357 results about "Sparse matrix" patented technology

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

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

Aspects of the disclosure are directed to hardware acceleration of structured sparse workloads with block quantization. A hardware accelerator can receive compressed input matrices, for example as part of a workload for training or processing a machine learning model. The hardware accelerator can multiply the compressed input matrix with a gains matrix loaded in one or more matrix multiply units (MXUs) of the hardware accelerator. The input matrices can be further provided in a block data type format, in which blocks of mantissas are represented with a single shared scaling factor. An MXU can multiply the block data, shift or cast the block data according to a shared scaling factor to generate an output product. To that end, block data type matrices exhibiting structured sparsity patterns can be accelerated without affecting the overall accuracy or quality of the output to the workload being processed.

The invention discloses a high-speed imaging method, system and device based on an SPAD array and a time counting circuit. The method comprises the following steps: acquiring original data of a photon event to obtain an original photon counting matrix of each frame; performing statistical modeling based on the original photon counting matrix to obtain a dynamic threshold matrix; performing threshold filtering based on the dynamic threshold matrix in combination with the original photon counting matrix to obtain a sparse matrix; and performing accumulation and image reconstruction based on the multi-frame original photon counting matrix to output a final image. The high time resolution of the SPAD array and the TCSPC is combined with the BNQSA algorithm, the noise problem of high frame rate imaging under extremely low illumination is solved, the system has the characteristics of high sensitivity, real-time performance and low power consumption, and a breakthrough solution is provided for a weak light scene.

A honeycomb-shaped active power distribution network topological structure selection and optimization method based on a graph theory comprises the following steps: taking line impedance and rated apparent power as constraints, constructing a multi-target weight factor, adopting an improved spectral clustering algorithm to cluster and distribute power grid nodes, and transiting a micro-grid group after cluster division into a honeycomb-shaped active power distribution network; abstracting each micro-grid as a honeycomb distribution network single node, constructing a connection relation mathematical representation of the HSPH and the micro-grid, and realizing sparse matrix method modeling of the honeycomb active power distribution network; optimizing a site selection strategy of the HSPH by taking a maximized HSPH return on investment index and a system stability index as a target function; and optimizing the energy storage capacity of the HSPH based on the net load time sequence data of the micro-grid group, and finally generating an optimal topological structure of the honeycomb-shaped active power distribution network. And through honeycomb topology and HSPH addressing and sizing optimization, the new energy consumption capability of the power distribution network is improved, and the economy, reliability and flexibility of the power distribution network are improved.

The invention relates to a risk control method for cross-border trade data. Cross-border trade behaviors are divided into three levels, namely a system type layer of national system / policy triggered transaction, a business type layer of a process chain and an execution type layer of data submission / customs clearance / payment behaviors. Modeling a behavior stability mapping relation among the three hierarchies through sparse matrix reconstruction and adversarial noise regression; introducing an interlayer fluctuation tension coefficient for each transaction to measure offset appearing in a behavior chain; when the tension peak value exceeds a training threshold value, even if single-point data is not abnormal, the event is marked as a potential risk transition event; according to the method, cross-border transaction behaviors are divided into a system type layer, a business type layer and an execution type layer, and an interlayer fluctuation tension coefficient is introduced, so that a cross-level collaborative imbalance phenomenon in a transaction chain is effectively captured. Even under the condition that no obvious field anomaly exists, the pseudo-legal transaction mode caused by behavior chain structure mutation can be recognized, and early warning of non-dominant structure anomaly is achieved.

The invention relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

The invention relates to the technical field of signal processing, and discloses a multichannel data acquisition system and method based on an FPGA (Field Programmable Gate Array), an acquisition module drives a global counter by using a global synchronous clock, writes acquired data into an annular buffer area with a physical address and a counter value in a linear mapping relationship, and establishes implicit time index storage. When a trigger event occurs, the system broadcasts the locked global trigger timestamp, and the acquisition module backtracks and reads historical data according to the global trigger timestamp, and packages the historical data into a sparse matrix type data packet in combination with the channel validity mask. And after receiving the data packet, the data processing module directly calculates a memory mapping address by using global time information in the data packet, and writes a data load into a corresponding position of the waveform reconstruction buffer area. According to the method, through strict binding of the physical address and the absolute time, independent time label redundancy is eliminated, high-precision synchronization of distributed multiple channels is ensured, and out-of-order automatic in-situ recombination and efficient waveform reconstruction of data of a receiving end are achieved.

