300 results about "Fitting algorithm" patented technology

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention discloses a tower detection data optimization analysis method and system based on a point cloud model, and belongs to the technical field of electric power facility detection. Clustering analysis is carried out on original point cloud data of a tower by adopting a clustering algorithm, and different point cloud clusters are generated for different parts of the tower; finding out the central axis of the pole tower through a central axis fitting algorithm, carrying out the positioning and structural analysis of the pole tower according to the central axis, carrying out the sorting and splicing of the point cloud clusters of all parts of the pole tower according to the central axis, and comparing the point cloud data with ideal pole tower model data, and obtaining a target pole tower three-dimensional point cloud model; the health condition of the pole tower is evaluated through the model, integration of point cloud data of different parts of the pole tower is achieved, the construction precision of the pole tower three-dimensional point cloud model is improved, and therefore the pole tower condition detection accuracy is improved.

The invention discloses an automobile seat framework machining defect detection method based on machine vision, and particularly relates to the technical field of defect detection. The method comprises the following steps: constructing a multi-angle image acquisition and edge reflection modeling module aiming at complex defects such as weld joint pseudo soldering, microcracks, hole site deviation and collapse deformation, extracting weld joint continuity, edge integrity and hole site geometric consistency characteristics by using a deep neural network, and generating a structural feature vector; defect type recognition and credibility scoring are completed through small sample anomaly modeling and Gaussian mixture model classification, sub-pixel-level coordinate labeling of defect positions is achieved in combination with a Gaussian fitting algorithm, a defect positioning map is output, traceability analysis and severity grading are conducted based on historical data comparison, and the defect positioning accuracy is improved. The method is suitable for industrial online detection and quality closed-loop control.

The invention provides a non-training burr detection method and device for a cover plate inner hole, relates to the field of burr detection, and solves the technical problem of non-target area interference caused by incapability of effectively focusing on an inner hole area. The method comprises the following steps: acquiring a current frame image of a target cover plate, and storing the current frame image as a grey-scale map; performing threshold segmentation on the grey-scale map, and extracting a maximum outer contour region in the grey-scale map; and carrying out convex hull calculation, and removing a concave part in the maximum outer contour region to obtain a regular convex hull contour. And generating an optimal ellipse boundary through an ellipse fitting algorithm. And carrying out segmentation fitting to remove outliers to obtain a final ellipse boundary. And generating a binary mask based on the ellipse fitting region, extracting a pixel set of which the gray value is zero in the mask, and outputting a burr region. And calculating the total defect area, comparing the total defect area with a dynamically adjusted area threshold value, judging whether the cover plate is qualified or not, and feeding back a judgment result to the detection control system. The device is used in the burr detection process of the inner hole of the cover plate.

The invention provides a welding seam identification and trajectory optimization method and system based on three-dimensional vision, and the method specifically comprises the following steps: firstly, collecting the three-dimensional point cloud data of a welding seam, extracting the local geometric features of the welding seam, and carrying out the feature enhancement of the local geometric features, so as to screen out representative effective edge points; and then, collecting multi-source point cloud data of the welding seam area, constructing a multi-scale welding seam feature recognition network, inputting effective edge points obtained by screening into the network, realizing accurate recognition of the welding seam area, and further extracting a linear welding seam area. According to the recognition result, a fitting algorithm is adopted for conducting curve fitting on the weld joint area, and a preliminary welding track is generated; and for the condition that multiple sections of welding seams are intersected, an optimal fitting point searching mechanism is further introduced, the preliminary track is optimized, and the continuity and integrity of a welding path are ensured. And finally, based on the center line of the welding seam, the posture of the welding gun is precisely planned in combination with the dihedral structure model, and therefore high-precision track control and posture adjustment are achieved.

