1012 results about "Singular value decomposition" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the technical field of data processing, and discloses an automatic noise monitoring system and method based on multi-sensor data fusion. According to the system, a calibration module carries out time synchronization processing on noise data collected by multiple sensors; the extraction module adopts a singular value decomposition algorithm to extract frequency domain and time domain features; the separation module analyzes and separates traffic, construction and industrial noise sources through attention independent components; the construction module generates noise space propagation characteristics in combination with geographic information data; the classification module identifies the type of a noise source through a space-time convolutional network, locates coordinates and allocates responsibility weight. The technical problem that the existing noise monitoring technology cannot realize multi-source noise intelligent identification and pollution source accurate traceability is solved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for fine-tuning diffusion-based generative neural networks in compact parameter spaces for text-to-image generation. In one aspect, a method performed by one or more computers for fine-tuning a diffusion-based generative neural network to obtain a fine-tuned version of the diffusion-based generative neural network is described. The method includes: for each of a number of neural network layers of the diffusion-based generative neural network: obtaining an initial weight matrix including a number of pre-trained weights parametrizing the neural network layer: performing a singular value decomposition on the initial weight matrix; and re-parametrizing the neural network layer with new weights that depend on spectral sifts; and training the spectral shifts of each of the number of neural network layers of the diffusion-based generative neural network to obtain the fine-tuned version of the diffusion-based generative neural network.

The invention discloses a large language model progressive field fine tuning and knowledge fusion method for shield engineering. The method comprises the following steps: constructing a layered shield training course containing a wide-area academic theory and a proprietary enterprise construction method; parallelly training a plurality of physically isolated parameter efficient adapters based on the frozen base; performing singular value decomposition on the adapter, extracting a geometric feature subspace representing knowledge distribution, and calculating a conflict correlation degree; based on this, a uniform adaptation mechanism of resource awareness is constructed. The mechanism not only can generate a static fusion model for conflict removal, but also can dynamically activate a specific rank slice of an adapter through a routing network based on real-time hardware resource budget (video memory / FLOPs) and geometry-resource signature. According to the method, multi-source knowledge is reserved, and adaptive dynamic scheduling of edge hardware resources by model reasoning is realized.

The invention discloses a natural language semantic recognition method, device and equipment and a medium, and the method comprises the steps: extracting semantic features according to an input natural language, inputting the semantic features into an initial hybrid expert model, and analyzing the semantic features through a Transform model to obtain a hidden state matrix; inputting the hidden state matrix into a gating network to generate a routing weight matrix; on the basis of rank estimation of randomized singular value decomposition, generating the number K of expert networks activated by the current layer in combination with the hidden state matrix; selecting first K expert networks according to a sorting result of the routing weight matrix, generating a mask matrix, executing sparse forward calculation, and optimizing model parameters of the expert networks based on sparse gradient propagation and load balancing loss to obtain a target hybrid expert model; and the target hybrid expert model is adopted to recognize the semantic features to obtain a semantic recognition result, and the semantic recognition accuracy and efficiency are improved by adopting the semantic recognition method and device.

Provided is a method for improved hand-eye calibration method based on a structured light camera. The method includes: step 1: establishing a pinhole camera model, and using a depth camera to detect a three-dimensional (3D) coordinate to obtain a physical coordinate of each point in an image coordinate system with a known depth relative to a camera coordinate system; step 2: establishing a Denavit-Hartenberg (DH) model of a robotic arm, and moving the robotic arm to a determined coordinate using inverse kinematics; step 3: collecting n sets of point cloud data, applying depth scaling coefficients to the n sets of point cloud data to perform Singular Value Decomposition (SVD), and solving for an optimal depth scaling coefficient using a Nelder-Mead algorithm; and step 4: completing the hand-eye calibration of the robotic arm based on the solved optimal depth scaling coefficient. The method offers strong operability and robustness.