The invention discloses an aircraft simulation method, device and equipment based on parallel block sparse matrix incomplete LU decomposition and a medium, and relates to the technical field of simulation, and the method comprises the steps: building a block sparse matrix comprising a plurality of zero sub-blocks and non-zero sub-blocks based on the topological relation of each grid point in an aircraft grid and simulation physical parameters, determining dependency pairs respectively corresponding to a plurality of non-zero sub-blocks from the non-zero sub-blocks, and storing the dependency pairs by adopting a preset data structure; wherein the dependent pair comprises a pair of matched non-zero sub-blocks; respectively distributing the plurality of non-zero sub-blocks to a plurality of GPU thread bundles for parallel processing so as to update the corresponding non-zero sub-blocks based on the updated dependency pairs after the GPU thread bundles complete updating on the dependency pairs corresponding to the corresponding non-zero sub-blocks; and after the plurality of non-zero sub-blocks are updated, updating the block sparse matrix based on the plurality of updated non-zero sub-blocks, and determining an incomplete LU decomposition result according to the updated block sparse matrix so as to perform aircraft simulation.

The invention discloses a dynamic compression coding method, which is applied to a sparse matrix, and comprises the following steps: S1, obtaining the sparse matrix, and generating a bitmap with the same dimension as the sparse matrix, a value in the bitmap being used for indicating whether an element at a corresponding position in the sparse matrix is zero; s2, a mark sequence is generated according to the bitmap, and each mark corresponds to multiple continuous elements in the sparse matrix and is used for representing whether each element in the multiple elements is zero or not; s3, extracting non-zero elements in the sparse matrix according to the mark sequence; and S4, combining the flag sequence with the non-zero elements to form a data stream after compression coding. The invention also discloses a dynamic compression coding system, a storage medium and a device. According to the dynamic compression coding method, the index storage cost can be reduced, the data access efficiency can be improved, the storage and bandwidth occupation can be reduced by supporting parallel coding and decoding, and finally the calculation throughput can be improved.

The invention provides a high-voltage circuit breaker voiceprint feature extraction method based on PSO-VMD and multi-scale permutation entropy, and belongs to the technical field of electrical digital data processing, and the method comprises the steps: firstly obtaining circuit breaker voiceprint signals under different working conditions, carrying out the size floating function processing, and carrying out the optimal parameter combination of VMD; variational mode decomposition is carried out on the voiceprint signal to obtain a plurality of intrinsic mode functions; extracting time domain features, frequency domain features and multi-scale permutation entropy features to construct sparse permutation features; then calculating a bilateral approximation balance contribution value of each modal function to construct a product operation of a feature weight matrix and an original feature matrix to form a disorder sparse matrix; and then hierarchical processing is carried out to convert into a disordered clustering matrix, and the disordered clustering matrix is input into a double-current CNN-SVM network to realize circuit breaker fault mode identification and classification. The technical problem that voiceprint feature extraction of the high-voltage circuit breaker cannot comprehensively represent complex fault acoustic characteristics is solved.

