110 results about "Sparse model" patented technology

A submerged arc furnace intelligent inspection system based on cloud side-end cooperation belongs to the technical field of submerged arc furnace intelligent robot inspection, and comprises an end side device, an edge side platform, a cloud platform and a cross-layer cooperative processing module, the end side device is in communication connection with the edge side platform, and the edge side platform is in communication connection with the cloud platform; and the cross-layer cooperative processing module is used for intelligently deploying global resources of the end-side equipment, the edge-side platform and the cloud platform, predicting a future load trend by adopting a dynamic sparse LSTM model algorithm, inputting a prediction result into a security constraint PPO reinforcement learning algorithm to adaptively adjust a task and a computing power allocation proportion, and carrying out dynamic resource allocation according to an output result. According to the invention, the cross-layer cooperative processing module interacts and communicates with the cloud side, the edge side and the end side, so that a dynamic allocation strategy of resources is realized, and the problems of low resource utilization rate and poor real-time performance caused by a rigid resource allocation strategy during routing inspection are avoided.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention belongs to the technical field of robots, and discloses a feature fusion and refining embedding sparse Bayesian learning method and system for robot curved surface milling contour error monitoring, and the method comprises the steps: carrying out the structural fusion of feature vectors composed of technological parameters, robot rigidity, cutting force and tracking errors into a dictionary matrix; and a sparse Bayesian learning method is combined to realize robot processing error monitoring. During model training, the posterior distribution of the weight variables can be obtained, and the expectation and variance of the posterior distribution of the weight coefficients are estimated by maximizing the posterior probability. Wherein the variance and the hyper-parameter influencing the weight sparsity are estimated by maximizing an edge likelihood function, that is, optimization is carried out by the expectation of a joint likelihood function under maximized weight posterior distribution, and the minimum value solution can be solved for the converted loss function to optimize the hyper-parameter. The posterior expectation and variance of the model weight and the hyper-parameter are iteratively updated in the training process, and the iteration process is terminated according to a set change threshold value so as to realize the sparsification of the model weight vector and the construction of the sparse model. According to the method, robot milling error monitoring of curved surface parts with different features under the variable poses is achieved, and the part measurement cost is effectively reduced.

The invention discloses a power adaptive digital pre-distortion method, system and device based on sparse GRU and a medium. The method comprises the steps that input and output signals under different powers are collected, a composite feature vector is constructed through amplitude normalization and phase alignment, and a pre-distortion training target is generated in combination with indirect learning; constructing a sparse GRU neural network, introducing L1 regularization constraint, and obtaining a sparse pre-distortion model through joint optimization of mean square error and sparse constraint; partial weight updating is realized based on power grading, and weight fusion output is carried out during power interval switching so as to complete power adaptive optimization; pruning and compressing the sparse GRU model and then deploying the sparse GRU model in a pre-distortion module to compensate the nonlinearity of the power amplifier; according to the method, the model complexity is reduced through rarefaction, the dynamic working condition stability is improved through a power adaptive mechanism, pruning compression adapts to low-power-consumption hardware, high precision, low complexity and adaptive capacity are considered, and the method is suitable for the low-power-consumption and high-performance requirements of a broadband wireless communication system.

The invention relates to the crossing field of a federated learning algorithm framework, dynamic sparse training and a spiking neural network, and discloses a federated learning method and architecture for dynamic sparse spiking neural network optimization, and the method comprises the steps: employing a cloud edge end hierarchical architecture, and carrying out the training of local data on an edge device through a spiking neural network; the global model and the sparse mask received by the edge device generate a local sparse model for training; after each round of local training, the edge device uploads new model parameters to the edge server; after the edge server receives the model parameters, the model parameters are aggregated in combination with information such as historical contribution degree and the like, and are uploaded to the central cloud server; on the cloud server, the model parameters are further aggregated to generate a global model, and the global model is broadcasted to each edge server and each edge device before the next round of training. The sparse training and the spiking neural network are introduced into federated learning, so that the computing communication overhead is remarkably reduced, and the performance of the global model is improved.