The invention provides a method for extracting equivalent parameters of a cavity resonant wireless power transmission system. The method comprises the following steps: S1, acquiring system input impedance spectrum data; s2, taking the input impedance spectrum data of the system as input, and utilizing a trained LSTM regression model to obtain a preliminary predicted value of an equivalent parameter of the system; s3, based on the preliminary prediction value of the equivalent parameters of the system, performing regression fitting by adopting a nonlinear fitting algorithm to obtain an accurate value of the equivalent parameters of the system; the system equivalent parameters comprise cavity internal resistance and cavity equivalent inductance of the resonant cavity and a mutual inductance value between the coil and the resonant cavity. The method has the advantages that equivalent parameters of the cavity resonant wireless power transmission system in all current mainstream modes can be effectively extracted; the NLS algorithm is prevented from convergence failure or falling into local optimum due to the fact that an initial value seriously deviates from a real value, and the fitting precision and stability are extremely high.

The invention provides an automobile air conditioner self-adaptive adjusting method and system based on multi-source data fusion, and belongs to the field of automobile heat management control systems. The method comprises the steps that the setting state of a user for the air conditioner, environment data and user state information are obtained, and the user state information comprises user characteristics, mental states and health indexes; on the basis of the setting state, the environment data and the user state information, personalized target air conditioner parameters are generated through statistical analysis and a fitting algorithm; according to the real-time environment change, the user state and the health data, the target air conditioner parameters are dynamically compensated, and optimized air conditioner control parameters are generated; and according to the optimized air conditioner control parameters, the air conditioner operation state is adjusted. The purpose of automatically adjusting the air conditioner to the expected state of the user under different states of different users can be achieved, and precise control over the climate in the cabin is achieved.

The invention provides a self-adaptive fitting method and system for a substrate deformation measurement system based on a fiber bragg grating (FBG), and the method comprises the steps: reading an output signal of an FBG sensor network, respectively collecting the wavelength data of a grating sensor and the stress data of a stress sensor at the same position as the grating sensor, and constructing a stress-wavelength data set; fBG data points with most sensitive deformation response are automatically identified, and a dynamic sensitive point set is formed and then preprocessed; a set needing to be continuously monitored is analyzed and determined, a specific point is selected to execute an adaptive segmentation fitting algorithm, segmentation points and fitting polynomial coefficients of all subintervals are output, and a final fitting model is constructed; according to the sub-interval of the wavelength value measured by the FBG sensor, calling a sub-interval fitting model, calculating a corresponding stress value, and further obtaining the deformation quantity of the measured object. According to the invention, the contradiction between the monitoring efficiency and precision of the traditional uniform layout in a complex stress scene is solved, and the technical path and the application target have remarkable industry uniqueness.

The invention discloses a Beidou and UWB dual-mode cooperative rail transit positioning method, and particularly relates to the technical field of fusion positioning, and the method comprises the following steps: constructing a cross-modal distortion discrimination array based on the space coverage state of two types of signals in a train path, recognizing a track mutation region, and dynamically adjusting the signal fusion weight; abrupt change points are corrected through a constraint backtracking trajectory fitting algorithm, and pseudo trajectory points are eliminated in combination with a positioning base station space topological structure; performing time sequence analysis on the signal fusion weight change, and if a frequent signal hopping trend exists, lowering the related fusion weight to the lowest tolerance threshold value to improve the track continuity and the positioning robustness; according to the method, the cross-modal distortion discrimination array is constructed, accurate identification of a track mutation region is realized, abnormal track points are effectively corrected in combination with fusion weight dynamic adjustment and a constraint backtracking track fitting algorithm, a signal jump trend is suppressed through a time sequence analysis mechanism, and the stability and positioning precision of track fusion are improved.