The invention discloses an acoustic temperature field reconstruction algorithm based on sparse matrix and refraction effect consideration, and belongs to the field of acoustic temperature measurement. The algorithm introduces a sparse reconstruction technology and refraction effect optimization to solve the problems of large reconstruction error of an underdetermined equation set, sensitivity to a measurement error and the like caused by the fact that the number of sound rays is smaller than a grid sectioning number in existing acoustic tomography. The method comprises the following specific steps: dividing a temperature measurement area into N grids, constructing a compressed sensing model through an observation matrix A and a sparse base psi, reconstructing a sparse signal by using an improved generalized orthogonal matching algorithm, namely IMOMP, reconstructing a temperature field through a relationship between sound slowness and temperature, and improving precision by using cubic spline interpolation. Meanwhile, the sound wave refraction effect in the non-uniform temperature field is considered, the sound ray path layout is optimized, the algorithm effectively deals with inverse problem instability, the reconstruction error is reduced, the anti-noise capability is improved, and the method is suitable for complex temperature field reconstruction and has application value for industrial temperature measurement.

The invention discloses an infrared bidirectional thermal effect simulation method based on radiation and GPU acceleration, and belongs to the field of thermal radiation simulation and parallel computing. Dispersing the complex geometry into patch units, constructing an inter-patch radiation energy balance equation based on a radiance theory, and iteratively solving the final temperature of the patches through a radiance method; a three-level parallel strategy of a task level, a data level and an instruction level is designed, data intensive tasks such as shape factor calculation and radiance iteration are migrated to a GPU to be executed, data storage is optimized in combination with a structure array (SoA) layout and a sparse matrix compression technology, and efficient data sharing of a CPU end and a GPU end is achieved through a CUDA zero copy technology. According to the method, the dependence of a traditional method on regular grids is broken through, the calculation efficiency and precision of million-level surface patch heat radiation transmission in a complex scene are remarkably improved, and an efficient tool is provided for heat radiation indirect transmission calculation and a global illumination model in the field of three-dimensional scene infrared simulation.

The invention relates to a calculation device, a calculation method, and a program. This computing device (10) computes a matrix product of a sparse matrix and a weight matrix in a neural network, the sparse matrix having a structure in which a predetermined number of non-zero elements are included in each block of a predetermined size. The sparse matrix is defined by a local index representing the position of the non-zero element in each block and the value of the non-zero element, and the arithmetic device (10) performs: a process for acquiring, from the weight matrix, an element corresponding to the position of the non-zero element in the sparse matrix by referring to the local index; and multiplication and accumulation operation of non-zero elements of the sparse matrix and corresponding elements of the weight matrix is carried out.

The invention discloses an entropy coding compression method for high-dimensional sparse data, which relates to the technical field of data compression, and comprises the following steps: reading an original high-dimensional sparse matrix, extracting a position index set and a corresponding non-zero value set of all non-zero elements, extracting active samples from the non-zero value set, and compressing the active samples. After an active sample matrix and an optimal mean value centralization matrix are constructed, principal component projection and a self-expression structure are introduced for joint modeling, a low-rank robust optimization objective function is formed, and a principal component feature matrix is finally output by alternately optimizing mean values, projection, weights and residual errors. According to the method, the compression efficiency and the processing pertinence of high-dimensional sparse data are effectively improved, self-expression structure modeling and residual regular optimization between samples are further combined, the structure consistency is kept in the dimension reduction process, a key information structure is kept while the compression ratio is guaranteed, and the high-fidelity and low-redundancy entropy coding compression effect is achieved.

The invention discloses a simulation method and system for improving the simulation efficiency of a PIN diode, and belongs to the technical field of semiconductor device simulation, and the method comprises the steps: firstly, employing a hash table data structure to optimize the preprocessing process of a semiconductor physical model time domain numerical method, and remarkably improving the parameter preprocessing efficiency; carrying out bandwidth compression optimization on the sparse matrix in combination with an inverse Cuthill-Mckee matrix reordering algorithm; and secondly, constructing a parallel iteration solver architecture to realize efficient distributed solution of a large nonlinear matrix equation. Through cooperative application of the method, the overall calculation efficiency of a PIN diode numerical simulation program is improved by more than three times compared with that of traditional commercial simulation software COMSOL on the premise that the calculation precision is kept, the industrial problem that the simulation timeliness of a semiconductor device is limited is effectively solved, and an innovative technical path is provided for high-precision real-time simulation. The method has important application value in the fields of power device design, integrated circuit simulation and the like.