The invention relates to the technical field of the Internet of Things, in particular to a lightweight distributed model aggregation method applied to a low-power-consumption chip of the Internet of Things, and is suitable for an Internet of Things system composed of a cloud server and a plurality of edge nodes. Each edge node learns an optimal strategy from offline data, and meanwhile, a complex teacher model is compressed into a lightweight student model through a knowledge distillation technology, so that the local calculation burden is reduced. And each edge node uploads quantized and sparse model parameters to the cloud server, and the cloud server performs federal aggregation by adopting a weighted average method to generate a global model and feeds back the global model to each terminal on the premise of ensuring data security. In order to adapt to the practical application of a low-power-consumption chip, a dynamic voltage frequency regulation technology is introduced in the hardware level, and the energy consumption and the communication overhead are effectively reduced. According to the method, the model training process of the edge nodes of the Internet of Things is optimized, and federated learning of privacy protection, efficient communication and cooperative self-adaption is realized.

The invention discloses a sparse bert model acceleration method and device for a multi-core vector processor, equipment and a medium, and relates to the technical field of vector processors, and the method comprises the steps: constructing a pre-training language model for adjustment and optimization; determining the type of the graph optimization operation; the type is operator fine tuning, and the graph optimization operation of the target application scene is realized by utilizing the optimized pre-training language model and a multi-core vector processor in combination with layer normalization and matrix transposition; the type is operator fusion, operator fusion is carried out on each layer of encoders in the optimized pre-training language model based on a multi-core vector processor, and graph optimization operation on a target application scene is realized; the type is storage optimization, data storage structure optimization and data reuse design are carried out on the optimized pre-training language model based on the multi-core vector processor, graph optimization operation on a target application scene is achieved, the parallel computing capacity of the multi-core vector processor can be brought into full play, and the data reusability and the data storage effect are improved.

The invention relates to the technical field of intelligent construction, and discloses a bent cap intelligent jacking construction progress intelligent prediction method, which comprises the following steps: collecting multi-source real-time data such as equipment, structure, personnel and environment in a construction process, and calculating a construction entropy for measuring system uncertainty; establishing a causal state evolution model, and predicting the evolution trend of the construction entropy in a future time period; and when it is predicted that a construction progress disturbance risk exists, performing anti-fact deduction based on the model or the dynamically trimmed sparse model thereof, and generating and recommending an optimal adaptive correction strategy. According to the method, the overall uncertainty of the construction system is quantified into the construction entropy, and the prospective deduction capability of the causal state evolution model is combined, so that the conversion from the risk passive response depending on experience to the data-driven beforehand active early warning and optimal decision recommendation is realized; and improvement of scientificity, foresight and timeliness of construction risk management is facilitated.

The invention provides a sparse Transform model reasoning acceleration method based on a GPU platform, and the method comprises the steps: building a first storage format in a sparse Transform model, and carrying out the coding of a sparse mask of any type through the first storage format; wherein the first storage format comprises an inner layer block and an outer layer block; an outer layer block adopts a block compression sparse row structure, and row pointers and column indexes of a completely effective block and a partially effective block are recorded; the inner layer block adopts an unsigned 64-bit integer value bitmap and is used for representing masks in the block; based on the sparse mask matrix coded by the first storage format, performing sparse calculation on the query, the key and the value, and outputting a corresponding context vector; compared with a traditional sparse mask storage structure in the prior art that calculation sparsity and memory access regularity are difficult to consider when the masks present random, global or mixed distribution characteristics, the method has the advantages that support for any type of masks is achieved, storage overhead is reduced, memory access continuity is improved, good GPU parallelism is kept, and thread divergence and memory access conflicts are avoided.

The application relates to a shape memory alloy soft driver control method based on data-driven modeling, which comprises the following steps: measuring and collecting input and output data of a shape memory alloy soft driver as sample data; designing an asymmetric activation function containing a hysteresis characteristic, constructing a neural network structure and selecting corresponding parameters, combining sample data and an improved gradient algorithm for iterative training, simultaneously introducing L1 regularization to generate a sparse model, and obtaining a soft driver theoretical model; inputting current soft driver corresponding input data into the soft driver theoretical model, outputting corresponding simulation output data, and controlling the soft driver according to the simulation output data. Compared with the prior art, the application adopts a neural network model, combines an improved nonlinear activation function and a gradient descent algorithm, improves the modeling accuracy and robustness of a hysteresis system, and effectively improves the subsequent control effect.