The invention discloses a numerical control machine tool autonomous tool setting method based on 3D visual guidance. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutter and a workpiece by using a 3D visual sensor and preprocessing; then, segmenting the point clouds through a region growing algorithm, realizing the separation of the tool point clouds and the workpiece point clouds, and extracting the point clouds of each cylindrical section of the workpiece; thirdly, based on a cylinder fitting algorithm, cylinder model fitting is conducted on the segmented tool and workpiece point cloud, and geometric parameters such as the axis direction, the radius and the center coordinate are obtained; calculating a relative position relation between the tool and the workpiece according to the geometric parameters, generating a tool setting path and converting the tool setting path into an NC code; and finally, the NC code is transmitted to a grinding machine control system through a grinding machine communication module, and tool setting operation is completed. The automatic tool setting device has the advantages that the relative position relation between the workpiece and the tool can be automatically measured in the machining process, automatic and rapid tool setting is completed, and the machining quality and efficiency are guaranteed.

The invention belongs to the field of gear machining detection, and particularly relates to a cylindrical gear ghost order detection method and detection equipment based on tooth surface waviness analysis. The method comprises the following steps: firstly, acquiring tooth surface deviation data of each tooth in a gear, carrying out resampling, inclination error correction and de-modification on the tooth surface deviation data, and then combining the tooth surface deviation data into full-tooth deviation curves in four directions; and then extracting waviness data and generating a full-tooth waviness curve. After RC is carried out on a full-tooth waviness curve, on one hand, the full-tooth waviness curve is converted into a frequency domain signal for selecting a frequency range, on the other hand, spectral analysis is carried out on a waviness signal through an iterative sine wave fitting algorithm within the specified frequency range, sine wave components are extracted, and a corresponding order spectrum is generated. And finally, when a sine wave component of which the amplitude is greater than or equal to 0.02 mu m and which is in a non-integral multiple harmonic order with the tooth number exists in the order spectrum of the target gear, detecting the gear ghost order. According to the invention, the problem of lack of a high-precision and high-efficiency waviness analysis and gear ghost order detection method in the prior art is solved.

The invention belongs to the technical field of semiconductor device performance testing, and particularly relates to a VCSEL epitaxial wafer resistivity distribution measuring method and system, and the method comprises the steps: constructing a three-dimensional discretization model representing an epitaxial wafer and an unknown parameter vector containing the vertical resistivity, the top transverse resistivity and the substrate leakage junction nonlinear resistance; a physical response function is defined, the vector is iterated through a discrete point fitting algorithm, so that the total residual error between the analog voltage difference output by the function and the actually measured voltage value under the cross-order-of-magnitude current is minimum, and the real vertical resistivity is extracted from the optimal parameter vector; and obtaining the vertical resistivity of each point on the two-dimensional test point grid so as to generate a distribution diagram and carry out anomaly detection. According to the invention, the problem of measurement distortion caused by the leakage defect of the substrate is solved, and the measurement accuracy of the vertical resistivity is improved.

The invention discloses an inter-ridge autonomous navigation method and system for an asparagus picking robot based on machine vision, and belongs to the technical field of agricultural automation. The method comprises the following steps: processing a field image acquired by a robot in real time by using a pre-trained lightweight inter-ridge path segmentation network to generate a path mask; extracting and smoothing left and right boundary points of the path based on the mask; fitting a middle navigation line; and finally, a control error is calculated according to the navigation line and the robot pose, and a steering instruction is generated to drive the robot to autonomously travel. Through lightweight model design and a robust path fitting algorithm, the problems that an existing visual navigation method is large in calculation amount, poor in real-time performance and insufficient in precision are solved, an efficient, accurate and easy-to-deploy autonomous navigation solution is provided for an agricultural robot, and the method is particularly suitable for complex field environments of ridge crops such as asparagus.

The invention discloses a ship water gauge scale line fitting algorithm, and relates to the technical field of ship water gauge scale calculation, and the method comprises the steps: obtaining a ship water gauge region image through an image collection device to extract a measurement point coordinate, and building a mapping relation between a pixel position and a physical draft; a weighted least square method is adopted to perform nonlinear curve fitting, a weight coefficient is calculated based on a local neighborhood standard deviation, a polynomial order p is dynamically adjusted according to an adjusted decision coefficient, and the decision coefficient is iteratively optimized to improve the balance between amplitude and model complexity. And the system repeatedly corrects model parameters through a dynamic verification mechanism of the residual sum of squares and a preset threshold value, and finally an accurate water gauge scale fitting curve is generated. The method effectively solves the problems that traditional manual observation is prone to environmental interference and large in measurement error, high robustness is kept in a complex water area environment, and reliable data support can be provided for the fields of ship load evaluation, channel safety management and the like.