The invention provides a block storage method and device for a sparse matrix, and is applied to the field of data storage.The method comprises the steps that block storage is conducted on the sparse matrix through a target sparse storage format, the target sparse storage format comprises a first-layer storage structure, a second-layer storage structure and a third-layer storage structure, comprising the following steps: in a first-layer storage structure, dividing a sparse matrix into blocks with the row and column number of 16 * 16; in the second-layer storage structure, the blocks are divided into sub-blocks with the row and column number being 4 * 4, the positions of non-zero sub-blocks in the sub-blocks are marked based on a 16-bit first-level bitmap, and the non-zero sub-blocks are stored; in the third-layer storage structure, marking the positions of the non-zero elements in the non-zero sub-blocks based on a 16-bit secondary bitmap, and positioning the non-zero element values of the non-zero elements through a secondary value pointer; according to the invention, the storage compatibility among different sparse cores can be realized.

The invention relates to a sparse matrix processing method based on a partitioning strategy. The method comprises the steps that a sparse matrix file is read, a first matrix stored in a CSC format and a second matrix stored in a CSR format are obtained based on the sparse matrix file, and the rows of the first matrix are equal to the columns of the second matrix; performing block processing on the first matrix according to a preset block size to obtain each first block, and performing block processing on the second matrix according to the preset block size to obtain each second block; performing symbol multiplication on the outer layer indexes of the first blocks of the first matrix and the second blocks of the second matrix to obtain a result matrix, wherein the result matrix is used for representing the total number of non-zero elements; and performing numerical multiplication on the inner layer indexes of the first blocks of the first matrix and the second blocks of the second matrix to obtain values of non-zero elements in a result matrix. By adopting the method, the overall performance can be improved.

Approaches in accordance with various embodiments provide for the processing of sparse matrices for mathematical and programmatic operations. In particular, various embodiments utilize a tiling approach that divides a sparse matrix into submatrices, many of which will include only zero-value entities. These empty tiles can be ignored, and only the tiles with non-zero entries processed, which reduces resource and time requirements for the processing. An indexing approach can be used for each entity that is a combination of the tile identifier and an offset value, which enables the values to be multiplied correctly against, for example, values of a dense matrix. The tiles can be processed in parallel and the results accumulated to generate a matrix product. The matrix product can then be passed to the next step in a process or operation, such as to a next layer in a deep neural network.

The invention relates to the technical field of edge-end collaboration, and discloses an edge-end collaboration AI model construction system and method for energy consumption prediction, and the method comprises the steps: collecting node parameters of edge nodes according to a fixed time interval in a preset time period, and obtaining a parameter matrix; calculating a sample covariance based on the parameter matrix, and executing strip projection to obtain a projection matrix; respectively constructing a variance matrix and a correlation matrix by using the projection matrix; constructing a power grid sparse matrix according to the sensor topology; splicing the power grid sparse matrix and the correlation matrix to form a normalized correlation matrix, and generating a multi-layer covariance matrix in combination with the variance matrix; performing positive definite processing on the multi-layer covariance matrix to obtain a positive definite covariance matrix; and whitening the parameter matrix by using the positive definite covariance matrix to obtain a whitened parameter matrix.

The invention discloses a seismic simulation method and system fusing an implicit iterative graph network and a spectral element method. The method comprises the following steps: acquiring acceleration data of a small time step node; performing grid sparse division and interpolation mapping processing, and projecting to a low-dimensional manifold to obtain large-time-step node acceleration data and a low-order unit calculation force matrix of the current frame; obtaining a physical relation sparse matrix I, a physical relation sparse matrix II and sparse matrix topological relation information of the physical relation sparse matrix I and the physical relation sparse matrix II through a processing method of Jacobian iteration and projection to a low-dimensional manifold; obtaining a seismic simulated diagram neural network model; and predicting by using the seismic simulation diagram neural network model, and predicting the acceleration by using the large time step of the sparse unit nodes projected back to the target area by using the projection matrix. According to the method provided by the invention, the mapping between the high-order unit small time step and the low-order unit large time step is established through the fitting capability of the neural network, so that the calculation time is greatly shortened, and the earthquake simulation is quickly carried out.