The embodiment of the invention provides a gene sequencing data analysis method and device, computing equipment and a readable storage medium, and belongs to the field of data processing. The gene sequencing data analysis method is applied to user equipment, and comprises the following steps: encoding gene sequencing data of a user to obtain a plurality of gene sequencing plaintexts corresponding to the gene sequencing data; encrypting each gene sequencing plaintext by using the public key to obtain a plurality of gene sequencing ciphertexts; sending the gene sequencing ciphertext to a server; receiving a gene sequencing data analysis result ciphertext sent by the server; and decrypting the gene sequencing data analysis result ciphertext by using the private key, and determining a gene sequencing data analysis result of the user. And the user equipment sends the plurality of encrypted gene sequencing ciphertexts to the server, so that the privacy leakage of the user is avoided. And the server performs data analysis by using the preset sparse model, and processes the to-be-detected gene sequencing ciphertext, so that the gene sequencing data analysis efficiency is improved.

The invention discloses a two-stage sparse method based on a small sample rapid sparse large-scale language model, and the method comprises the following steps: S1, mask initialization: based on a plurality of importance indexes for measuring parameters, employing a plurality of mask initialization methods to initialize a mask of a language model; s2, reordering the masks: reordering the masks based on the small sample calibration set and the straight-through estimator; s3, mask adjustment: based on the small sample calibration set and the reordered mask matrix in the step S2, taking an original model as a teacher model, taking a sparse model as a student model, realizing a block-level self-distillation process of the large language model in a time dimension before and after sparsification, and obtaining the sparse model; the method consumes less computing power, uses a smaller data set, occupies less GPU memory while realizing considerable performance, does not influence the reasoning speed of the sparse model, and has remarkable practical value and application prospect.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.

The embodiment of the invention discloses an acceleration method and device based on an irregular sparse model, electronic equipment and a medium. A specific embodiment of the method comprises the steps of inputting a sparse model into a target compiler; in the layer intermediate representation, splitting a sparse weight matrix of the sparse model into sparse sub-blocks to obtain a split sparse weight matrix; based on a sparse operator performance model, generating attribute information of each sparse sub-block in the split sparse weight matrix; in the block layer intermediate representation, according to the attribute information of each sparse sub-block, performing fusion processing on each sparse sub-block; in operator layer intermediate representation, according to attribute information corresponding to each sparse block, mapping each sparse block and a calculation unit to obtain a mapping result; and generating a processing task corresponding to the sparse model according to the mapping result, and executing the processing task. According to the embodiment, end-to-end optimization is realized through multi-layer optimization, and the income boundary of the traditional irregular sparse acceleration design is broken through.

The invention relates to the technical field of size measurement, in particular to a high-precision measurement method for the size of an ABS plate mold, and the method comprises the steps: scanning the surface of the ABS plate mold twice, respectively obtaining all point cloud data and all sparse point cloud data, and respectively constructing a three-dimensional mold model and a three-dimensional sparse model, performing spatial calibration on the three-dimensional mold model, and dividing the three-dimensional mold model into a plurality of component models; and calculating the space deviation value, the reflection deviation value, the adjustment coefficient and the adjusted filtering radius of each part model, filtering the point cloud data on all the part models by combining a filtering algorithm to obtain a reconstructed three-dimensional mold model, and measuring the size of the ABS plate mold. By dynamically adjusting the filtering radius of the filtering algorithm, the deviation influence caused by the reflectivity difference of different materials in the ABS plate mold is effectively reduced, and the precision and accuracy of size measurement of the plate mold are improved.