The invention provides a weld joint extraction method based on multi-dimensional information fusion. The method comprises the following steps: S1, collecting a Texture image and depth data of a weld joint; s2, inputting the Texture image into an image recognition model to obtain a mask image of the welding seam; s3, binarization processing is carried out on the mask image, and pixel coordinates of the welding seam are extracted; s4, aligning the pixel coordinates with the depth data to generate an initial welding seam point cloud; s5, the initial welding seam point cloud is processed through plane segmentation and a fitting algorithm, and welding seam feature points are extracted; and S6, obtaining an accurate welding seam point cloud by using a straight line fitting algorithm or a plane intersection fitting algorithm. By combining the two-dimensional image segmentation and the three-dimensional point cloud processing technology, the problem that accurate three-dimensional information extraction and background noise interference resistance of the weld joint are difficult to consider simultaneously in a complex industrial scene by a traditional method is solved, the precision and robustness of weld joint extraction are improved, the processing efficiency is ensured, and the method is suitable for popularization and application. And a high-reliability three-dimensional path planning basis is provided for an automatic welding system.

A bridge in-service geometric form recognition method based on multi-point cloud fusion comprises: defining a bridge in-service geometric form; obtaining multi-point cloud data of a bridge girder in different service periods under the condition of not stopping traffic; converting bridge three-dimensional point cloud data obtained by multiple times of scanning to a same coordinate system; fusing the point cloud data obtained by multiple times of scanning using a regional point cloud fitting algorithm to obtain a continuous and smooth theoretical girder point cloud reflecting a true spatial form of a bridge; and extracting three-dimensional coordinates of all points at any target transverse position in the theoretical girder point cloud in a span extension direction to obtain the bridge in-service geometric form.

The invention relates to the technical field of semiconductor detection, in particular to a method and a system for measuring the thickness of a semiconductor epitaxial layer based on multi-beam interference correction. Acquiring infrared reflection spectrum data of the wafer under different incident angles, and performing double-stage smoothing preprocessing on the data by using a moving least square fitting algorithm; an interference extreme point is accurately identified by adopting a self-adaptive extreme value feature extraction strategy; the method comprises the following steps of: calculating the dynamic refractive index and the initial thickness estimation value of the epitaxial layer, constructing a multi-beam interference intensity distribution model, and finally providing a hybrid nonlinear optimization algorithm to invert the accurate thickness d of the epitaxial layer, thereby overcoming the measurement error caused by neglecting the multi-beam effect and the refractive index change in the traditional double-beam interference, and improving the accuracy of the multi-beam interference. And the precision and the reliability of epitaxial layer thickness measurement are remarkably improved through the combination of data preprocessing and a nonlinear optimization algorithm. The objective of the invention is to solve the problem of how to correct measurement errors caused by multi-beam effect and refractive index change.

The invention relates to a dynamic path prediction method for satellite antenna tracking and computer equipment, and the method comprises the steps: collecting satellite antenna tracking angle data which comprise original data and actual data; calculating a first deviation sequence and a second deviation sequence based on the original data and the actual data; performing curve fitting on the first deviation sequence and the second deviation sequence through a fitting algorithm, substituting a fitting result into a pre-constructed mathematical model to obtain a prediction model, and extrapolating and predicting a prediction deviation of a preset time point through the prediction model; and compensating the predicted deviation to original data corresponding to the preset time point, and generating a dynamic range instruction, so that the satellite antenna is aligned with the communication satellite under the action of the dynamic range instruction. And the satellite antenna can continuously align to the real position of the communication satellite.