The invention relates to the technical field of artificial intelligence, and discloses a large model continuous learning method and device based on low-rank adaptation, equipment and a medium, which are applied to a scene that a financial institution updates a credit scoring model of a user in real time, and the method comprises the following steps: when a basic large model continuous learning task is received, obtaining an initial task text, constructing training data based on the initial task text; performing singular value decomposition on the parameters of the original low-rank matrix to generate principal component parameters and residual parameters; freezing the principal component parameters, and updating the updated original low-rank matrix by adopting a singular value loss function and an orthogonal loss function based on the residual parameters of the training data to generate a basic low-rank matrix; performing iterative training on the basic large model based on the training data and the basic low-rank matrix to generate a target large language model; and predicting the to-be-predicted task data through the target large language model. According to the method, the problem of disastrous forgetting is relieved, and computing resource consumption is reduced.

The invention provides a fault diagnosis intelligent analysis method and system for industrial equipment, and a storage medium. The method comprises the following steps: segmenting monitoring time sequence data into at least one sub-sequence based on statistical characteristics and working conditions of the monitoring time sequence data; determining the length of an embedded window based on the local characteristics of the subsequences, constructing a trajectory matrix by using the length of the embedded window, and performing singular value decomposition on the trajectory matrix to obtain a primitive matrix and a corresponding singular value; forming candidate groups for the primitive matrixes of different preset combinations, calculating a separability metric value of each candidate group, and determining a component group based on the separability metric values and the singular values; performing diagonal averaging reconstruction on the selected component group to obtain signal components, and calculating preset characteristic parameters of each signal component; and according to the preset feature parameters, target signal components related to the preset fault mode are screened out, feature vectors representing the fault state are extracted from the target signal components, the feature vectors are input into a pre-configured fault diagnosis model, and fault diagnosis information is output.

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 provides a federated learning backdoor defense method based on singular value decomposition and model weight amplification, and the method comprises the steps: carrying out the normalization processing of a model updating parameter locally trained by a client, and obtaining a normalized model updating parameter; on the basis of the normalized model updating parameters, model updating parameters after dimension reduction are obtained; performing clustering algorithm processing on the model updating parameters after dimension reduction to obtain clustered clusters; obtaining cluster model parameters based on the clustered clusters; combining the cluster model parameters into a cluster model parameter matrix; performing singular value decomposition on the cluster model parameter matrix to obtain a singular vector; obtaining a trust score through the singular vector; obtaining global model update parameters based on the trust score; calculating by utilizing the global model updating parameters to obtain a global model; and adding Gaussian noise to the global model through a differential privacy mechanism to obtain a final global model. According to the method, backdoor attacks can still be effectively resisted under the scene that the client data sets are non-independent and identically distributed.

The invention provides a production line product quality tendency defect determination method, medium and system, and belongs to the technical field of production digital data processing.The production line product quality tendency defect determination method comprises the steps that firstly, quality detection data is collected to construct a feature matrix, and a time sequence feature vector is obtained through singular value decomposition; and calculating a defect tendency index, and establishing association mapping in combination with a defect category vector. Then calculating the parameter utility by adopting a multi-level analysis method, and weighting the defect tendency index; a bidirectional long-short-term memory neural network is constructed as a discrimination model, a special defect screening layer is included, and dynamic identification of defect types is realized. According to the method, a defect feature database is established to store historical data, real-time monitoring and early warning of production line products are realized through model training and association rule mining, and finally early warning information is output, so that the technical problem that the quality defect tendency of the production line products cannot be accurately identified and predicted in the prior art is solved.

The invention discloses a logic board comprehensive performance testing device and testing method, and relates to the technical field of electronic equipment testing. A time sequence signal, a power consumption curve and infrared temperature data are synchronously acquired through a logic board interface, a time-space aligned fusion data set is generated by using a dynamic time warping algorithm, a power consumption coupling analysis model is input, a dynamic incidence matrix of a signal integrity parameter and power consumption fluctuation is acquired, and key coupling characteristics are extracted through singular value decomposition. And constructing an adaptive neighborhood density clustering model according to the correlation mode parameters and the temperature gradient distribution, generating an abnormal region probability graph, and marking electrothermal coupling abnormal coordinates and confidence. And inputting the fusion data, the correlation parameters and the abnormal probability graph into a graph neural network, taking a time sequence signal spectrum entropy, a local temperature mean value and a power consumption fluctuation variance as node features, taking an electrothermal coupling coefficient as an edge weight, iteratively updating a node state through a graph attention mechanism, and outputting a defect type and a three-dimensional coordinate. And the defect detection accuracy is effectively improved.