The invention provides a time-varying parameter model identification method based on beta wavelet basis function expansion. As shown in the flow in figure 1, the method comprises the following steps: firstly, establishing a nonlinear autoregression time-varying parameter model by using a non-stationary time-varying signal, and determining the order of the model by using an FPE order determination criterion; performing linear weighted expansion on the model time-varying parameters by utilizing a first-order beta wavelet basis function, and converting a model time-varying parameter identification problem into a time-invariant parameter estimation and model effective item selection problem, namely obtaining a beta wavelet expansion time-invariant parameter model; selecting effective items of the model by adopting a classical orthogonal forward regression algorithm, determining the number of the effective items of the model in combination with an APRESS cross validation standard, eliminating redundant items in the expansion model, estimating corresponding time-invariant parameters of the model, and establishing a sparse model structure; and finally, in combination with the obtained effective items and corresponding parameters, obtaining a sparse nonlinear autoregression model and time-varying parameters thereof through reverse solution. Compared with an existing time-varying parameter identification method based on main B-spline multi-wavelet basis function expansion, the time complexity of the identification process can be effectively reduced, the identification precision of a sparse model is improved, and a new thought and a new calculation framework are provided for identification analysis of nonlinear time-varying parameter signals.

The invention provides a model pruning method and device, electronic equipment and a program product, and the method comprises the steps: carrying out the iterative sparse processing of channels in each network layer in a pre-trained target model, and obtaining a target sparse model corresponding to the target model until the difference value between the model precision of the target model and the model precision of the sparse model corresponding to the target model in the current iteration is greater than or equal to a preset threshold value, and obtaining a sparse processing result of channels in each network layer in the target sparse model. And determining to-be-pruned channels in each network layer in the target model, and performing physical pruning on each to-be-pruned channel to remove the to-be-pruned channels in each network layer in the target model to obtain a pruned model of the target model. According to the model pruning method, the model precision of the target model after pruning can be effectively ensured on the premise of reducing the resource occupation amount of the target model after pruning.

The invention discloses a method for designing and manufacturing a flexible ultrasonic transducer of a sparse array, and relates to the technical field of transducer manufacturing, a sparse model taking array element number-imaging quality-cost as a target is constructed, and a genetic algorithm is utilized to carry out non-uniform array element arrangement optimization; positive and negative electrodes are manufactured by adopting a microelectronic printing technology, an S-shaped negative electrode loop is designed to reduce crosstalk, PDMS is spin-coated in a layered manner to realize light and thin packaging, and acoustic impedance matching and bendability are improved in combination with a flexible backing material; the method comprises the following steps: simulating a wearing scene, identifying an ultrasonic signal distortion rate, calculating a signal-to-noise ratio and a resolution ratio in real time based on pulse echo data, adjusting an array element driving phase through feedback, and carrying out dynamic calibration optimization. According to the flexible ultrasonic transducer, the imaging performance and reliability are remarkably improved while the manufacturing cost is reduced through sparse, multi-objective optimization and dynamic calibration technologies, and the applicability of the flexible ultrasonic transducer in the fields of wearable medical treatment, industrial detection and the like is expanded.

The invention discloses a sparse reasoning method, system, equipment and product for large language model reasoning, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring a weight matrix of a large language model, and then carrying out pruning sparsification processing on the weight matrix to obtain a sparse matrix; the storage format of the sparse matrix is converted into a bitmap coding storage format which is suitable for tensor calculation core perception and adopts a multi-level block structure to respectively correspond to different calculation granularities in a GPU / NPU hardware architecture so as to obtain a sparse model, and then the sparse model is deployed to respond to a reasoning request to perform reasoning service; sparse matrix multiplication is completed through a sparse matrix multiplication kernel based on on-chip storage bitmap coding so as to perform reasoning, so that through an innovative sparse matrix storage format and calculation optimization, the storage efficiency and the calculation performance in the reasoning process are remarkably improved, and especially in a low-sparseness scene, the reasoning efficiency is greatly improved. And the performance blank of the existing sparse reasoning framework in the field is filled.

The invention discloses a personalized federal learning method and system based on client clustering and sparse pruning, and the method comprises the steps: firstly constructing a central server-multi-client frame, initializing a full-size cluster model, a channel mask and an incremental pruning rate, and configuring SGD training, network slimming compression and encrypted transmission; performing dynamic re-clustering in each round of training, executing channel-level pruning by the client by using cluster structure information, performing local fine tuning, and uploading a sparse model; and the server carries out channel fusion on the model, and arithmetic averaging is carried out on the shared channel weight to generate a unified cluster model to be issued. After the target pruning rate is reached, the client continues personalized training and uploads, the server only executes FedAvg in the shared channel, the private channel keeps the original weight, and heterogeneous aggregation is achieved. A lightweight model with shared knowledge and private features can be obtained through iteration, the communication and storage overhead is remarkably reduced, and the method is suitable for a privacy-friendly intelligent reasoning scene of resource-constrained edge equipment.