The invention discloses a new energy system frequency domain modeling and equivalent circuit order reduction analysis method, and relates to the technical field of new energy power system modeling and stability analysis. The method is used for solving the problems that in an existing frequency domain modeling means, the automation degree of data acquisition is low, fitting precision and stability are poor, equivalent circuit universality is poor, and order reduction analysis is not systematic. The method comprises the following steps: acquiring system frequency domain response data based on disturbance injection; constructing a system rational function model by adopting an improved vector fitting algorithm; and then model order reduction is realized through state space modeling and an SVD-based balanced truncation method, and response consistency and stability analysis is carried out in a frequency domain and a time domain. The method can be widely applied to modeling analysis of new energy equipment such as wind power equipment, voltage source converters and high-voltage direct-current power transmission equipment, and has the advantages of being high in automation degree, high in modeling precision, good in visualization, clear in structure, suitable for simulation and control design and the like.

The invention provides an activatable head and neck cancer diagnosis and treatment integrated nanoprobe and a preparation method and application thereof.The preparation method includes the steps that according to an original data matrix, an adaptive spectrum correction algorithm is applied to eliminate background noise of an instrument, and when the relative fluctuation of spectral intensity exceeds a preset threshold value by 5%, a sliding window filtering technology is adopted, the window size is 5 nanometers, and the spectrum intensity is obtained; generating a de-noised spectral data set; measuring the pH value of the head and neck cancer tissue microenvironment by using a pH value sensor, recording the emission peak position change of the near-infrared quantum dots when the pH value is less than 6.5 in combination with the de-noising spectral data set, analyzing the frequency band displacement by adopting a Gaussian fitting algorithm, and generating a mapping data set of the pH value and the frequency band displacement; and aiming at the activation spectrum fingerprint data set, applying a dynamic spectrum matching algorithm, comparing with the spectrum response characteristic data set, calculating a cosine similarity coefficient, and when the similarity exceeds 0.85, determining the activation state and the tissue penetration depth of the nanoprobe, and generating a diagnosis and treatment parameter adjustment instruction set.

The invention relates to the technical field of textile equipment testing, in particular to a yarn textile high-speed spindle operation stability testing method and system, and the method comprises the following steps: S1, testing element layout; s2, initializing the system; s3, carrying out transient test; s4, testing a steady state; s5, interference processing: carrying out spectrum analysis on the originally acquired data, identifying interference frequency characteristics, eliminating interference by adopting a notch filtering and Kalman filtering combined method, and verifying a filtering effect; s6, parameter correlation modeling: constructing a vibration-temperature-tension-rotating speed parameter correlation model by adopting a control variable method, solving model parameters through a fitting algorithm and verifying validity; s7, quality tracing; s8, stability judgment; and S9, performing real-time monitoring and regulation. According to the method, a combined filtering scheme of notch filtering and Kalman filtering is adopted, fixed interference and random interference such as power frequency and mechanical resonance are suppressed in a targeted manner, and through comparison of locally amplified signals, the filtered signals are smooth in waveform and thorough in noise removal, and the signal-to-noise ratio and reliability of vibration detection signals are improved.

The invention provides a laser radar attitude compensation wind measurement method and system, and relates to the technical field of laser radar wind measurement, and the method comprises the steps: obtaining the six-degree-of-freedom motion data of a carrier; driving the two-axis stabilized platform to compensate based on the three-axis rotation angle so as to stabilize the direction of the laser beam, and scanning to obtain initial radial wind speed data; then synchronizing the translation speed with the wind speed data by using a linear fitting algorithm to obtain a synchronous data set; a laser radar scanning mode is determined, and a corresponding attitude compensation strategy is selected according to the mode; inputting the synchronous data set and the strategy into a pre-training machine learning model to generate a compensation parameter, and processing the initial data through Kalman filtering based on the parameter to obtain target radial wind speed data; and finally, performing wind field inversion calculation based on the target data to generate a wind field measurement result. According to the invention, the wind field measurement precision of the laser radar on the motion platform under the complex sea condition is improved.