The invention provides an arch bridge cable force adjusting method and device, a medium and a program product, and relates to the technical field of arch bridge cable force adjusting.The method comprises the steps that by establishing an arch bridge parameterization finite element model, specific unit force is applied to all suspenders to construct a first influence matrix; singular value decomposition is carried out on the first influence matrix, the first k suspenders with the total energy contribution rate exceeding a first preset value are extracted, and the sensitivity of the suspenders to the global suspender cable force is calculated; calculating an importance index in combination with sensitivity and suspender cable force deviation, and screening the first n important key suspenders to construct a second influence matrix; an adjusted vector is obtained based on the difference value of the second influence matrix and the target influence matrix, and the cable force adjustment amount of the key suspender is solved; the key suspender cable force is sequentially adjusted according to the importance sequence. According to the method, the key suspender is accurately identified through matrix decomposition and sensitivity analysis, the adjustment amount is quantified, interaction is considered, full suspender adjustment is avoided, the construction cost is remarkably reduced, and the cable force adjustment efficiency and accuracy are improved.

The invention relates to the technical field of spectral analysis, and discloses a visible spectrum noise reduction and heavy metal ion detection method, which comprises the following steps: reacting a water sample to be detected with a color developing agent to generate a color developing system, obtaining a gray spectrum signal through optical imaging equipment, carrying out variational mode decomposition, optimizing the signal by adopting singular value decomposition, and calculating the noise of the heavy metal ions. And determining an optimal reconstruction order by using a particle swarm optimization algorithm and reconstructing a signal, finally extracting a feature value from the denoised spectral signal, and predicting the type and concentration of metal ions through a neural network model. Spectral global / local features are modeled through cooperation of multi-head self-attention and learnable position coding, signal frequency-time domain cross-modal fusion is realized in combination with variational mode decomposition and cross attention, training parameters are dynamically optimized by innovatively adopting a reinforcement learning strategy, classification and regression tasks are self-adaptively balanced based on a gradient diversity reward mechanism, and frequency-time domain cross-modal fusion is realized. And finally, dual precision improvement of ion type identification and concentration prediction and model robustness enhancement are realized in a complex noise scene.

The invention provides a high-resolution emission flux inversion system and method. The method comprises the following steps: collecting atmosphere multi-source data; determining a distance weighting function, and calculating the correlation between the grid points in the simulation area and the observation value; generating a set sample meeting physical constraints of the assimilation object; performing singular value decomposition and dimension reduction on the set samples; replacing a tangent line and an adjoint mode of a regional air quality mode with a mixed assimilation method, and obtaining a simulated regional space grid point analysis increment; obtaining corrected concentration and flux distribution data; the data is used for carrying out emission flux inversion, and the unit time emission flux of different positions is estimated. According to the method, a traditional four-dimensional variation mode is replaced with a mixed assimilation method, and the calculation and programming difficulty is reduced; generating a set sample by using a four-dimensional sliding sampling algorithm, reducing dimensions, and calculating resource consumption; a joint assimilation algorithm is introduced to optimize the concentration and flux field, and accurate inversion of the high-temporal-spatial-resolution emission flux is achieved.

The invention provides a power load prediction method based on a space-time diagram convolutional network in extreme weather. Comprising the following steps: collecting historical load data and regional meteorological element data of a plurality of load nodes in a power system, and screening key meteorological characteristics which have obvious influence on loads through a mode of combining model interpretation and regression analysis to construct a meteorological characteristic vector; multivariable empirical mode decomposition and singular value decomposition are adopted to carry out multi-scale reconstruction on load data, and smooth and effective load feature tensors are extracted. On the basis, a graph network structure is constructed in combination with a node physical connection relationship, and load and meteorological characteristics are fused in a time dimension to form node time sequence characteristics. And predicting the load by using the space-time diagram convolutional network model. According to the method, the space-time dependency relationship of the load data can be effectively modeled, the prediction accuracy of the load change under the extreme weather condition is enhanced, and the method has good robustness and generalization ability.

