message by using the unmanned aerial vehicle which obtains the release right, and broadcasting the standard message to the cluster; and incorporating the obtained predicted trajectory message into collision avoidance constraint of a planner, performing local trajectory planning, and generating a collision-free flight path. According to the method, the safety, the collaboration and the passing efficiency of the cluster system in a real complex scene are remarkably improved.

The invention discloses a lunar balance dynamic parameter high-precision resolving method and system combining LRR and LLR technologies, and belongs to the field of deep space exploration and celestial body dynamics, and the method comprises the steps: constructing an LRR and LLR combined observation data set, and carrying out the preprocessing; constructing an LRR and LLR combined moon measurement model, and deducing a partial derivative of the model for a moon rotation parameter and a tidal LOVE number to obtain a combined partial derivative matrix; iterative solution is carried out through nonlinear least square to obtain dynamic parameters of the moon balance; and evaluating a resolving result and optimizing a resolving strategy, and optimizing a resolving scheme by dynamically adjusting the weight, improving a tide model and eliminating abnormal values. According to the method, the parameter calculation error is reduced, the technical advantages of two types of data are effectively integrated, the problem of insufficient observation continuity or precision of a single data source is solved, and reliable parameter support is provided for moon rotation and internal structure research.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

The invention discloses a GPS oscillator taming system and method based on PID parameter adaptive adjustment, and the system comprises a forgetting factor dynamic updating module which is used for dynamically updating a forgetting factor with the variance of the frequency error of VC-TCXO as a negative index; the system identification module is used for estimating ARX model parameters of the VC-TCXO by adopting a recursive least square algorithm with the updated forgetting factor; the model parameter mapping module is used for mapping the estimated ARX model parameters to obtain PID control parameters; the incremental PID controller is used for calculating a frequency control word increment based on the frequency error; the digital-to-analog converter is used for calculating according to the frequency control word increment to obtain a frequency control word and converting the frequency control word into analog voltage; and the VC-TCXO is used for obtaining the frequency of the GPS reference signal based on the analog voltage corresponding to the frequency control word so as to complete frequency taming. The method is high in calculation efficiency and robustness, and can adapt to the dynamic change and external disturbance of the controlled object in real time.

The invention relates to the technical field of vacuum heat insulation pipes, in particular to a vacuum heat insulation pipe regulation and control method based on a self-adaptive heat flow prediction algorithm, and the method comprises the steps: S1, collecting the temperature, vacuum degree, flow and other synchronous data of a partition; s2, calculating physical quantities such as partition heat flux density; s3, constructing feature vectors and recursively updating parameters; s4, establishing a heat flow prediction model and a vacuum conduction prediction model; s5, performing joint normalization on the thermal mode residual error and the vacuum mode residual error; s6, a target partition is determined through sparse inversion, and a partition regulation and control instruction is generated; s7, cooperatively setting air exhaust, air supply, bypass and heating under the constraint; and S8, recursive least square is matched with a forgetting factor to update model parameters online. According to the method, real-time positioning is achieved under the condition that conveying is not stopped, partition abnormity is restrained, and the vacuum degree of the partition interlayer cavity and the temperature of the outer wall of the inner pipe are stably maintained in a preset working condition window.

The invention discloses a propulsion system wake flow prediction method based on a physical information neural network, and belongs to the cross technical field of computational fluid mechanics and deep machine learning. The method provided by the invention comprises the following steps: firstly, acquiring geometric parameters and working condition parameters of a propeller in a propulsion system, and constructing an unsteady open water area flow field simulation model according to the geometric parameters and the working condition parameters; secondly, constructing high-fidelity wake flow simulation data and establishing a neural network data set; then constructing a neural network model used for capturing and predicting spatial-temporal characteristics of the flow field, inputting local flow field data in the data set into the neural network model in a sliding window mode for training, and extracting derivative information of each data point of the flow field by using three-dimensional convolution based on least square difference; and calculating the residual error of mass conservation and momentum conservation equations by using the derivative information, and constructing a loss function to optimize neural network parameters until the neural network outputs accurate target moment prediction flow field data, so that the flow field prediction precision is remarkably improved.

The invention belongs to the technical field of distributed multi-sensor passive detection multi-target tracking. The invention provides a factor graph optimization passive detection multi-target tracking method based on a Gaussian mixture model. According to the embodiment of the invention, the multi-target batch number is distributed by constructing the distributed passive sensor cooperative coordinate system and combining the multi-target identity judgment result between the two sensors; calculating direction finding lines based on two-dimensional observation of a sensor, combining the direction finding lines of the same batch number, obtaining a multi-target position estimation point set through a least square method, and obtaining a multi-target coarse positioning point through weighted fusion; modeling by adopting a Gaussian mixture model, fusing measurement distribution characteristics, solving parameters through an expectation maximization algorithm, and completing solvable conversion of an optimization problem; a factor graph optimization model containing multiple factors is constructed, state estimation is achieved through sliding window optimization, and track association and state updating are completed in combination with the Mahalanobis distance and the Hungary algorithm; and the passive detection multi-target tracking performance of the distributed sensor is effectively improved.