The embodiment of the invention provides a model data compression and decompression method and device. In the compression method, after a compression device obtains a to-be-compressed target model, N key view angles can be determined in M shooting view angles, and projection and back projection are carried out on position information of point clouds in the target model under the N key view angles to obtain a first sparse model; and rendering the attribute information of each point cloud in the first sparse model to obtain a second sparse model, so that the scene characterization capability of the second sparse model tends to the scene characterization capability of the target model, and then projecting the position information and the attribute information of each point cloud in the second sparse model under N key perspectives to obtain a projection result. And obtaining a first projection image set, performing image compression on each projection image in the first projection image set to obtain a second projection image set, and taking the second projection image set as compression data corresponding to the target model.

The invention discloses a storage optimization method and device for a hybrid expert model, equipment, a medium and a product. The method comprises the following steps: loading a target hybrid expert model into a computing chip, and obtaining various sparse model weight matrixes in the target hybrid expert model; the sparse model weight matrix comprises a plurality of compression units with set sizes, each compression unit comprises at least one effective row and at least one sparse row, and each effective row comprises at least one structured sparse calculation storage unit; generating a compression matrix set corresponding to each model weight matrix; the compression matrix set comprises a compression data matrix, an index matrix and a metadata matrix; and storing each sparse model weight matrix in the target hybrid expert model as a matched compression matrix set so as to realize storage optimization of the target hybrid expert model. According to the technical scheme provided by the embodiment of the invention, the storage resource overhead of the hybrid expert model can be effectively optimized.

A method, system, and transitory computer-readable media for providing rapid deployment of Deep Neural Networks (DNNs) for Edge Computing using Structured Pruning at Initialization (SPaI). An input of a dense model and a pruning amount is received. Unstructured Pruning (UP) of the input model is performed by the pruning amount to generate a sparse model pruned by the pruning amount. The sensitivity of each layer of the sparse model to pruning is evaluated by contrasting a sparsity of the sparse model with an average sparsity of a global model to generate a structured pruning plan. The structured pruning plan is applied to the resilient layers. A remaining sensitive layers are reinitialized to produce an initialized model pruned by the pruning amount. The initialized model pruned by the pruning amount is provided as an output.

The application relates to a deep learning channel estimation method and device suitable for an underwater acoustic sensor network. The method utilizes OFDM pilot data to establish a sparse signal model, and obtains a real-value sparse model by performing real-value transformation on the sparse signal model. An approximate message passing (AMP) estimation framework is introduced to realize preliminary recovery of channel information. Further, a model-driven deep learning framework is proposed. An ST-LAMP network based on AMP without considering sparse prior and a GGM-LAMP network considering sparse prior are respectively established. Network training is respectively performed according to a predetermined strategy. Optimal matching parameters are learned through data learning and are updated and fixed. Correct channel estimation values can be output through input of new measurement values. The problem that traditional empirical parameter setting cannot be applied to high complexity of an underwater acoustic channel, leading to deviation of channel estimation from an actual channel, is solved. The precision of channel estimation is improved. The channel estimation method has the characteristics of low complexity and strong self-adaptability.

An adaptive distillation method for N:M sparse fine-tuning relates to the compression and acceleration of artificial neural networks. The method includes the following steps: 1) Pruning a pre-trained model using an N:M pruning scheme based on weight size. 2) Using adaptive knowledge distillation to transfer knowledge in the pre-trained model to the N:M sparse model, wherein the adaptive knowledge distillation can automatically replace the activation values ​​with large differences in the student feature map with the activation values ​​in the teacher feature map according to the difference in activation values ​​between the student model feature map and the teacher model feature map, so as to reduce the difficulty of student model learning and accelerate the convergence speed of student model fine-tuning. The proposed adaptive knowledge distillation method can be used to accelerate the N:M sparse fine-tuning of the pre-trained model and reduce the accuracy loss caused by N:M sparsity.