The invention discloses a motor abnormal sound detection method and system based on contact acquisition and small sample learning, and the method comprises the steps: directly coupling a motor housing through a contact vibration sensor, collecting an original vibration signal, and generating an anti-interference vibration signal; inputting the anti-interference vibration signal into a nonlinear resonance enhancement module to generate an enhanced sound signal; performing wavelet packet decomposition on the enhanced sound signal, extracting a multi-scale frequency band energy entropy, and generating a motor abnormal sound feature matrix by combining singular value decomposition dimension reduction; on the basis of a dynamic weight distribution element learning algorithm, a small number of normal samples and abnormal samples are utilized to construct an abnormal sound classification model; and inputting the motor abnormal sound characteristic matrix into an abnormal sound classification model, detecting transient abnormality through a sliding window time sequence matching algorithm, and outputting an abnormal sound judgment result. According to the embodiment of the invention, high-precision and low-false-alarm motor abnormal sound detection under complex working conditions can be realized.

The invention discloses a sudden drought identification method and system based on a space-time double-branch fusion model. The method comprises the steps that meteorological data are acquired and preprocessed; calculating a composite sudden drought index according to the obtained data; performing data dimension reduction on the obtained data through singular value decomposition (SVD); a deep learning model is adopted to construct a time branch model, a graph attention network GAT is adopted to construct a space branch model, and the time branch model and the space branch model are dynamically fused through a cross attention mechanism to construct a space-time double-branch fusion model; according to the method, data dimensions are compressed and model complexity is reduced by fusing multi-source variables and combining singular value decomposition (SVD), meanwhile, geographic neighborhood weights are dynamically learned by adopting Transforme and based on a graph attention network (GAT), dynamic fusion of spatial-temporal characteristics is finally realized through a cross attention mechanism, a sudden drought recognition result is generated, and the method has the advantages of being high in robustness, high in accuracy and high in reliability. The limitation of a traditional method on nonlinear feature capture, space-time modeling splitting and generalization ability is broken through.

The invention discloses a federated learning method for realizing client selection based on data feature clustering, which comprises the following steps of: firstly, performing singular value decomposition on local data by a client, and combining left singular vectors corresponding to first k singular values as local data features; secondly, performing hierarchical clustering on clients by taking included angles between local data features as similarity measurement standards; and then, constructing a client contribution quantification model, comprehensively considering data quality, sample capacity and selected times, refining contributions of the clients, and selecting h clients with the highest current contribution degree to participate in global training. And finally, during each global training, the server selects h clients from each group to carry out current global training, and the server carries out weighted aggregation to obtain a new global model. According to the method, the data isomerism is effectively relieved, the global model prediction precision is improved, and the problem of low global model precision caused by the data isomerism in a federated learning scene is solved.

The invention provides a photovoltaic inverter operation state diagnosis method, which belongs to the technical field of photovoltaic inverters, and comprises the following steps: collecting multi-source operation signals of a photovoltaic inverter, carrying out wavelet packet decomposition on the signals to construct a time-frequency characteristic dense matrix, generating a fault characteristic super-sparse representation vector through singular value decomposition and sparse processing, and carrying out fault characteristic super-sparse representation on the fault characteristic super-sparse representation vector. Performing envelope demodulation on sensitive mode components obtained by complete set empirical mode decomposition to extract approaching periodic feature vectors, and inputting three types of complementary features into a weak fault recognition model with a circulation attention mechanism to perform fusion diagnosis. And whether preventive maintenance early warning is triggered or not is judged according to the output determinant characteristic value of the convergence state matrix, and an operation strategy is adjusted. The technical problem that early weak fault characteristics of the photovoltaic inverter are difficult to be accurately identified and early warned in time in a strong noise background is solved.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention belongs to the technical field of GNSS-R SAR (Global Navigation Satellite System-Radar Synthetic Aperture Radar) super-resolution imaging, and discloses a fast distance super-resolution imaging method based on a GNSS-R SAR. According to the method, matrix compression, regularization modeling and a rapid optimization algorithm are creatively combined, and a set of efficient and stable distance super-resolution processing flow is formed. Specifically, after a preliminary imaging result of a GNSS echo signal is obtained and a signal convolution model form of the GNSS echo signal is given, firstly, dimension reduction processing is performed on an observation matrix in a signal convolution model through singular value decomposition, and on the basis of a weighted singular value maintenance strategy processing result, the signal convolution model is reconstructed by using an inverse matrix of a truncated measurement matrix; then, starting from a regularization strategy, introducing an L1 norm constraint to construct a target function by utilizing the sparse characteristic of a target; and finally, solving the target function by adopting a rapid iterative optimization algorithm. According to the method provided by the invention, the range resolution of the GNSS echo data is remarkably improved.