The embodiment of the invention relates to the technical field of robot control, and provides a self-adaptive cleaning path planning method and system based on a swimming pool wall robot. The method comprises the following steps: acquiring a swimming pool space structure model, and acquiring a wall surface image by an underwater camera; deep learning is carried out to identify dirt types, and pixel gray analysis is carried out to obtain dirt density data; acquiring real-time environment information to build a wall surface environment map, and verifying and correcting dirt density data; constructing a dirt distribution thermodynamic diagram, and generating a global path by using an irregular boundary fitting algorithm in combination with the spatial model; adjusting local paths of the sub-regions according to the thermodynamic diagram and the real-time state of the robot, and matching the cleaning intensity with the path density; and the global path is preloaded, the corresponding local path is loaded when the robot enters the sub-region, and wall surface cleaning is completed. The cleaning path is planned in a multi-level mode through the space structure model and the dirt distribution thermodynamic diagram, the swimming pool wall surface cleaning efficiency is improved, the swimming pool wall surface cleaning coverage rate is guaranteed, cleaning blind areas are effectively reduced, and the swimming pool wall surface cleaning quality is improved.

The invention discloses a circular hole measurement method based on image feature recognition, a medium and a system. The method comprises the steps of S1, performing Gaussian filtering processing on an original image; s2, performing gray scale superposition projection through a sub-pixel circular boundary projection algorithm to generate a projection curve; s3, performing moving average filtering on the projection curve to generate a smooth curve; s4, performing differential operation based on the smooth curve to generate a differential curve; s5, screening an effective boundary feature point set on the differential curve; s6, for each extreme point in the effective boundary feature point set, calculating an adaptive threshold value in a neighborhood gray scale curve of the extreme point, and realizing sub-pixel-level positioning through gray scale straight line and smooth curve interpolation fitting; s7, generating an initial ellipse model from the sub-pixel feature points through a robustness ellipse fitting algorithm; and S8, iteratively optimizing a fitting result, removing abnormal points which deviate from the model and exceed a threshold value, and outputting an optimized ellipse parameter. The method has the advantages of high measurement precision and the like.

The invention discloses a rain detection radar clutter suppression and high-precision echo recovery method and a related device, and belongs to the technical field of weather radar signal processing. According to the method, a non-uniform Doppler filter bank design is adopted, a wide and deep null trap is formed near zero frequency to suppress ground clutters, and meanwhile, a clutter channel of a DFT filter bank is reserved to be used for detecting a low-speed / zero-speed target; self-adaptive cancellation processing is carried out in combination with the dynamically constructed azimuth-distance-velocity three-dimensional clutter map; and finally, recovering a rainfall echo frequency spectrum submerged by clutters through a Gaussian fitting algorithm to realize high-precision estimation of rainfall parameters. According to the method, the problems of insufficient weak rainfall echo detection capability, low-speed / zero-speed target information loss and large spectral moment estimation error of a traditional method under a strong clutter background are effectively solved.

The invention discloses a railway auxiliary positioning method and system for a signal fault scene, and the method comprises the following steps: a train auxiliary positioning system obtains a train positioning quality parameter and a train Beidou positioning signal, carries out the normalization processing of the train positioning quality parameter, and calculates a fault diagnosis coefficient; setting a sliding window threshold according to the fault diagnosis coefficient; designing a train auxiliary fitting algorithm to obtain a final fitting position of the train; and finally, jointly calculating the train position according to the train inertial navigation position and the final fitting position of the train. According to the method, the fault diagnosis coefficient is calculated according to the normalized train positioning quality parameters, then the fault severity of the positioning signal is judged, the richness and the calculation efficiency of positioning signal quality judgment are improved, the auxiliary fitting algorithm is designed, the fitting parameters of the train auxiliary fitting algorithm are solved continuously in an iteration mode, and the fault diagnosis accuracy is improved. The train position is calculated in combination with the train inertial navigation position, and the accuracy of the scheme is further improved.