The invention discloses a real-time online superconducting accelerator low-temperature system construction method and system.The method comprises the steps that physical property calculation in a low-temperature system is simplified, and a simplified physical property function of a working medium in the low-temperature system is obtained; the key point temperature is simulated according to the combination of a one-dimensional pipeline model and a 0-dimensional tank sub-model based on an agent model technology, the one-dimensional pipeline model is used for simulating the delay of heat transfer, and the 0-dimensional tank sub-model is used for simulating heat capacity; and responding to the simplified physical property function and the key point temperature, and solving node parameters in the low-temperature system by adopting a large-scale nonlinear sparse matrix equation. According to the invention, the fast simulator which operates independently can be provided, and meanwhile, the fast simulator can be combined with a real low-temperature system to operate to provide analysis and prediction functions for the real low-temperature system.

The invention belongs to the technical field of defect detection, and particularly relates to an artificial quartz stone plate surface defect detection system and method to solve the technical problem that normal textures are easily misjudged as defects in an existing detection method. The detection system comprises an acquisition module which combines color components and anisotropy degrees into multi-dimensional features and arranges the multi-dimensional features of all pixels into an observation matrix; the construction module is used for decomposing the observation matrix into linear superposition of a global background low-rank matrix, a local texture low-rank matrix and a defect sparse matrix; and the extraction module is used for calculating the area of each candidate defect connected domain, the length-width ratio of a bounding rectangle, the average chromatic aberration and the average anisotropy degree. According to the invention, the screening standard can be obtained according to the texture characteristics of the candidate region, pseudo defects caused by noise or background residue can be reduced, and high accuracy and high reliability of the detection result are ensured.

The invention discloses an MAC unit supporting input activation and weight double-end sparsity, and relates to the technical field of neural network accelerator hardware design. Aiming at the technical defects existing in a sparse matrix acceleration scheme, the scheme is adopted, an activation value and non-zero elements of a weight matrix are stored through a special compressed data register, corresponding zero value distribution is recorded through a matched sparse bitmap register, and a joint sparse bitmap marking a double-non-zero effective calculation position is generated through dual-port joint operation; and the control and address generation subunit scans the joint sparse bitmap and outputs an address and an enable signal, the MAC calculation subunit is driven to read non-zero data to complete multiply-accumulate operation, and finally, an operation result is compressed and coded through an output end sparse encoder, so that efficient transmission of interlayer sparse data streams is realized. According to the method, input activation and weight double-end sparsity can be utilized at the same time, end-to-end sparse data flow is supported, zero value calculation can be dynamically skipped, and the energy efficiency ratio and the calculation throughput of neural network reasoning are improved.

The invention relates to an in-hole transient electromagnetic response characteristic numerical simulation method based on a tunnel three-dimensional geoelectric model and an absorption boundary finite element method, and belongs to the technical field of geophysical exploration. The method comprises the following steps: constructing a tunnel full-space three-dimensional conductivity model; based on a Maxwell equation set, a vector potential and a scalar potential are introduced, a vector control equation of a transient electromagnetic field is derived in combination with Coulomb specifications, first-order absorption boundary conditions are established, and a weak form finite element equation is derived through a Galerkin weighted margin method; generating a global sparse matrix by utilizing spatial discretization and unstructured grid division, and performing iterative solution on the dynamic electromagnetic field by adopting a Newmark time discretization method; and simulating the transient electromagnetic response of geological anomalous bodies with different resistivity and the distance between the geological anomalous bodies and the receiving coil, and analyzing the attenuation characteristic and the geometric gravity center effect to obtain an electromagnetic response rule. The device can accurately simulate the electromagnetic response of abnormal bodies such as aquifers and faults in front of a tunnel face and around drill holes.