The invention discloses a robot hand-eye calibration method, control equipment and a robot system, and relates to the technical field of robots. The robot hand-eye calibration method comprises the following steps: uniformly correcting a plurality of pixel coordinates obtained by dynamic shooting to a reference pixel coordinate system which is consistent with a camera coordinate system at a reference position in direction; and meanwhile, the world coordinates of the end effector corresponding to each sampling position are localized relative to the world coordinates of the end effector at the reference position, so that interference caused by global coordinate offset and camera orientation difference is eliminated, and the application range of the hand-eye calibration method is expanded. Besides, a singular value decomposition algorithm is adopted to solve a mapping relation between pixel coordinates and local world coordinates under a reference pixel coordinate system, so that a solved rotation matrix can meet orthogonality constraints, the hand-eye transformation process strictly conforms to a robot motion model with translation and rotation motion, and the calibration precision is improved. The calibration process can be automatically calculated and completed, and the calibration efficiency is high.

The invention discloses a method for predicting a favorable area of a thin-layer stacked sand body, and relates to the technical field of exploration and development of oil and gas reservoirs, and the method comprises the steps: generating a synthetic seismic record through well-seismic combination by using interval transit time and a density curve, aligning the synthetic seismic record with an actual seismic record, and building a three-dimensional frame model of a target layer of a research area; performing abnormal value elimination and standardization processing on the logging curve, making a statistical histogram, comparing peak separation degrees of sandstone and mudstone, and selecting the most sensitive curve to distinguish the sandstone and mudstone; and realizing well-side seismic trace waveform dynamic clustering analysis through singular value decomposition, and determining the number of effective samples. Based on the result of waveform indication simulation, the method is combined with the optimized seismic attribute to predict the thin-layer stacked sand body together, a systematic method is provided, the multiplicity of solutions of predicting the thin-layer stacked favorable sand body through the seismic attribute and the uncertainty of depicting the sand body by the sedimentary facies in the area with few wells can be reduced, and the prediction accuracy of the thin-layer stacked favorable sand body is improved. Therefore, the precision and reliability of thin-layer stacked sand body prediction are improved.

The invention belongs to the field of time synchronization and data processing, and discloses a satellite and Rowland timing data fusion method and related device.Firstly, original data are intercepted and subjected to mean value removal to eliminate baseline offset, and a frequency domain matrix is reconstructed by combining frequency domain conversion with singular value decomposition; according to the method, periodic term interference signals in a frequency domain are accurately recognized and filtered out through a dynamic threshold strategy, then effective components are reserved through time domain conversion, finally, a noise covariance matrix and observation model parameters are dynamically adjusted based on an adaptive Kalman filtering algorithm, and dynamic weight fusion of double-source data is achieved. By the adoption of the method, the defects that a traditional weighted average method is insensitive in fixed weight, Kalman filtering parameters are rigid and global interference suppression of wavelet transformation is insufficient are effectively overcome, the suppression capacity for non-stationary noise and periodic interference is remarkably improved, fused data have the high precision of a satellite system and the anti-interference characteristic of a Rowland system, and the method is suitable for being applied to the field of satellite communication. And finally, high-reliability and high-stability time synchronization performance is realized.