The invention discloses a motor multi-parameter identification method based on sampling noise excitation and bias compensation, and the method comprises the steps: firstly obtaining the voltage and sampling current of a motor, and enabling the sampling current to comprise sampling noise; then constructing an information evaluation index, and adjusting a proportional parameter of a current controller to improve the identification information amount; then, shaft voltage feed-forward compensation is constructed to suppress electromagnetic torque pulsation; recursion is carried out by adopting a least square method based on the discrete linear parameterization model to obtain a parameter initial value and a covariance matrix; secondly, estimating offset-containing parameters on line, and calculating a sampling noise variance by using an estimated residual error; and finally, obtaining unbiased parameter estimation according to bias compensation iteration updating so as to output online estimation values of stator resistance, direct-axis inductance Ld, quadrature-axis inductance Lq and permanent magnet flux linkage.

The invention relates to the technical field of industrial automation control, in particular to an inclined shaft skip attitude anomaly detection and control method based on multi-source data fusion. The method comprises the following steps: acquiring multi-source operation data, and performing time delay compensation on suspension tension data based on a cross-correlation principle to realize time-space synchronization of the multi-source data. And constructing a longitudinal dynamic observation model, and identifying the current load mass in real time by using a recursive least square method of an adaptive variable forgetting factor. And the inertial attitude data is combined with an unscented Kalman filtering algorithm of a robust estimation theory to suppress orbit impact interference and estimate an attitude inclination angle in real time. And constructing a dynamic safety envelope based on the current load quality and the running speed, calculating a dynamic attitude safety threshold value, and comparing the attitude inclination angle with the dynamic attitude safety threshold value to realize running control. According to the scheme, the load can be identified in real time, interference can be filtered out, the safety bottom line is dynamically adjusted, and accurate operation control under the complex working condition is achieved.

The embodiment of the invention provides a fluid characterization method for fracture frequency change attribute inversion and related equipment, which can be used for detecting fluid in a reservoir with fractures and improving the fluid identification precision. The method comprises the following steps: determining omnibearing pre-stack common midpoint gather CMP data; converting the CMP data into target angle gather data; determining azimuth difference seismic angle gather data according to the target angle gather data; performing spectral decomposition on the azimuth difference seismic angle gather data to obtain seismic frequency division difference angle gather data; constructing a function between a background rock matrix parameter and a crack weakness parameter based on a reflection theory; determining pre-stack seismic data of different orientations based on the function; determining synthesized azimuth difference data according to the pre-stack seismic data; constructing an inversion problem, and carrying out linear processing on the azimuth difference data to obtain a linear equation; and establishing a target function based on the linear equation, and solving through damping least square to obtain an inversion model, so as to detect the fluid in the fractured reservoir through the inversion model.

The invention relates to a double-antenna BDS-R real-time water level monitoring method and an integrated device, and belongs to the technical field of GNSS remote sensing. The method comprises the following steps: data acquisition; data processing: firstly decoding the received data, and extracting a double-frequency pseudo range and a carrier phase observation value; a second step of performing quality control on the decoded data, including constraint of an elevation angle and an azimuth angle, cycle slip detection and the like, and eliminating non-water-surface reflection signals and abnormal data; a third step of establishing a double-difference observation equation based on the pseudo-range and carrier phase observation value, introducing a horizontal baseline constraint into the observation equation, and enhancing the resolving stability through virtual observation quantity; fourthly, Kalman filtering is used for achieving per-epoch baseline estimation, and a least square ambiguity decorrelation adjustment (LAMBDA) method is used for solving a baseline fixed solution; and finally calculating the water level of the current epoch by using the baseline vector. And transmitting data. According to the invention, the problems of poor real-time performance, complex deployment and high cost in the existing water level monitoring technology are solved.

The invention discloses a large model mixed load-oriented self-adaptive low-delay reasoning configuration generation method and device, computer equipment and a storage medium, and the method comprises the steps: determining a first token generation delay and an adjacent token delay interval which are historically configured on requests with different reasoning configurations, and obtaining tuples to form a configuration performance database, generating a delay prediction model by combining a least square method with the configuration performance database; dynamically dividing the historical request into a plurality of buckets according to the input length and the output length through a self-adaptive bucket dividing strategy; a configuration generator generates reasoning configuration for each bucket according to the input length and the output length of the historical request of each bucket; under the real mixed load, the problems of remarkable resource contention, queue head blockage, KV Cache switching overhead increase and the like are avoided in concurrent execution of long and short requests, and meanwhile, the problem of tail delay amplification is avoided, so that a reasoning system gives consideration to low delay and high throughput among different requests.