The invention discloses an image registration method based on grouped motion estimation and neighborhood refining sampling. The method comprises the following steps: constructing an image registration network comprising a shared feature extraction module, a sparse motion module and a dense motion module; a source image and a target image are obtained to train the image registration network, and shared feature extraction, grouping motion estimation and neighborhood refining sampling are sequentially carried out until a loss function converges to complete training; a to-be-registered source image and a target image thereof are processed through a network, and finally a registration motion stream is obtained to register the target image to the source image. According to the method, sparse modeling is carried out on input features by introducing grouping motion estimation, the local deformation expression ability is enhanced in combination with an adaptive fusion mechanism, meanwhile, a neighborhood refining sampling method is designed, a learnable local weighted correction mechanism is introduced in the motion flow sampling process, and the local deformation expression ability is improved. The boundary definition and detail precision of the dense motion field are effectively improved, and the precision and robustness of image registration are remarkably improved.

Examples including a system described herein implement a novel liquid time constant neural network (LTC-NN) based architecture to recover an underlying model of physical dynamics from real world data. The automatic differentiation property of LTC-NN nodes overcomes problems associated with low sampling rate, the input dependent time constant in the forward pass of the hidden layer of LTC-NN nodes creates a massive search space of implicit physical dynamics, the physics model solver based data reconstruction loss guides the search for the correct set of implicit dynamics, and drop out in dense layer ensures extraction of the sparsest model. Further, to account for perturbation timing error, the LTC-NN based architecture of the system utilizes dense layer nodes to search through input shifts that results in the lowest reconstruction loss.

The present invention provides a communication-efficient federated learning method based on compressed sensing, which can be summarized as follows: first, dictionary learning is performed using a quasi-validation dataset held by the server to learn a sparse representation of the model parameters; then, an adaptive compression ratio selection algorithm is used to determine the appropriate compression ratio based on the model training loss; finally, the linearity of compression is exploited on the server side to reduce the computational cost of global model recovery from n times to one execution of the reconstruction algorithm; in addition, the computational cost of compression is reduced through layered compression. With the present invention, non-sparse model parameters can be effectively compressed and accurately reconstructed, supporting not only uplink and downlink compression but also reducing overall communication costs without compromising test accuracy. Experiments on three image classification tasks demonstrate that our method consistently outperforms existing methods, achieving high reconstruction accuracy even when using a small quasi-validation dataset to learn the sparse dictionary.

The invention provides a short-wavelength residual static correction method based on a target reconstruction algorithm, and belongs to the technical field of seismic data processing. Comprising the following steps: modeling seismic data, expressing the seismic data as a form of a plurality of waves, conforming to a target sparse model, and assuming that the target sparse model contains a plurality of targets and is accompanied by noise; target decomposition is carried out on the seismic data containing the noise and reconstruction is carried out by adopting an iteration method, one target is reconstructed each time, and residual errors after decomposition are continuously decomposed until reconstruction of all targets is completed; the minimization problem of the target reconstruction process is converted into a regularization optimization problem with constraint conditions; and solving a quadratic optimization problem about the target to obtain a minimum value depending on a shift vector, and completing short-wavelength residual static correction by solving the maximum weight from the vertex 1 to the vertex N + 1 in the K approximate graph and reconstructing the shift vector. The invention provides the residual static correction method with robustness in combination with the properties of the seismic data under the condition that the calculation cost is not increased.

The invention provides a costume design system and method based on artificial intelligence, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional design data through a cloud collaborative architecture of costume design resources; performing dynamic hierarchical allocation on the multi-dimensional design data to obtain a hierarchical allocation result, and performing sparse processing on the multi-dimensional design data to obtain a sparse model of the costume design resources; performing strategy training based on deep reinforcement learning on the sparse model according to a hierarchical distribution result and historical collaborative data to obtain a sharing strategy and an optimized migration parameter; constructing a knowledge migration component of the cloud collaborative architecture based on the sharing strategy and the optimized migration parameters; knowledge migration optimization is carried out on the collaborative process of the costume design resources based on the knowledge migration component through the cloud collaborative architecture, the costume shared resources for intelligent generalization are generated, dynamic hierarchical distribution and knowledge migration optimization can be carried out on the costume design resources, and intelligent generalization sharing of the costume design resources is achieved.