The invention discloses a partial discharge signal denoising method based on STFT-SVD and Bayesian kurtosis threshold adaptive optimization, and the method comprises the steps: collecting an analog signal outputted by a high-frequency current transformer, carrying out the analog-to-digital conversion, obtaining a one-dimensional time domain signal sequence, carrying out the DC component removal and amplitude normalization of the signal, and obtaining a preprocessing time domain signal; performing short-time Fourier transform on the preprocessed time-domain signal to obtain a time-frequency spectrum, suppressing low-amplitude noise by adopting a soft mask method, and retaining main characteristics of partial discharge pulses; performing singular value decomposition on the time-frequency spectrum after soft masking, automatically selecting a principal component number according to a principal component, and adaptively reserving a main signal component to obtain a principal component spectrum; and performing inverse short-time Fourier transform on the principal component atlas to reconstruct a time domain signal, adaptively selecting a kurtosis threshold in combination with a Bayesian optimization algorithm, and outputting a denoised time domain signal.

The invention belongs to the technical field of wireless communication, and discloses a CNN-Transform direction estimation method based on covariance-unitary matrix input, and the method comprises the following steps: generating received signal sample data of different incident angles; calculating a covariance matrix for a received signal sample, performing singular value decomposition, extracting real parts and imaginary parts of the covariance matrix and a unitary matrix, and constructing a four-channel two-dimensional real number tensor as input; extracting local spatial features and coherence structures through CNN; serializing the feature map and adding a position code; a Transform encoder module is input, and global spatial dependence is modeled; carrying out average pooling and full-connection classification on the output to realize direction angle prediction; and training is carried out by adopting cross entropy loss, an AdamW optimizer and a cosine annealing learning rate scheduler. According to the method, local and global features are fused, and the method has high precision, strong robustness and excellent generalization ability in complex environments of low signal-to-noise ratio, multipath interference and the like.

The invention relates to the technical field of quantum computing, and provides a linear complexity quantum state preparation method based on tensor decomposition and a quantum circuit construction system.The high-dimensional tensor is decomposed into a series of low-rank core tensors through continuous singular value decomposition, each core tensor in a core tensor sequence is expanded into a unitary matrix, and the unitary matrix is used as a quantum circuit; the unitary matrix sequence is mapped to quantum lines coupled using adjacent qubits, and the quantum lines are run to prepare a target quantum state. Based on this, the line generated by the method can approximately or accurately prepare a target quantum state only by coupling adjacent quantum bits, and is perfectly adaptive to quantum chips of linear or grid topologies such as superconducting and semiconductor quantum dots and the like.

The invention provides a frame processing method, which is applied to beam forming receiving equipment, and comprises the following steps: in response to a current channel detection frame of beam forming transmitting equipment, performing channel estimation on the current channel detection frame to obtain a channel estimation result; selecting at least one target receiving antenna port from a plurality of receiving antenna ports of the beamforming receiving equipment; performing singular value decomposition on the channel estimation result according to the target receiving antenna port to obtain a singular value decomposition result; and determining report information corresponding to the singular value decomposition result, and sending a report frame carrying the report information to the beamforming transmitting equipment. According to the method, more autonomous and efficient detection can be realized according to the self-selected target receiving antenna port for singular value decomposition.

The invention provides a rock body point cloud registration method, apparatus and device, and a storage medium thereof. The method comprises the steps of obtaining source point cloud data and target point cloud data; executing a target operation based on the source point cloud data to obtain a plurality of source concave-convex descriptors, and executing the target operation based on the target point cloud data to obtain a plurality of target concave-convex descriptors; determining a plurality of pairs of available matching point pairs of which the editing distances meet a first preset threshold value from the source concave-convex descriptor and the target concave-convex descriptor; selecting three target matching point pairs from all available matching point pairs, and calculating a transformation matrix based on the target matching point pairs and a singular value decomposition algorithm; and determining an optimal transformation matrix based on all the transformation matrixes, controlling the source point cloud data to perform pose transformation based on the optimal transformation matrix to obtain an initial registration result, and adjusting the initial registration result based on the transformation matrixes to obtain a target registration result. According to the technical scheme of the embodiment of the invention, the registration precision of the two point cloud data with low overlapping rate and large angle deflection can be improved.