The invention relates to the technical field of flow-state solidified soil detection, in particular to a flow-state solidified soil online flow value detection and feedback method and system, and the method comprises the steps: collecting multi-source signal data of flow-state solidified soil; adopting a data fitting algorithm to judge an equivalent flow value of the flow-state solidified soil; constructing a flow value prediction model of the flow-state solidified soil, and taking the equivalent flow value as the input of the flow value prediction model to obtain a real-time flow value of the flow-state solidified soil; comparing the real-time flow value with a preset target flow value, and automatically adjusting production control parameters of the flow-state solidified soil according to a comparison result; and visualizing the real-time flow value curve of the flow state solidified soil. Through the system and the method, the problems of lagging, poor precision and lack of a feedback mechanism in flow-state solidified soil flow value detection are effectively solved, online flow value detection in the flow-state solidified soil production process is realized, manual sampling is not needed, flow speed, pressure and visual signals are fused in flow value detection, the precision is high, and the response is fast.

The invention relates to the technical field of signal processing, in particular to a multi-dimensional force signal processing method and system, and the method comprises the steps: obtaining analog signals outputted by a plurality of strain bridges, carrying out the amplification and filtering of the analog signals, and converting the analog signals into digital signals; under the standard force load, the relation between the digital signal and the corresponding load is obtained, and a calibration data set is obtained; establishing a mapping model between the digital signal and the multi-dimensional force based on the calibration data, and determining decoupling parameters by adopting a fitting algorithm; the digital signals collected in real time are calculated based on the decoupling parameters, and force values of all dimensions are obtained; and outputting the force value of each dimension through a plurality of communication interfaces, and carrying out online calibration on the decoupling parameters. According to the invention, the problem of insufficient precision caused by coupling interference among different channels in a multi-dimensional force signal processing process in the prior art is effectively solved, and high-precision and stable multi-dimensional force value output is realized.

The invention discloses a machining method suitable for a large marine propeller, and relates to the technical field of precise combined machining technologies of the large marine propeller. The machining method comprises the steps that the propeller and a standard ball are installed on a machine tool rotating table, a high-precision calibration needle is installed on a main shaft, and a fitting algorithm is used for establishing a positioning reference of a workpiece; constructing a workpiece coordinate system and a conversion relation between the workpiece coordinate system and a machine tool coordinate system; acquiring point cloud data of each part of the propeller, and unifying the point cloud data to a workpiece coordinate system through coordinate conversion; performing coarse and fine splicing on the blade root point cloud, and forming workpiece coordinate system point cloud data of the propeller in combination with data of other parts; a normal projection algorithm is adopted to calculate the polishing amount based on the data; and according to the polishing amount, generating a machining track containing blade thickness calibration, and according to the machining track, milling and polishing the propeller. According to the method, the positioning precision is remarkably improved, the clamping error is effectively eliminated, the grinding amount is accurately and efficiently calculated, the adaptability is high, the machining efficiency is remarkably improved, and the production period is shortened.

The application discloses a macro modeling method for ensuring direct current precision, and the method comprises the following steps: reading n-port network parameters; initializing sampling points for constructing a macro model; initializing the order of the macro model; initializing the pole of the macro model; establishing a weighted linear system according to the current order, the sampling points and the pole; solving a linear equation set, judging convergence according to an error, and obtaining the macro model; and outputting the optimized macro model by applying a residue matrix disturbance to optimize the macro model. The macro modeling method for ensuring direct current precision provided by the application controls the direct current precision in two stages. In the first stage, a weighted least square method is adopted on the basis of a linear system established by a traditional vector fitting algorithm to preliminarily control the direct current precision. In the second stage, a small disturbance is applied to the residue matrix of the macro model, so that the disturbed macro model has accurate direct current characteristics.