The invention discloses a bridge finite element real-time analysis method and system, and the method comprises the steps: constructing a finite element model of a structure, and dividing the finite element model into a plurality of finite element units through discretization processing; calculating an overall stiffness matrix K according to the established finite element model; in combination with the boundary condition of the structure, recalculating to obtain a processed total stiffness matrix K ', and carrying out serialization operation on the processed total stiffness matrix K '; selecting a position for storing the sparse matrix according to a specific computing environment; according to the load information acquired by the sensor, converting the load information into a corresponding load vector in the finite element model; and combining the cached total stiffness matrix K'with a load vector, solving node displacement and structural response, and transmitting a result to a post-processing module. According to the method, the required data are efficiently accessed according to different calculation requirements on the basis of a sparse matrix cache strategy, when the load changes, only the load vector needs to be recalculated and solved, the overall stiffness matrix does not need to be recalculated, and the calculation efficiency is improved.

The noise performance of the image sensor is improved by using hundreds of pixel-to-pixel comparisons that all contribute to information about a given pixel. This allows accurate determination of image chromaticity even at extremely low signal-to-noise ratios. In other embodiments, filters of a non-conventional spectral transmittance function are employed to reduce metamerism, enabling resolution of scene information that is not visible to the human eye. Yet other embodiments relate to manufacturing a sparse matrix of transparent pedestals on an array of image photosensors. When a color resist is applied thereafter, these pedestals produce a change in thickness of the resulting resist layer, resulting in the pixels having a different spectral response despite the use of the same color resist. Yet other embodiments relate to image sensor arrangements that enable generation of red, green, blue, and NIR output data using only four conventional (red, green, blue, cyan, magenta, yellow) color resists. Numerous other novel features and arrangements are also detailed.

A sparse dot product and / or matrix multiply is computed by subdividing each vector and simultaneously performing operations to generate output matrix elements. In an embodiment, a bit mask is computed that includes one bit for each element of an input matrix, each bit indicating whether the element is non-zero or zero. In an embodiment, the element values are stored in a packed format, where all of the non-zero values are packed together and the remaining storage for the matrix contains zeros (or any other values). The bit mask can then be used to determine the location of each non-zero element in the packed storage. Rather than reading all of the elements, only the non-zero elements that will be multiplied by a non-zero element from the other input vector should be read. Any multiplication by a zero element from either input vector or matrix is unnecessary.

The invention provides a data hierarchy scheduling method and system, a storage medium and equipment, and relates to the field of data processing, and the method comprises the steps: obtaining a sparse matrix in a row compression storage format; performing matrix segmentation on the sparse matrix to obtain segmented small matrixes; calling a vector access unit to package and access paired indexes in the sparse matrix, storing row data to be updated into an on-chip local memory, and storing a non-zero element column index of the sparse matrix corresponding to each row of the L matrix and a corresponding numerical value into a storage space of each thread in the on-chip local memory; global threads are grouped to obtain a plurality of thread blocks, and thread indexes of the thread blocks are stored in an atomic cache; and in the atomic cache, the segmented small matrixes are correspondingly processed by using the thread blocks. The parallel computing capability of the multi-core processor can be fully utilized, the memory delay is reduced, and the data access efficiency is improved.

The invention provides an edge side industrial data lightweight preprocessing caching method, and belongs to the technical field of industrial data. According to the method, the data acquisition quality is improved through Gray code anti-interference coding, dynamic sampling is realized through adaptive sampling rate adjustment and weak abnormal feature weight vectors, a fault position is accurately positioned through wavelet decomposition and a high-frequency fault feature sparse matrix, a semantic perception compression encoder is matched with a dispersion evaluation matrix to carry out differential coding, and the accuracy and the reliability of the algorithm are improved. According to the overall systematic technical scheme, the transmission path is optimized through a network transmission graph model, and all links from signal acquisition, self-adaptive processing, intelligent compression, dynamic cache management to network optimization are optimized. The technical problems that the cache occupancy rate is too high and the data transmission delay is large due to the fact that the multi-source signals of the industrial equipment are difficult to pre-process efficiently on the edge side are solved.